diff --git a/data/motifs/createFpr.py b/data/motifs/createFpr.py index c0a05d2f4..32b548a7e 100644 --- a/data/motifs/createFpr.py +++ b/data/motifs/createFpr.py @@ -1,8 +1,8 @@ - # Import import sys from glob import glob from os.path import basename + from MOODS import tools, parsers # Input @@ -14,10 +14,10 @@ pseudocounts = 1.0 outFile = open(outFileName, "w") -outFile.write("\t".join(["MOTIF"]+[str(e) for e in fprList])+"\n") +outFile.write("\t".join(["MOTIF"] + [str(e) for e in fprList]) + "\n") # Iterating on all PWMs -for pwmFileName in sorted(glob(inFolder+"*.pwm")): +for pwmFileName in sorted(glob(inFolder + "*.pwm")): # Creating PSSM name = ".".join(basename(pwmFileName).split(".")[:-1]) @@ -33,6 +33,6 @@ resVec.append(str(tools.threshold_from_p(pssm, bg, fpr))) # Writing results - outFile.write("\t".join(resVec)+"\n") + outFile.write("\t".join(resVec) + "\n") outFile.close() diff --git a/data/motifs/createMtf.py b/data/motifs/createMtf.py index b5b969de6..0baa2e77f 100644 --- a/data/motifs/createMtf.py +++ b/data/motifs/createMtf.py @@ -1,4 +1,3 @@ - ################################################################################################### ##### Annotation File Standard (tab-separated): # MATRIX_ID: The matrices' ID. It may change format for different repositories. (STRING) @@ -6,18 +5,19 @@ # SOURCE: The source repository of such matrix. (STRING) # VERSION: The version of such matrix (1 for 'primary motif', 2 for 'secondary motif', etc). (INT) # GENE_NAMES: Name of genes associated with such TF matrix. (LIST) -# GROUP: . (STRING) +# GROUP: Name of factor "family" or "class" or "cluster", depending on repository. (STRING) +# UniProt: UniProt accession for the transcription factor. (STRING) ################################################################################################### # * Mandatory fields: MATRIX_ID, SOURCE, VERSION, GENE_NAMES. # * Fields with multiple entries should be separated by ';' (no spaces). # * Fields with missing/non-existing/doesn't matter data should be filled with '.' # * Co-binding should be represented by '+' (no spaces). +# * Group can be any string, and it may also contain whitespaces and punctuation (no tab). ################################################################################################### # Import -import os -import sys import glob +import csv # Parameters dataLocation = "./" @@ -28,27 +28,35 @@ ################################################################################################### # Fetching file names -# TODO: check if this still works for hocomoco v10 source = "hocomoco" -inputLocation = dataLocation+source+"/" +inputLocation = dataLocation + source + "/" resultMatrix = [] -for inputFileName in glob.glob(inputLocation+"*.pwm"): +hocomoco_anno = {} +with open("hocomoco_anno.csv", "rb") as f: + csvf = csv.reader(f) + for l in csvf: + hocomoco_anno[l[0]] = l[1:] +for inputFileName in glob.glob(inputLocation + "*.pwm"): ll = inputFileName.split("/")[-1].split(".")[0].split("_") - matrix_id = ll[1] + matrix_id = ll[0] pwm_name = ".".join(inputFileName.split("/")[-1].split(".")[:-1]) version = "1" - gene_names = ll[0] - resultMatrix.append([matrix_id,pwm_name,source,version,gene_names,group]) + gene_names = hocomoco_anno[pwm_name][0] + group = hocomoco_anno[pwm_name][1] + if not group: + group = "." + uniprot = hocomoco_anno[pwm_name][2] + data_source = hocomoco_anno[pwm_name][3] + resultMatrix.append([matrix_id, pwm_name, source, version, gene_names, group, uniprot, data_source]) # Sorting results by ID -resultMatrix = sorted(resultMatrix ,key=lambda x: x[2]) -resultMatrix = sorted(resultMatrix ,key=lambda x: x[0]) +resultMatrix = sorted(resultMatrix, key=lambda x: x[0]) # Writing to output file -outputFileName = dataLocation+source+".mtf" -outputFile = open(outputFileName,"w") +outputFileName = dataLocation + source + ".mtf" +outputFile = open(outputFileName, "w") for resultVec in resultMatrix: - outputFile.write("\t".join(resultVec)+"\n") + outputFile.write("\t".join(resultVec) + "\n") outputFile.close() ################################################################################################### @@ -57,26 +65,38 @@ # Fetching file names source = "jaspar_vertebrates" -inputLocation = dataLocation+source+"/" +inputLocation = dataLocation + source + "/" resultMatrix = [] -for inputFileName in glob.glob(inputLocation+"*.pwm"): +jaspar_anno = {} +with open("jaspar_anno.csv", "rb") as f: + csvf = csv.reader(f) + for l in csvf: + if not l: + continue + jaspar_anno[l[0]] = l[1:] +for inputFileName in glob.glob(inputLocation + "*.pwm"): ll = inputFileName.split("/")[-1].split(".") matrix_id = ll[0] pwm_name = ".".join(inputFileName.split("/")[-1].split(".")[:-1]) version = "1" - if(len(ll) > 4): version = ll[4] - gene_names = ll[2].replace("::","+") - resultMatrix.append([matrix_id,pwm_name,source,version,gene_names,group]) + if len(ll) > 4: + version = ll[4] + gene_names = jaspar_anno[pwm_name][0] + group = jaspar_anno[pwm_name][1] + if not group: + group = "." + uniprot = jaspar_anno[pwm_name][2] + data_source = jaspar_anno[pwm_name][3] + resultMatrix.append([matrix_id, pwm_name, source, version, gene_names, group, uniprot, data_source]) # Sorting results by ID -resultMatrix = sorted(resultMatrix ,key=lambda x: x[2]) -resultMatrix = sorted(resultMatrix ,key=lambda x: x[0]) +resultMatrix = sorted(resultMatrix, key=lambda x: x[0]) # Writing to output file -outputFileName = dataLocation+source+".mtf" -outputFile = open(outputFileName,"w") +outputFileName = dataLocation + source + ".mtf" +outputFile = open(outputFileName, "w") for resultVec in resultMatrix: - outputFile.write("\t".join(resultVec)+"\n") + outputFile.write("\t".join(resultVec) + "\n") outputFile.close() ################################################################################################### @@ -85,25 +105,24 @@ # Fetching file names source = "transfac_public" -inputLocation = dataLocation+source+"/" +inputLocation = dataLocation + source + "/" resultMatrix = [] -for inputFileName in glob.glob(inputLocation+"*.pwm"): +for inputFileName in glob.glob(inputLocation + "*.pwm"): ll = inputFileName.split("/")[-1].split(".")[0].split("_") matrix_id = ll[0] pwm_name = ".".join(inputFileName.split("/")[-1].split(".")[:-1]) version = "1" gene_names = ll[1] - resultMatrix.append([matrix_id,pwm_name,source,version,gene_names,group]) + resultMatrix.append([matrix_id, pwm_name, source, version, gene_names, ".", ".", ".", "."]) # Sorting results by ID -resultMatrix = sorted(resultMatrix ,key=lambda x: x[2]) -resultMatrix = sorted(resultMatrix ,key=lambda x: x[0]) +resultMatrix = sorted(resultMatrix, key=lambda x: x[0]) # Writing to output file -outputFileName = dataLocation+source+".mtf" -outputFile = open(outputFileName,"w") +outputFileName = dataLocation + source + ".mtf" +outputFile = open(outputFileName, "w") for resultVec in resultMatrix: - outputFile.write("\t".join(resultVec)+"\n") + outputFile.write("\t".join(resultVec) + "\n") outputFile.close() ################################################################################################### @@ -112,25 +131,24 @@ # Fetching file names source = "uniprobe_primary" -inputLocation = dataLocation+source+"/" +inputLocation = dataLocation + source + "/" resultMatrix = [] -for inputFileName in glob.glob(inputLocation+"*.pwm"): +for inputFileName in glob.glob(inputLocation + "*.pwm"): ll = inputFileName.split("/")[-1].split(".")[0].split("_") matrix_id = ll[0] pwm_name = ".".join(inputFileName.split("/")[-1].split(".")[:-1]) version = ll[1] gene_names = ll[2] - resultMatrix.append([matrix_id,pwm_name,source,version,gene_names,group]) + resultMatrix.append([matrix_id, pwm_name, source, version, gene_names, ".", ".", ".", "."]) # Sorting results by ID -resultMatrix = sorted(resultMatrix ,key=lambda x: x[2]) -resultMatrix = sorted(resultMatrix ,key=lambda x: x[0]) +resultMatrix = sorted(resultMatrix, key=lambda x: x[0]) # Writing to output file -outputFileName = dataLocation+source+".mtf" -outputFile = open(outputFileName,"w") +outputFileName = dataLocation + source + ".mtf" +outputFile = open(outputFileName, "w") for resultVec in resultMatrix: - outputFile.write("\t".join(resultVec)+"\n") + outputFile.write("\t".join(resultVec) + "\n") outputFile.close() ################################################################################################### @@ -139,25 +157,22 @@ # Fetching file names source = "uniprobe_secondary" -inputLocation = dataLocation+source+"/" +inputLocation = dataLocation + source + "/" resultMatrix = [] -for inputFileName in glob.glob(inputLocation+"*.pwm"): +for inputFileName in glob.glob(inputLocation + "*.pwm"): ll = inputFileName.split("/")[-1].split(".")[0].split("_") matrix_id = ll[0] pwm_name = ".".join(inputFileName.split("/")[-1].split(".")[:-1]) version = ll[1] gene_names = ll[2] - resultMatrix.append([matrix_id,pwm_name,source,version,gene_names,group]) + resultMatrix.append([matrix_id, pwm_name, source, version, gene_names, ".", ".", ".", "."]) # Sorting results by ID and version -resultMatrix = sorted(resultMatrix ,key=lambda x: x[2]) -resultMatrix = sorted(resultMatrix ,key=lambda x: x[0]) +resultMatrix = sorted(resultMatrix, key=lambda x: x[0]) # Writing to output file -outputFileName = dataLocation+source+".mtf" -outputFile = open(outputFileName,"w") +outputFileName = dataLocation + source + ".mtf" +outputFile = open(outputFileName, "w") for resultVec in resultMatrix: - outputFile.write("\t".join(resultVec)+"\n") + outputFile.write("\t".join(resultVec) + "\n") outputFile.close() - - diff --git a/data/motifs/hocomoco.mtf b/data/motifs/hocomoco.mtf index 099d27ce4..341718bed 100644 --- a/data/motifs/hocomoco.mtf +++ b/data/motifs/hocomoco.mtf @@ -1,771 +1,771 @@ -HUMAN ZN331_HUMAN.H11MO.0.C hocomoco 1 ZN331 . -HUMAN ZIC4_HUMAN.H11MO.0.D hocomoco 1 ZIC4 . -HUMAN RXRB_HUMAN.H11MO.0.C hocomoco 1 RXRB . -HUMAN RORA_HUMAN.H11MO.0.C hocomoco 1 RORA . -HUMAN ZN436_HUMAN.H11MO.0.C hocomoco 1 ZN436 . -HUMAN EVX1_HUMAN.H11MO.0.D hocomoco 1 EVX1 . -HUMAN PAX7_HUMAN.H11MO.0.D hocomoco 1 PAX7 . -HUMAN HTF4_HUMAN.H11MO.0.A hocomoco 1 HTF4 . -HUMAN GFI1B_HUMAN.H11MO.0.A hocomoco 1 GFI1B . -HUMAN PO4F2_HUMAN.H11MO.0.D hocomoco 1 PO4F2 . -HUMAN CEBPA_HUMAN.H11MO.0.A hocomoco 1 CEBPA . -HUMAN SOX15_HUMAN.H11MO.0.D hocomoco 1 SOX15 . -HUMAN ANDR_HUMAN.H11MO.0.A hocomoco 1 ANDR . -HUMAN TF2LX_HUMAN.H11MO.0.D hocomoco 1 TF2LX . -HUMAN NFKB2_HUMAN.H11MO.0.B hocomoco 1 NFKB2 . -HUMAN NR1I3_HUMAN.H11MO.1.D hocomoco 1 NR1I3 . -HUMAN RXRA_HUMAN.H11MO.0.A hocomoco 1 RXRA . -HUMAN MYB_HUMAN.H11MO.0.A hocomoco 1 MYB . -HUMAN KLF14_HUMAN.H11MO.0.D hocomoco 1 KLF14 . -HUMAN DUXA_HUMAN.H11MO.0.D hocomoco 1 DUXA . -HUMAN HESX1_HUMAN.H11MO.0.D hocomoco 1 HESX1 . -HUMAN JUND_HUMAN.H11MO.0.A hocomoco 1 JUND . -HUMAN HSF4_HUMAN.H11MO.0.D hocomoco 1 HSF4 . -HUMAN ARNT2_HUMAN.H11MO.0.D hocomoco 1 ARNT2 . -HUMAN ISL2_HUMAN.H11MO.0.D hocomoco 1 ISL2 . -HUMAN ZN449_HUMAN.H11MO.0.C hocomoco 1 ZN449 . -HUMAN P53_HUMAN.H11MO.1.A hocomoco 1 P53 . -HUMAN ZN134_HUMAN.H11MO.0.C hocomoco 1 ZN134 . -HUMAN PBX1_HUMAN.H11MO.1.C hocomoco 1 PBX1 . -HUMAN GLIS2_HUMAN.H11MO.0.D hocomoco 1 GLIS2 . -HUMAN STAT3_HUMAN.H11MO.0.A hocomoco 1 STAT3 . -HUMAN HME1_HUMAN.H11MO.0.D hocomoco 1 HME1 . -HUMAN OVOL1_HUMAN.H11MO.0.C hocomoco 1 OVOL1 . -HUMAN NANOG_HUMAN.H11MO.0.A hocomoco 1 NANOG . -HUMAN ERR3_HUMAN.H11MO.0.B hocomoco 1 ERR3 . -HUMAN ANDR_HUMAN.H11MO.1.A hocomoco 1 ANDR . -HUMAN MYOD1_HUMAN.H11MO.1.A hocomoco 1 MYOD1 . -HUMAN ALX3_HUMAN.H11MO.0.D hocomoco 1 ALX3 . -HUMAN TCF7_HUMAN.H11MO.0.A hocomoco 1 TCF7 . -HUMAN FOXJ3_HUMAN.H11MO.1.B hocomoco 1 FOXJ3 . -HUMAN MECP2_HUMAN.H11MO.0.C hocomoco 1 MECP2 . -HUMAN MTF1_HUMAN.H11MO.0.C hocomoco 1 MTF1 . -HUMAN PATZ1_HUMAN.H11MO.0.C hocomoco 1 PATZ1 . -HUMAN E2F7_HUMAN.H11MO.0.B hocomoco 1 E2F7 . -HUMAN KLF6_HUMAN.H11MO.0.A hocomoco 1 KLF6 . -HUMAN PDX1_HUMAN.H11MO.0.A hocomoco 1 PDX1 . -HUMAN CEBPD_HUMAN.H11MO.0.C hocomoco 1 CEBPD . -HUMAN PAX4_HUMAN.H11MO.0.D hocomoco 1 PAX4 . -HUMAN ZN148_HUMAN.H11MO.0.D hocomoco 1 ZN148 . -HUMAN ZN232_HUMAN.H11MO.0.D hocomoco 1 ZN232 . -HUMAN ZN214_HUMAN.H11MO.0.C hocomoco 1 ZN214 . -HUMAN USF2_HUMAN.H11MO.0.A hocomoco 1 USF2 . -HUMAN ZN680_HUMAN.H11MO.0.C hocomoco 1 ZN680 . -HUMAN PAX1_HUMAN.H11MO.0.D hocomoco 1 PAX1 . -HUMAN ONEC3_HUMAN.H11MO.0.D hocomoco 1 ONEC3 . -HUMAN LBX2_HUMAN.H11MO.0.D hocomoco 1 LBX2 . -HUMAN TEF_HUMAN.H11MO.0.D hocomoco 1 TEF . -HUMAN CEBPB_HUMAN.H11MO.0.A hocomoco 1 CEBPB . -HUMAN PBX2_HUMAN.H11MO.0.C hocomoco 1 PBX2 . -HUMAN MLXPL_HUMAN.H11MO.0.D hocomoco 1 MLXPL . -HUMAN MGAP_HUMAN.H11MO.0.D hocomoco 1 MGAP . -HUMAN MYBA_HUMAN.H11MO.0.D hocomoco 1 MYBA . -HUMAN OLIG2_HUMAN.H11MO.1.B hocomoco 1 OLIG2 . -HUMAN SOX2_HUMAN.H11MO.1.A hocomoco 1 SOX2 . -HUMAN DLX1_HUMAN.H11MO.0.D hocomoco 1 DLX1 . -HUMAN GATA6_HUMAN.H11MO.0.A hocomoco 1 GATA6 . -HUMAN PRGR_HUMAN.H11MO.0.A hocomoco 1 PRGR . -HUMAN NFAC1_HUMAN.H11MO.1.B hocomoco 1 NFAC1 . -HUMAN FIGLA_HUMAN.H11MO.0.D hocomoco 1 FIGLA . -HUMAN PRRX2_HUMAN.H11MO.0.C hocomoco 1 PRRX2 . -HUMAN PRDM1_HUMAN.H11MO.0.A hocomoco 1 PRDM1 . -HUMAN RFX4_HUMAN.H11MO.0.D hocomoco 1 RFX4 . -HUMAN KLF9_HUMAN.H11MO.0.C hocomoco 1 KLF9 . -HUMAN PTF1A_HUMAN.H11MO.0.B hocomoco 1 PTF1A . -HUMAN ZSC31_HUMAN.H11MO.0.C hocomoco 1 ZSC31 . -HUMAN ZN263_HUMAN.H11MO.0.A hocomoco 1 ZN263 . -HUMAN THB_HUMAN.H11MO.0.C hocomoco 1 THB . -HUMAN FOXA2_HUMAN.H11MO.0.A hocomoco 1 FOXA2 . -HUMAN NDF1_HUMAN.H11MO.0.A hocomoco 1 NDF1 . -HUMAN RFX1_HUMAN.H11MO.0.B hocomoco 1 RFX1 . -HUMAN ZN554_HUMAN.H11MO.0.C hocomoco 1 ZN554 . -HUMAN VENTX_HUMAN.H11MO.0.D hocomoco 1 VENTX . -HUMAN AIRE_HUMAN.H11MO.0.C hocomoco 1 AIRE . -HUMAN FOXG1_HUMAN.H11MO.0.D hocomoco 1 FOXG1 . -HUMAN GATA3_HUMAN.H11MO.0.A hocomoco 1 GATA3 . -HUMAN NR2F6_HUMAN.H11MO.0.D hocomoco 1 NR2F6 . -HUMAN COE1_HUMAN.H11MO.0.A hocomoco 1 COE1 . -HUMAN PIT1_HUMAN.H11MO.0.C hocomoco 1 PIT1 . -HUMAN TWST1_HUMAN.H11MO.1.A hocomoco 1 TWST1 . -HUMAN ERR1_HUMAN.H11MO.0.A hocomoco 1 ERR1 . -HUMAN ZN134_HUMAN.H11MO.1.C hocomoco 1 ZN134 . -HUMAN ZN264_HUMAN.H11MO.0.C hocomoco 1 ZN264 . -HUMAN THA_HUMAN.H11MO.1.D hocomoco 1 THA . -HUMAN ATOH1_HUMAN.H11MO.0.B hocomoco 1 ATOH1 . -HUMAN STA5A_HUMAN.H11MO.0.A hocomoco 1 STA5A . -HUMAN SOX9_HUMAN.H11MO.1.B hocomoco 1 SOX9 . -HUMAN CTCFL_HUMAN.H11MO.0.A hocomoco 1 CTCFL . -HUMAN PPARG_HUMAN.H11MO.1.A hocomoco 1 PPARG . -HUMAN ESR2_HUMAN.H11MO.1.A hocomoco 1 ESR2 . -HUMAN STA5B_HUMAN.H11MO.0.A hocomoco 1 STA5B . -HUMAN ZNF18_HUMAN.H11MO.0.C hocomoco 1 ZNF18 . -HUMAN BARH2_HUMAN.H11MO.0.D hocomoco 1 BARH2 . -HUMAN MAZ_HUMAN.H11MO.1.A hocomoco 1 MAZ . -HUMAN PO6F1_HUMAN.H11MO.0.D hocomoco 1 PO6F1 . -HUMAN HSFY1_HUMAN.H11MO.0.D hocomoco 1 HSFY1 . -HUMAN ZIM3_HUMAN.H11MO.0.C hocomoco 1 ZIM3 . -HUMAN ZN219_HUMAN.H11MO.0.D hocomoco 1 ZN219 . -HUMAN HXD12_HUMAN.H11MO.0.D hocomoco 1 HXD12 . -HUMAN TBX19_HUMAN.H11MO.0.D hocomoco 1 TBX19 . -HUMAN RUNX3_HUMAN.H11MO.0.A hocomoco 1 RUNX3 . -HUMAN AP2B_HUMAN.H11MO.0.B hocomoco 1 AP2B . -HUMAN ZFP28_HUMAN.H11MO.0.C hocomoco 1 ZFP28 . -HUMAN PAX2_HUMAN.H11MO.0.D hocomoco 1 PAX2 . -HUMAN ONEC2_HUMAN.H11MO.0.D hocomoco 1 ONEC2 . -HUMAN IRF8_HUMAN.H11MO.0.B hocomoco 1 IRF8 . -HUMAN NFE2_HUMAN.H11MO.0.A hocomoco 1 NFE2 . -HUMAN PO2F3_HUMAN.H11MO.0.D hocomoco 1 PO2F3 . -HUMAN MBD2_HUMAN.H11MO.0.B hocomoco 1 MBD2 . -HUMAN BARH1_HUMAN.H11MO.0.D hocomoco 1 BARH1 . -HUMAN TFDP1_HUMAN.H11MO.0.C hocomoco 1 TFDP1 . -HUMAN IRF9_HUMAN.H11MO.0.C hocomoco 1 IRF9 . -HUMAN NFAC2_HUMAN.H11MO.0.B hocomoco 1 NFAC2 . -HUMAN NFIA_HUMAN.H11MO.0.C hocomoco 1 NFIA . -HUMAN BARX1_HUMAN.H11MO.0.D hocomoco 1 BARX1 . -HUMAN UNC4_HUMAN.H11MO.0.D hocomoco 1 UNC4 . -HUMAN CXXC1_HUMAN.H11MO.0.D hocomoco 1 CXXC1 . -HUMAN BATF3_HUMAN.H11MO.0.B hocomoco 1 BATF3 . -HUMAN SRY_HUMAN.H11MO.0.B hocomoco 1 SRY . -HUMAN MAFB_HUMAN.H11MO.0.B hocomoco 1 MAFB . -HUMAN PO5F1_HUMAN.H11MO.0.A hocomoco 1 PO5F1 . -HUMAN SPIB_HUMAN.H11MO.0.A hocomoco 1 SPIB . -HUMAN ZN320_HUMAN.H11MO.0.C hocomoco 1 ZN320 . -HUMAN SRBP2_HUMAN.H11MO.0.B hocomoco 1 SRBP2 . -HUMAN MSX2_HUMAN.H11MO.0.D hocomoco 1 MSX2 . -HUMAN CDX1_HUMAN.H11MO.0.C hocomoco 1 CDX1 . -HUMAN HXD3_HUMAN.H11MO.0.D hocomoco 1 HXD3 . -HUMAN ETV3_HUMAN.H11MO.0.D hocomoco 1 ETV3 . -HUMAN RARB_HUMAN.H11MO.0.D hocomoco 1 RARB . -HUMAN ZN136_HUMAN.H11MO.0.C hocomoco 1 ZN136 . -HUMAN ERR2_HUMAN.H11MO.0.A hocomoco 1 ERR2 . -HUMAN NR1H4_HUMAN.H11MO.0.B hocomoco 1 NR1H4 . -HUMAN ZBTB4_HUMAN.H11MO.1.D hocomoco 1 ZBTB4 . -HUMAN MAF_HUMAN.H11MO.1.B hocomoco 1 MAF . -HUMAN GLIS3_HUMAN.H11MO.0.D hocomoco 1 GLIS3 . -HUMAN HNF6_HUMAN.H11MO.0.B hocomoco 1 HNF6 . -HUMAN PITX2_HUMAN.H11MO.0.D hocomoco 1 PITX2 . -HUMAN HNF4G_HUMAN.H11MO.0.B hocomoco 1 HNF4G . -HUMAN ZN350_HUMAN.H11MO.0.C hocomoco 1 ZN350 . -HUMAN ELF1_HUMAN.H11MO.0.A hocomoco 1 ELF1 . -HUMAN ESR1_HUMAN.H11MO.1.A hocomoco 1 ESR1 . -HUMAN MAFK_HUMAN.H11MO.0.A hocomoco 1 MAFK . -HUMAN PITX1_HUMAN.H11MO.0.D hocomoco 1 PITX1 . -HUMAN DLX4_HUMAN.H11MO.0.D hocomoco 1 DLX4 . -HUMAN ZBT48_HUMAN.H11MO.0.C hocomoco 1 ZBT48 . -HUMAN PBX1_HUMAN.H11MO.0.A hocomoco 1 PBX1 . -HUMAN GABPA_HUMAN.H11MO.0.A hocomoco 1 GABPA . -HUMAN ETV6_HUMAN.H11MO.0.D hocomoco 1 ETV6 . -HUMAN RUNX2_HUMAN.H11MO.0.A hocomoco 1 RUNX2 . -HUMAN MAF_HUMAN.H11MO.0.A hocomoco 1 MAF . -HUMAN GRHL2_HUMAN.H11MO.0.A hocomoco 1 GRHL2 . -HUMAN FOXP2_HUMAN.H11MO.0.C hocomoco 1 FOXP2 . -HUMAN VDR_HUMAN.H11MO.1.A hocomoco 1 VDR . -HUMAN ZN586_HUMAN.H11MO.0.C hocomoco 1 ZN586 . -HUMAN FOXC2_HUMAN.H11MO.0.D hocomoco 1 FOXC2 . -HUMAN BHE41_HUMAN.H11MO.0.D hocomoco 1 BHE41 . -HUMAN SP2_HUMAN.H11MO.0.A hocomoco 1 SP2 . -HUMAN GLI1_HUMAN.H11MO.0.D hocomoco 1 GLI1 . -HUMAN LHX8_HUMAN.H11MO.0.D hocomoco 1 LHX8 . -HUMAN STAT4_HUMAN.H11MO.0.A hocomoco 1 STAT4 . -HUMAN STAT1_HUMAN.H11MO.0.A hocomoco 1 STAT1 . -HUMAN MAFG_HUMAN.H11MO.1.A hocomoco 1 MAFG . -HUMAN ZN394_HUMAN.H11MO.1.D hocomoco 1 ZN394 . -HUMAN CREM_HUMAN.H11MO.0.C hocomoco 1 CREM . -HUMAN HXB8_HUMAN.H11MO.0.C hocomoco 1 HXB8 . -HUMAN GATA2_HUMAN.H11MO.1.A hocomoco 1 GATA2 . -HUMAN RFX3_HUMAN.H11MO.0.B hocomoco 1 RFX3 . -HUMAN HXD8_HUMAN.H11MO.0.D hocomoco 1 HXD8 . -HUMAN ZN335_HUMAN.H11MO.0.A hocomoco 1 ZN335 . -HUMAN ZN143_HUMAN.H11MO.0.A hocomoco 1 ZN143 . -HUMAN NR2E3_HUMAN.H11MO.0.C hocomoco 1 NR2E3 . -HUMAN NR5A2_HUMAN.H11MO.0.B hocomoco 1 NR5A2 . -HUMAN ZBT14_HUMAN.H11MO.0.C hocomoco 1 ZBT14 . -HUMAN ATF2_HUMAN.H11MO.2.C hocomoco 1 ATF2 . -HUMAN HLTF_HUMAN.H11MO.0.D hocomoco 1 HLTF . -HUMAN ESX1_HUMAN.H11MO.0.D hocomoco 1 ESX1 . -HUMAN CUX2_HUMAN.H11MO.0.D hocomoco 1 CUX2 . -HUMAN PAX8_HUMAN.H11MO.0.D hocomoco 1 PAX8 . -HUMAN HES7_HUMAN.H11MO.0.D hocomoco 1 HES7 . -HUMAN ZN563_HUMAN.H11MO.1.C hocomoco 1 ZN563 . -HUMAN E2F4_HUMAN.H11MO.0.A hocomoco 1 E2F4 . -HUMAN TBX20_HUMAN.H11MO.0.D hocomoco 1 TBX20 . -HUMAN MEOX1_HUMAN.H11MO.0.D hocomoco 1 MEOX1 . -HUMAN ZNF8_HUMAN.H11MO.0.C hocomoco 1 ZNF8 . -HUMAN VSX2_HUMAN.H11MO.0.D hocomoco 1 VSX2 . -HUMAN ZFX_HUMAN.H11MO.0.A hocomoco 1 ZFX . -HUMAN NFIL3_HUMAN.H11MO.0.D hocomoco 1 NFIL3 . -HUMAN NR1H3_HUMAN.H11MO.0.B hocomoco 1 NR1H3 . -HUMAN RORG_HUMAN.H11MO.0.C hocomoco 1 RORG . -HUMAN GATA4_HUMAN.H11MO.0.A hocomoco 1 GATA4 . -HUMAN SP4_HUMAN.H11MO.0.A hocomoco 1 SP4 . -HUMAN ZN467_HUMAN.H11MO.0.C hocomoco 1 ZN467 . -HUMAN FOXA1_HUMAN.H11MO.0.A hocomoco 1 FOXA1 . -HUMAN SOX7_HUMAN.H11MO.0.D hocomoco 1 SOX7 . -HUMAN KLF13_HUMAN.H11MO.0.D hocomoco 1 KLF13 . -HUMAN OZF_HUMAN.H11MO.0.C hocomoco 1 OZF . -HUMAN CDC5L_HUMAN.H11MO.0.D hocomoco 1 CDC5L . -HUMAN TGIF1_HUMAN.H11MO.0.A hocomoco 1 TGIF1 . -HUMAN HAND1_HUMAN.H11MO.1.D hocomoco 1 HAND1 . -HUMAN OTX1_HUMAN.H11MO.0.D hocomoco 1 OTX1 . -HUMAN HNF1B_HUMAN.H11MO.1.A hocomoco 1 HNF1B . -HUMAN ZN410_HUMAN.H11MO.0.D hocomoco 1 ZN410 . -HUMAN ZNF76_HUMAN.H11MO.0.C hocomoco 1 ZNF76 . -HUMAN SNAI1_HUMAN.H11MO.0.C hocomoco 1 SNAI1 . -HUMAN STF1_HUMAN.H11MO.0.B hocomoco 1 STF1 . -HUMAN MAFK_HUMAN.H11MO.1.A hocomoco 1 MAFK . -HUMAN REST_HUMAN.H11MO.0.A hocomoco 1 REST . -HUMAN PRD14_HUMAN.H11MO.0.A hocomoco 1 PRD14 . -HUMAN EVX2_HUMAN.H11MO.0.A hocomoco 1 EVX2 . -HUMAN AP2A_HUMAN.H11MO.0.A hocomoco 1 AP2A . -HUMAN DLX6_HUMAN.H11MO.0.D hocomoco 1 DLX6 . -HUMAN THAP1_HUMAN.H11MO.0.C hocomoco 1 THAP1 . -HUMAN TFCP2_HUMAN.H11MO.0.D hocomoco 1 TFCP2 . -HUMAN FOXH1_HUMAN.H11MO.0.A hocomoco 1 FOXH1 . -HUMAN HXA11_HUMAN.H11MO.0.D hocomoco 1 HXA11 . -HUMAN FOXO6_HUMAN.H11MO.0.D hocomoco 1 FOXO6 . -HUMAN RARA_HUMAN.H11MO.1.A hocomoco 1 RARA . -HUMAN VDR_HUMAN.H11MO.0.A hocomoco 1 VDR . -HUMAN HXC8_HUMAN.H11MO.0.D hocomoco 1 HXC8 . -HUMAN NFAC4_HUMAN.H11MO.0.C hocomoco 1 NFAC4 . -HUMAN TEAD2_HUMAN.H11MO.0.D hocomoco 1 TEAD2 . -HUMAN NGN2_HUMAN.H11MO.0.D hocomoco 1 NGN2 . -HUMAN PRDM6_HUMAN.H11MO.0.C hocomoco 1 PRDM6 . -HUMAN HXC10_HUMAN.H11MO.0.D hocomoco 1 HXC10 . -HUMAN VEZF1_HUMAN.H11MO.0.C hocomoco 1 VEZF1 . -HUMAN RARA_HUMAN.H11MO.2.A hocomoco 1 RARA . -HUMAN ZN281_HUMAN.H11MO.0.A hocomoco 1 ZN281 . -HUMAN ZN382_HUMAN.H11MO.0.C hocomoco 1 ZN382 . -HUMAN SOX9_HUMAN.H11MO.0.B hocomoco 1 SOX9 . -HUMAN BHE23_HUMAN.H11MO.0.D hocomoco 1 BHE23 . -HUMAN FOS_HUMAN.H11MO.0.A hocomoco 1 FOS . -HUMAN HXA2_HUMAN.H11MO.0.D hocomoco 1 HXA2 . -HUMAN RREB1_HUMAN.H11MO.0.D hocomoco 1 RREB1 . -HUMAN PAX6_HUMAN.H11MO.0.C hocomoco 1 PAX6 . -HUMAN BCL6_HUMAN.H11MO.0.A hocomoco 1 BCL6 . -HUMAN MAFA_HUMAN.H11MO.0.D hocomoco 1 MAFA . -HUMAN ARX_HUMAN.H11MO.0.D hocomoco 1 ARX . -HUMAN KLF15_HUMAN.H11MO.0.A hocomoco 1 KLF15 . -HUMAN SIX1_HUMAN.H11MO.0.A hocomoco 1 SIX1 . -HUMAN WT1_HUMAN.H11MO.0.C hocomoco 1 WT1 . -HUMAN TAF1_HUMAN.H11MO.0.A hocomoco 1 TAF1 . -HUMAN USF1_HUMAN.H11MO.0.A hocomoco 1 USF1 . -HUMAN DMRT1_HUMAN.H11MO.0.D hocomoco 1 DMRT1 . -HUMAN PLAG1_HUMAN.H11MO.0.D hocomoco 1 PLAG1 . -HUMAN EGR4_HUMAN.H11MO.0.D hocomoco 1 EGR4 . -HUMAN COT1_HUMAN.H11MO.1.C hocomoco 1 COT1 . -HUMAN HXB1_HUMAN.H11MO.0.D hocomoco 1 HXB1 . -HUMAN GSC2_HUMAN.H11MO.0.D hocomoco 1 GSC2 . -HUMAN ZN263_HUMAN.H11MO.1.A hocomoco 1 ZN263 . -HUMAN FOXI1_HUMAN.H11MO.0.B hocomoco 1 FOXI1 . -HUMAN PHX2A_HUMAN.H11MO.0.D hocomoco 1 PHX2A . -HUMAN HEN1_HUMAN.H11MO.0.C hocomoco 1 HEN1 . -HUMAN PPARA_HUMAN.H11MO.0.B hocomoco 1 PPARA . -HUMAN FOXC1_HUMAN.H11MO.0.C hocomoco 1 FOXC1 . -HUMAN SOX5_HUMAN.H11MO.0.C hocomoco 1 SOX5 . -HUMAN NKX31_HUMAN.H11MO.0.C hocomoco 1 NKX31 . -HUMAN SMCA1_HUMAN.H11MO.0.C hocomoco 1 SMCA1 . -HUMAN ZN708_HUMAN.H11MO.1.D hocomoco 1 ZN708 . -HUMAN GATA1_HUMAN.H11MO.1.A hocomoco 1 GATA1 . -HUMAN MSX1_HUMAN.H11MO.0.D hocomoco 1 MSX1 . -HUMAN ZN282_HUMAN.H11MO.0.D hocomoco 1 ZN282 . -HUMAN KAISO_HUMAN.H11MO.2.A hocomoco 1 KAISO . -HUMAN LHX3_HUMAN.H11MO.0.C hocomoco 1 LHX3 . -HUMAN HMGA1_HUMAN.H11MO.0.D hocomoco 1 HMGA1 . -HUMAN NRF1_HUMAN.H11MO.0.A hocomoco 1 NRF1 . -HUMAN SOX2_HUMAN.H11MO.0.A hocomoco 1 SOX2 . -HUMAN SOX10_HUMAN.H11MO.0.B hocomoco 1 SOX10 . -HUMAN ZN784_HUMAN.H11MO.0.D hocomoco 1 ZN784 . -HUMAN COT2_HUMAN.H11MO.0.A hocomoco 1 COT2 . -HUMAN STAT6_HUMAN.H11MO.0.B hocomoco 1 STAT6 . -HUMAN ARNT_HUMAN.H11MO.0.B hocomoco 1 ARNT . -HUMAN NKX21_HUMAN.H11MO.0.A hocomoco 1 NKX21 . -HUMAN NKX61_HUMAN.H11MO.0.B hocomoco 1 NKX61 . -HUMAN NKX28_HUMAN.H11MO.0.C hocomoco 1 NKX28 . -HUMAN NR1H4_HUMAN.H11MO.1.B hocomoco 1 NR1H4 . -HUMAN AP2D_HUMAN.H11MO.0.D hocomoco 1 AP2D . -HUMAN ATF7_HUMAN.H11MO.0.D hocomoco 1 ATF7 . -HUMAN RXRA_HUMAN.H11MO.1.A hocomoco 1 RXRA . -HUMAN ZN524_HUMAN.H11MO.0.D hocomoco 1 ZN524 . -HUMAN ESR2_HUMAN.H11MO.0.A hocomoco 1 ESR2 . -HUMAN NR1I2_HUMAN.H11MO.1.D hocomoco 1 NR1I2 . -HUMAN ZN582_HUMAN.H11MO.0.C hocomoco 1 ZN582 . -HUMAN MAFG_HUMAN.H11MO.0.A hocomoco 1 MAFG . -HUMAN IKZF1_HUMAN.H11MO.0.C hocomoco 1 IKZF1 . -HUMAN ATF1_HUMAN.H11MO.0.B hocomoco 1 ATF1 . -HUMAN HSF2_HUMAN.H11MO.0.A hocomoco 1 HSF2 . -HUMAN ITF2_HUMAN.H11MO.0.C hocomoco 1 ITF2 . -HUMAN NR2C2_HUMAN.H11MO.0.B hocomoco 1 NR2C2 . -HUMAN PDX1_HUMAN.H11MO.1.A hocomoco 1 PDX1 . -HUMAN NFIA_HUMAN.H11MO.1.D hocomoco 1 NFIA . -HUMAN HOMEZ_HUMAN.H11MO.0.D hocomoco 1 HOMEZ . -HUMAN ID4_HUMAN.H11MO.0.D hocomoco 1 ID4 . -HUMAN GATA5_HUMAN.H11MO.0.D hocomoco 1 GATA5 . -HUMAN SRF_HUMAN.H11MO.0.A hocomoco 1 SRF . -HUMAN E2F2_HUMAN.H11MO.0.B hocomoco 1 E2F2 . -HUMAN SOX10_HUMAN.H11MO.1.A hocomoco 1 SOX10 . -HUMAN HEY1_HUMAN.H11MO.0.D hocomoco 1 HEY1 . -HUMAN BPTF_HUMAN.H11MO.0.D hocomoco 1 BPTF . -HUMAN ZN770_HUMAN.H11MO.0.C hocomoco 1 ZN770 . -HUMAN SP1_HUMAN.H11MO.1.A hocomoco 1 SP1 . -HUMAN ZFHX3_HUMAN.H11MO.0.D hocomoco 1 ZFHX3 . -HUMAN ZN708_HUMAN.H11MO.0.C hocomoco 1 ZN708 . -HUMAN CEBPZ_HUMAN.H11MO.0.D hocomoco 1 CEBPZ . -HUMAN TFAP4_HUMAN.H11MO.0.A hocomoco 1 TFAP4 . -HUMAN TEAD3_HUMAN.H11MO.0.D hocomoco 1 TEAD3 . -HUMAN NR1D1_HUMAN.H11MO.0.B hocomoco 1 NR1D1 . -HUMAN HMGA2_HUMAN.H11MO.0.D hocomoco 1 HMGA2 . -HUMAN NKX25_HUMAN.H11MO.0.B hocomoco 1 NKX25 . -HUMAN SP1_HUMAN.H11MO.0.A hocomoco 1 SP1 . -HUMAN TF7L2_HUMAN.H11MO.0.A hocomoco 1 TF7L2 . -HUMAN NKX22_HUMAN.H11MO.0.D hocomoco 1 NKX22 . -HUMAN TBR1_HUMAN.H11MO.0.D hocomoco 1 TBR1 . -HUMAN MAFF_HUMAN.H11MO.0.B hocomoco 1 MAFF . -HUMAN ISX_HUMAN.H11MO.0.D hocomoco 1 ISX . -HUMAN ZF64A_HUMAN.H11MO.0.D hocomoco 1 ZF64A . -HUMAN ZBT18_HUMAN.H11MO.0.C hocomoco 1 ZBT18 . -HUMAN TAL1_HUMAN.H11MO.0.A hocomoco 1 TAL1 . -HUMAN HMX1_HUMAN.H11MO.0.D hocomoco 1 HMX1 . -HUMAN TBX21_HUMAN.H11MO.0.A hocomoco 1 TBX21 . -HUMAN SCRT1_HUMAN.H11MO.0.D hocomoco 1 SCRT1 . -HUMAN ZN250_HUMAN.H11MO.0.C hocomoco 1 ZN250 . -HUMAN EGR3_HUMAN.H11MO.0.D hocomoco 1 EGR3 . -HUMAN TWST1_HUMAN.H11MO.0.A hocomoco 1 TWST1 . -HUMAN SPZ1_HUMAN.H11MO.0.D hocomoco 1 SPZ1 . -HUMAN SIX2_HUMAN.H11MO.0.A hocomoco 1 SIX2 . -HUMAN DBP_HUMAN.H11MO.0.B hocomoco 1 DBP . -HUMAN HXC11_HUMAN.H11MO.0.D hocomoco 1 HXC11 . -HUMAN MITF_HUMAN.H11MO.0.A hocomoco 1 MITF . -HUMAN MYOG_HUMAN.H11MO.0.B hocomoco 1 MYOG . -HUMAN SHOX_HUMAN.H11MO.0.D hocomoco 1 SHOX . -HUMAN PAX3_HUMAN.H11MO.0.D hocomoco 1 PAX3 . -HUMAN HXA13_HUMAN.H11MO.0.C hocomoco 1 HXA13 . -HUMAN STAT2_HUMAN.H11MO.0.A hocomoco 1 STAT2 . -HUMAN GCM1_HUMAN.H11MO.0.D hocomoco 1 GCM1 . -HUMAN TFE2_HUMAN.H11MO.0.A hocomoco 1 TFE2 . -HUMAN NKX61_HUMAN.H11MO.1.B hocomoco 1 NKX61 . -HUMAN ZEB1_HUMAN.H11MO.0.A hocomoco 1 ZEB1 . -HUMAN IRF7_HUMAN.H11MO.0.C hocomoco 1 IRF7 . -HUMAN SMAD1_HUMAN.H11MO.0.D hocomoco 1 SMAD1 . -HUMAN FOXO3_HUMAN.H11MO.0.B hocomoco 1 FOXO3 . -HUMAN EOMES_HUMAN.H11MO.0.D hocomoco 1 EOMES . -HUMAN ZIC2_HUMAN.H11MO.0.D hocomoco 1 ZIC2 . -HUMAN ZKSC3_HUMAN.H11MO.0.D hocomoco 1 ZKSC3 . -HUMAN ZN350_HUMAN.H11MO.1.D hocomoco 1 ZN350 . -HUMAN SMAD4_HUMAN.H11MO.0.B hocomoco 1 SMAD4 . -HUMAN CEBPG_HUMAN.H11MO.0.B hocomoco 1 CEBPG . -HUMAN STAT1_HUMAN.H11MO.1.A hocomoco 1 STAT1 . -HUMAN P53_HUMAN.H11MO.0.A hocomoco 1 P53 . -HUMAN ARI3A_HUMAN.H11MO.0.D hocomoco 1 ARI3A . -HUMAN ERG_HUMAN.H11MO.0.A hocomoco 1 ERG . -HUMAN RFX2_HUMAN.H11MO.1.A hocomoco 1 RFX2 . -HUMAN GSX1_HUMAN.H11MO.0.D hocomoco 1 GSX1 . -HUMAN LMX1B_HUMAN.H11MO.0.D hocomoco 1 LMX1B . -HUMAN NOBOX_HUMAN.H11MO.0.C hocomoco 1 NOBOX . -HUMAN ZN816_HUMAN.H11MO.0.C hocomoco 1 ZN816 . -HUMAN EGR2_HUMAN.H11MO.0.A hocomoco 1 EGR2 . -HUMAN HAND1_HUMAN.H11MO.0.D hocomoco 1 HAND1 . -HUMAN HXD11_HUMAN.H11MO.0.D hocomoco 1 HXD11 . -HUMAN ZN333_HUMAN.H11MO.0.D hocomoco 1 ZN333 . -HUMAN PBX3_HUMAN.H11MO.1.A hocomoco 1 PBX3 . -HUMAN CENPB_HUMAN.H11MO.0.D hocomoco 1 CENPB . -HUMAN GMEB2_HUMAN.H11MO.0.D hocomoco 1 GMEB2 . -HUMAN NR1I3_HUMAN.H11MO.0.C hocomoco 1 NR1I3 . -HUMAN IRX2_HUMAN.H11MO.0.D hocomoco 1 IRX2 . -HUMAN PRDM4_HUMAN.H11MO.0.D hocomoco 1 PRDM4 . -HUMAN RFX5_HUMAN.H11MO.0.A hocomoco 1 RFX5 . -HUMAN ETV2_HUMAN.H11MO.0.B hocomoco 1 ETV2 . -HUMAN IRX3_HUMAN.H11MO.0.D hocomoco 1 IRX3 . -HUMAN ZN713_HUMAN.H11MO.0.D hocomoco 1 ZN713 . -HUMAN SPDEF_HUMAN.H11MO.0.D hocomoco 1 SPDEF . -HUMAN KAISO_HUMAN.H11MO.0.A hocomoco 1 KAISO . -HUMAN ZEP1_HUMAN.H11MO.0.D hocomoco 1 ZEP1 . -HUMAN FEZF1_HUMAN.H11MO.0.C hocomoco 1 FEZF1 . -HUMAN EMX2_HUMAN.H11MO.0.D hocomoco 1 EMX2 . -HUMAN NANOG_HUMAN.H11MO.1.B hocomoco 1 NANOG . -HUMAN MCR_HUMAN.H11MO.0.D hocomoco 1 MCR . -HUMAN ZKSC1_HUMAN.H11MO.0.B hocomoco 1 ZKSC1 . -HUMAN ZSCA4_HUMAN.H11MO.0.D hocomoco 1 ZSCA4 . -HUMAN RUNX1_HUMAN.H11MO.0.A hocomoco 1 RUNX1 . -HUMAN SOX3_HUMAN.H11MO.0.B hocomoco 1 SOX3 . -HUMAN ZN121_HUMAN.H11MO.0.C hocomoco 1 ZN121 . -HUMAN GCR_HUMAN.H11MO.1.A hocomoco 1 GCR . -HUMAN HXB4_HUMAN.H11MO.0.B hocomoco 1 HXB4 . -HUMAN BRAC_HUMAN.H11MO.0.A hocomoco 1 BRAC . -HUMAN NOTO_HUMAN.H11MO.0.D hocomoco 1 NOTO . -HUMAN E2F8_HUMAN.H11MO.0.D hocomoco 1 E2F8 . -HUMAN DDIT3_HUMAN.H11MO.0.D hocomoco 1 DDIT3 . -HUMAN EPAS1_HUMAN.H11MO.0.B hocomoco 1 EPAS1 . -HUMAN MEF2D_HUMAN.H11MO.0.A hocomoco 1 MEF2D . -HUMAN KLF12_HUMAN.H11MO.0.C hocomoco 1 KLF12 . -HUMAN PURA_HUMAN.H11MO.0.D hocomoco 1 PURA . -HUMAN LMX1A_HUMAN.H11MO.0.D hocomoco 1 LMX1A . -HUMAN PLAL1_HUMAN.H11MO.0.D hocomoco 1 PLAL1 . -HUMAN NR4A1_HUMAN.H11MO.0.A hocomoco 1 NR4A1 . -HUMAN E2F4_HUMAN.H11MO.1.A hocomoco 1 E2F4 . -HUMAN P73_HUMAN.H11MO.1.A hocomoco 1 P73 . -HUMAN ZIC1_HUMAN.H11MO.0.B hocomoco 1 ZIC1 . -HUMAN ASCL1_HUMAN.H11MO.0.A hocomoco 1 ASCL1 . -HUMAN SMCA5_HUMAN.H11MO.0.C hocomoco 1 SMCA5 . -HUMAN ZSC16_HUMAN.H11MO.0.D hocomoco 1 ZSC16 . -HUMAN FOXA3_HUMAN.H11MO.0.B hocomoco 1 FOXA3 . -HUMAN TYY1_HUMAN.H11MO.0.A hocomoco 1 TYY1 . -HUMAN AP2C_HUMAN.H11MO.0.A hocomoco 1 AP2C . -HUMAN ALX1_HUMAN.H11MO.0.B hocomoco 1 ALX1 . -HUMAN NR4A2_HUMAN.H11MO.0.C hocomoco 1 NR4A2 . -HUMAN MEF2A_HUMAN.H11MO.0.A hocomoco 1 MEF2A . -HUMAN ZNF85_HUMAN.H11MO.0.C hocomoco 1 ZNF85 . -HUMAN HXD4_HUMAN.H11MO.0.D hocomoco 1 HXD4 . -HUMAN FOXP1_HUMAN.H11MO.0.A hocomoco 1 FOXP1 . -HUMAN THB_HUMAN.H11MO.1.D hocomoco 1 THB . -HUMAN NFAC1_HUMAN.H11MO.0.B hocomoco 1 NFAC1 . -HUMAN RARG_HUMAN.H11MO.0.B hocomoco 1 RARG . -HUMAN PRRX1_HUMAN.H11MO.0.D hocomoco 1 PRRX1 . -HUMAN FOSL2_HUMAN.H11MO.0.A hocomoco 1 FOSL2 . -HUMAN TAL1_HUMAN.H11MO.1.A hocomoco 1 TAL1 . -HUMAN NRL_HUMAN.H11MO.0.D hocomoco 1 NRL . -HUMAN E2F3_HUMAN.H11MO.0.A hocomoco 1 E2F3 . -HUMAN ETS1_HUMAN.H11MO.0.A hocomoco 1 ETS1 . -HUMAN ZN329_HUMAN.H11MO.0.C hocomoco 1 ZN329 . -HUMAN KLF8_HUMAN.H11MO.0.C hocomoco 1 KLF8 . -HUMAN Z354A_HUMAN.H11MO.0.C hocomoco 1 Z354A . -HUMAN PHX2B_HUMAN.H11MO.0.D hocomoco 1 PHX2B . -HUMAN FOXJ3_HUMAN.H11MO.0.A hocomoco 1 FOXJ3 . -HUMAN CREB5_HUMAN.H11MO.0.D hocomoco 1 CREB5 . -HUMAN ZN547_HUMAN.H11MO.0.C hocomoco 1 ZN547 . -HUMAN NKX62_HUMAN.H11MO.0.D hocomoco 1 NKX62 . -HUMAN GBX1_HUMAN.H11MO.0.D hocomoco 1 GBX1 . -HUMAN TBX1_HUMAN.H11MO.0.D hocomoco 1 TBX1 . -HUMAN RFX2_HUMAN.H11MO.0.A hocomoco 1 RFX2 . -HUMAN RXRG_HUMAN.H11MO.0.B hocomoco 1 RXRG . -HUMAN SP2_HUMAN.H11MO.1.B hocomoco 1 SP2 . -HUMAN RARG_HUMAN.H11MO.2.D hocomoco 1 RARG . -HUMAN BC11A_HUMAN.H11MO.0.A hocomoco 1 BC11A . -HUMAN ZBED1_HUMAN.H11MO.0.D hocomoco 1 ZBED1 . -HUMAN NFIC_HUMAN.H11MO.0.A hocomoco 1 NFIC . -HUMAN ZFP82_HUMAN.H11MO.0.C hocomoco 1 ZFP82 . -HUMAN DUX4_HUMAN.H11MO.0.A hocomoco 1 DUX4 . -HUMAN FOXJ2_HUMAN.H11MO.0.C hocomoco 1 FOXJ2 . -HUMAN DLX5_HUMAN.H11MO.0.D hocomoco 1 DLX5 . -HUMAN GLI2_HUMAN.H11MO.0.D hocomoco 1 GLI2 . -HUMAN DLX3_HUMAN.H11MO.0.C hocomoco 1 DLX3 . -HUMAN SMAD2_HUMAN.H11MO.0.A hocomoco 1 SMAD2 . -HUMAN HNF4A_HUMAN.H11MO.0.A hocomoco 1 HNF4A . -HUMAN XBP1_HUMAN.H11MO.0.D hocomoco 1 XBP1 . -HUMAN ZN335_HUMAN.H11MO.1.A hocomoco 1 ZN335 . -HUMAN HXB6_HUMAN.H11MO.0.D hocomoco 1 HXB6 . -HUMAN TBX2_HUMAN.H11MO.0.D hocomoco 1 TBX2 . -HUMAN GRHL1_HUMAN.H11MO.0.D hocomoco 1 GRHL1 . -HUMAN SALL4_HUMAN.H11MO.0.B hocomoco 1 SALL4 . -HUMAN GCR_HUMAN.H11MO.0.A hocomoco 1 GCR . -HUMAN PO2F1_HUMAN.H11MO.0.C hocomoco 1 PO2F1 . -HUMAN SRBP1_HUMAN.H11MO.0.A hocomoco 1 SRBP1 . -HUMAN SOX11_HUMAN.H11MO.0.D hocomoco 1 SOX11 . -HUMAN NF2L1_HUMAN.H11MO.0.C hocomoco 1 NF2L1 . -HUMAN ZN770_HUMAN.H11MO.1.C hocomoco 1 ZN770 . -HUMAN NFAC3_HUMAN.H11MO.0.B hocomoco 1 NFAC3 . -HUMAN BHE40_HUMAN.H11MO.0.A hocomoco 1 BHE40 . -HUMAN SP4_HUMAN.H11MO.1.A hocomoco 1 SP4 . -HUMAN FOXF2_HUMAN.H11MO.0.D hocomoco 1 FOXF2 . -HUMAN PROX1_HUMAN.H11MO.0.D hocomoco 1 PROX1 . -HUMAN FOXL1_HUMAN.H11MO.0.D hocomoco 1 FOXL1 . -HUMAN BCL6B_HUMAN.H11MO.0.D hocomoco 1 BCL6B . -HUMAN HXA10_HUMAN.H11MO.0.C hocomoco 1 HXA10 . -HUMAN VAX1_HUMAN.H11MO.0.D hocomoco 1 VAX1 . -HUMAN ZN384_HUMAN.H11MO.0.C hocomoco 1 ZN384 . -HUMAN MYBB_HUMAN.H11MO.0.D hocomoco 1 MYBB . -HUMAN ZFP42_HUMAN.H11MO.0.A hocomoco 1 ZFP42 . -HUMAN MESP1_HUMAN.H11MO.0.D hocomoco 1 MESP1 . -HUMAN OLIG3_HUMAN.H11MO.0.D hocomoco 1 OLIG3 . -HUMAN BMAL1_HUMAN.H11MO.0.A hocomoco 1 BMAL1 . -HUMAN FOXB1_HUMAN.H11MO.0.D hocomoco 1 FOXB1 . -HUMAN EVI1_HUMAN.H11MO.0.B hocomoco 1 EVI1 . -HUMAN ZN317_HUMAN.H11MO.0.C hocomoco 1 ZN317 . -HUMAN THA11_HUMAN.H11MO.0.B hocomoco 1 THA11 . -HUMAN ZN667_HUMAN.H11MO.0.C hocomoco 1 ZN667 . -HUMAN TBP_HUMAN.H11MO.0.A hocomoco 1 TBP . -HUMAN GBX2_HUMAN.H11MO.0.D hocomoco 1 GBX2 . -HUMAN HMX3_HUMAN.H11MO.0.D hocomoco 1 HMX3 . -HUMAN MYNN_HUMAN.H11MO.0.D hocomoco 1 MYNN . -HUMAN MNX1_HUMAN.H11MO.0.D hocomoco 1 MNX1 . -HUMAN PITX3_HUMAN.H11MO.0.D hocomoco 1 PITX3 . -HUMAN HNF1B_HUMAN.H11MO.0.A hocomoco 1 HNF1B . -HUMAN MYC_HUMAN.H11MO.0.A hocomoco 1 MYC . -HUMAN ETV7_HUMAN.H11MO.0.D hocomoco 1 ETV7 . -HUMAN FOXD2_HUMAN.H11MO.0.D hocomoco 1 FOXD2 . -HUMAN HXB2_HUMAN.H11MO.0.D hocomoco 1 HXB2 . -HUMAN HXC12_HUMAN.H11MO.0.D hocomoco 1 HXC12 . -HUMAN MYCN_HUMAN.H11MO.0.A hocomoco 1 MYCN . -HUMAN JDP2_HUMAN.H11MO.0.D hocomoco 1 JDP2 . -HUMAN ETS2_HUMAN.H11MO.0.B hocomoco 1 ETS2 . -HUMAN ZN740_HUMAN.H11MO.0.D hocomoco 1 ZN740 . -HUMAN ZBT49_HUMAN.H11MO.0.D hocomoco 1 ZBT49 . -HUMAN P63_HUMAN.H11MO.0.A hocomoco 1 P63 . -HUMAN RARG_HUMAN.H11MO.1.B hocomoco 1 RARG . -HUMAN E2F6_HUMAN.H11MO.0.A hocomoco 1 E2F6 . -HUMAN PAX5_HUMAN.H11MO.0.A hocomoco 1 PAX5 . -HUMAN E2F5_HUMAN.H11MO.0.B hocomoco 1 E2F5 . -HUMAN ATF2_HUMAN.H11MO.1.B hocomoco 1 ATF2 . -HUMAN HXB3_HUMAN.H11MO.0.D hocomoco 1 HXB3 . -HUMAN PO4F3_HUMAN.H11MO.0.D hocomoco 1 PO4F3 . -HUMAN LHX2_HUMAN.H11MO.0.A hocomoco 1 LHX2 . -HUMAN ZN418_HUMAN.H11MO.1.D hocomoco 1 ZN418 . -HUMAN NR4A3_HUMAN.H11MO.0.D hocomoco 1 NR4A3 . -HUMAN SHOX2_HUMAN.H11MO.0.D hocomoco 1 SHOX2 . -HUMAN SNAI2_HUMAN.H11MO.0.A hocomoco 1 SNAI2 . -HUMAN FOXF1_HUMAN.H11MO.0.D hocomoco 1 FOXF1 . -HUMAN KLF5_HUMAN.H11MO.0.A hocomoco 1 KLF5 . -HUMAN NFKB1_HUMAN.H11MO.0.A hocomoco 1 NFKB1 . -HUMAN HXA1_HUMAN.H11MO.0.C hocomoco 1 HXA1 . -HUMAN ZNF41_HUMAN.H11MO.0.C hocomoco 1 ZNF41 . -HUMAN PRGR_HUMAN.H11MO.1.A hocomoco 1 PRGR . -HUMAN ZBTB4_HUMAN.H11MO.0.D hocomoco 1 ZBTB4 . -HUMAN ZN394_HUMAN.H11MO.0.C hocomoco 1 ZN394 . -HUMAN RX_HUMAN.H11MO.0.D hocomoco 1 RX . -HUMAN HXB13_HUMAN.H11MO.0.A hocomoco 1 HXB13 . -HUMAN LHX4_HUMAN.H11MO.0.D hocomoco 1 LHX4 . -HUMAN CDX2_HUMAN.H11MO.0.A hocomoco 1 CDX2 . -HUMAN SUH_HUMAN.H11MO.0.A hocomoco 1 SUH . -HUMAN GFI1_HUMAN.H11MO.0.C hocomoco 1 GFI1 . -HUMAN BSH_HUMAN.H11MO.0.D hocomoco 1 BSH . -HUMAN FOXO4_HUMAN.H11MO.0.C hocomoco 1 FOXO4 . -HUMAN MEF2B_HUMAN.H11MO.0.A hocomoco 1 MEF2B . -HUMAN LYL1_HUMAN.H11MO.0.A hocomoco 1 LYL1 . -HUMAN PO3F4_HUMAN.H11MO.0.D hocomoco 1 PO3F4 . -HUMAN CREB3_HUMAN.H11MO.0.D hocomoco 1 CREB3 . -HUMAN SOX1_HUMAN.H11MO.0.D hocomoco 1 SOX1 . -HUMAN ZN549_HUMAN.H11MO.0.C hocomoco 1 ZN549 . -HUMAN SMAD3_HUMAN.H11MO.0.B hocomoco 1 SMAD3 . -HUMAN MZF1_HUMAN.H11MO.0.B hocomoco 1 MZF1 . -HUMAN NFYB_HUMAN.H11MO.0.A hocomoco 1 NFYB . -HUMAN ASCL2_HUMAN.H11MO.0.D hocomoco 1 ASCL2 . -HUMAN FOXD1_HUMAN.H11MO.0.D hocomoco 1 FOXD1 . -HUMAN SOX8_HUMAN.H11MO.0.D hocomoco 1 SOX8 . -HUMAN BACH1_HUMAN.H11MO.0.A hocomoco 1 BACH1 . -HUMAN NKX23_HUMAN.H11MO.0.D hocomoco 1 NKX23 . -HUMAN MLX_HUMAN.H11MO.0.D hocomoco 1 MLX . -HUMAN NDF2_HUMAN.H11MO.0.B hocomoco 1 NDF2 . -HUMAN DMBX1_HUMAN.H11MO.0.D hocomoco 1 DMBX1 . -HUMAN MAZ_HUMAN.H11MO.0.A hocomoco 1 MAZ . -HUMAN ZNF85_HUMAN.H11MO.1.C hocomoco 1 ZNF85 . -HUMAN COT2_HUMAN.H11MO.1.A hocomoco 1 COT2 . -HUMAN RARA_HUMAN.H11MO.0.A hocomoco 1 RARA . -HUMAN ELF3_HUMAN.H11MO.0.A hocomoco 1 ELF3 . -HUMAN BARX2_HUMAN.H11MO.0.D hocomoco 1 BARX2 . -HUMAN NFAT5_HUMAN.H11MO.0.D hocomoco 1 NFAT5 . -HUMAN ELF5_HUMAN.H11MO.0.A hocomoco 1 ELF5 . -HUMAN EGR1_HUMAN.H11MO.0.A hocomoco 1 EGR1 . -HUMAN HXC9_HUMAN.H11MO.0.C hocomoco 1 HXC9 . -HUMAN ISL1_HUMAN.H11MO.0.A hocomoco 1 ISL1 . -HUMAN OVOL2_HUMAN.H11MO.0.D hocomoco 1 OVOL2 . -HUMAN PPARA_HUMAN.H11MO.1.B hocomoco 1 PPARA . -HUMAN CPEB1_HUMAN.H11MO.0.D hocomoco 1 CPEB1 . -HUMAN HXB7_HUMAN.H11MO.0.C hocomoco 1 HXB7 . -HUMAN HXD13_HUMAN.H11MO.0.D hocomoco 1 HXD13 . -HUMAN P5F1B_HUMAN.H11MO.0.D hocomoco 1 P5F1B . -HUMAN PATZ1_HUMAN.H11MO.1.C hocomoco 1 PATZ1 . -HUMAN TEAD4_HUMAN.H11MO.0.A hocomoco 1 TEAD4 . -HUMAN TF65_HUMAN.H11MO.0.A hocomoco 1 TF65 . -HUMAN GLI3_HUMAN.H11MO.0.B hocomoco 1 GLI3 . -HUMAN GATA1_HUMAN.H11MO.0.A hocomoco 1 GATA1 . -HUMAN HIC1_HUMAN.H11MO.0.C hocomoco 1 HIC1 . -HUMAN ZN490_HUMAN.H11MO.0.C hocomoco 1 ZN490 . -HUMAN EHF_HUMAN.H11MO.0.B hocomoco 1 EHF . -HUMAN PO5F1_HUMAN.H11MO.1.A hocomoco 1 PO5F1 . -HUMAN PPARD_HUMAN.H11MO.0.D hocomoco 1 PPARD . -HUMAN HMX2_HUMAN.H11MO.0.D hocomoco 1 HMX2 . -HUMAN ETV1_HUMAN.H11MO.0.A hocomoco 1 ETV1 . -HUMAN NKX32_HUMAN.H11MO.0.C hocomoco 1 NKX32 . -HUMAN SOX4_HUMAN.H11MO.0.B hocomoco 1 SOX4 . -HUMAN ESR1_HUMAN.H11MO.0.A hocomoco 1 ESR1 . -HUMAN VAX2_HUMAN.H11MO.0.D hocomoco 1 VAX2 . -HUMAN KLF4_HUMAN.H11MO.0.A hocomoco 1 KLF4 . -HUMAN WT1_HUMAN.H11MO.1.B hocomoco 1 WT1 . -HUMAN ZN341_HUMAN.H11MO.1.C hocomoco 1 ZN341 . -HUMAN BACH2_HUMAN.H11MO.0.A hocomoco 1 BACH2 . -HUMAN LEF1_HUMAN.H11MO.0.A hocomoco 1 LEF1 . -HUMAN HME2_HUMAN.H11MO.0.D hocomoco 1 HME2 . -HUMAN RAX2_HUMAN.H11MO.0.D hocomoco 1 RAX2 . -HUMAN TLX1_HUMAN.H11MO.0.D hocomoco 1 TLX1 . -HUMAN ATF3_HUMAN.H11MO.0.A hocomoco 1 ATF3 . -HUMAN ZFX_HUMAN.H11MO.1.A hocomoco 1 ZFX . -HUMAN RFX1_HUMAN.H11MO.1.B hocomoco 1 RFX1 . -HUMAN FOSB_HUMAN.H11MO.0.A hocomoco 1 FOSB . -HUMAN CTCF_HUMAN.H11MO.0.A hocomoco 1 CTCF . -HUMAN PO6F2_HUMAN.H11MO.0.D hocomoco 1 PO6F2 . -HUMAN FLI1_HUMAN.H11MO.1.A hocomoco 1 FLI1 . -HUMAN JUNB_HUMAN.H11MO.0.A hocomoco 1 JUNB . -HUMAN EMX1_HUMAN.H11MO.0.D hocomoco 1 EMX1 . -HUMAN ZN260_HUMAN.H11MO.0.C hocomoco 1 ZN260 . -HUMAN THA_HUMAN.H11MO.0.C hocomoco 1 THA . -HUMAN FOXO1_HUMAN.H11MO.0.A hocomoco 1 FOXO1 . -HUMAN AHR_HUMAN.H11MO.0.B hocomoco 1 AHR . -HUMAN HXC13_HUMAN.H11MO.0.D hocomoco 1 HXC13 . -HUMAN GATA2_HUMAN.H11MO.0.A hocomoco 1 GATA2 . -HUMAN NR1H3_HUMAN.H11MO.1.B hocomoco 1 NR1H3 . -HUMAN NR1H2_HUMAN.H11MO.0.D hocomoco 1 NR1H2 . -HUMAN SOX21_HUMAN.H11MO.0.D hocomoco 1 SOX21 . -HUMAN ZN502_HUMAN.H11MO.0.C hocomoco 1 ZN502 . -HUMAN MXI1_HUMAN.H11MO.0.A hocomoco 1 MXI1 . -HUMAN LHX9_HUMAN.H11MO.0.D hocomoco 1 LHX9 . -HUMAN PO3F3_HUMAN.H11MO.0.D hocomoco 1 PO3F3 . -HUMAN CEBPE_HUMAN.H11MO.0.A hocomoco 1 CEBPE . -HUMAN DRGX_HUMAN.H11MO.0.D hocomoco 1 DRGX . -HUMAN TFE3_HUMAN.H11MO.0.B hocomoco 1 TFE3 . -HUMAN CR3L2_HUMAN.H11MO.0.D hocomoco 1 CR3L2 . -HUMAN IRF1_HUMAN.H11MO.0.A hocomoco 1 IRF1 . -HUMAN HXC6_HUMAN.H11MO.0.D hocomoco 1 HXC6 . -HUMAN ZN554_HUMAN.H11MO.1.D hocomoco 1 ZN554 . -HUMAN TBX3_HUMAN.H11MO.0.C hocomoco 1 TBX3 . -HUMAN IRF5_HUMAN.H11MO.0.D hocomoco 1 IRF5 . -HUMAN GCM2_HUMAN.H11MO.0.D hocomoco 1 GCM2 . -HUMAN TF7L1_HUMAN.H11MO.0.B hocomoco 1 TF7L1 . -HUMAN ETV4_HUMAN.H11MO.0.B hocomoco 1 ETV4 . -HUMAN ZN257_HUMAN.H11MO.0.C hocomoco 1 ZN257 . -HUMAN HSF1_HUMAN.H11MO.0.A hocomoco 1 HSF1 . -HUMAN PO4F1_HUMAN.H11MO.0.D hocomoco 1 PO4F1 . -HUMAN HMBX1_HUMAN.H11MO.0.D hocomoco 1 HMBX1 . -HUMAN ZNF41_HUMAN.H11MO.1.C hocomoco 1 ZNF41 . -HUMAN ZBT7B_HUMAN.H11MO.0.D hocomoco 1 ZBT7B . -HUMAN HXA9_HUMAN.H11MO.0.B hocomoco 1 HXA9 . -HUMAN ZN140_HUMAN.H11MO.0.C hocomoco 1 ZN140 . -HUMAN FUBP1_HUMAN.H11MO.0.D hocomoco 1 FUBP1 . -HUMAN IRF2_HUMAN.H11MO.0.A hocomoco 1 IRF2 . -HUMAN DLX2_HUMAN.H11MO.0.D hocomoco 1 DLX2 . -HUMAN GSC_HUMAN.H11MO.0.D hocomoco 1 GSC . -HUMAN OSR2_HUMAN.H11MO.0.C hocomoco 1 OSR2 . -HUMAN BHA15_HUMAN.H11MO.0.B hocomoco 1 BHA15 . -HUMAN ATF2_HUMAN.H11MO.0.B hocomoco 1 ATF2 . -HUMAN ZN768_HUMAN.H11MO.0.C hocomoco 1 ZN768 . -HUMAN PPARG_HUMAN.H11MO.0.A hocomoco 1 PPARG . -HUMAN RFX5_HUMAN.H11MO.1.A hocomoco 1 RFX5 . -HUMAN GLIS1_HUMAN.H11MO.0.D hocomoco 1 GLIS1 . -HUMAN MXI1_HUMAN.H11MO.1.A hocomoco 1 MXI1 . -HUMAN SOX13_HUMAN.H11MO.0.D hocomoco 1 SOX13 . -HUMAN ELF2_HUMAN.H11MO.0.C hocomoco 1 ELF2 . -HUMAN SP3_HUMAN.H11MO.0.B hocomoco 1 SP3 . -HUMAN ETV5_HUMAN.H11MO.0.C hocomoco 1 ETV5 . -HUMAN TEAD1_HUMAN.H11MO.0.A hocomoco 1 TEAD1 . -HUMAN TGIF2_HUMAN.H11MO.0.D hocomoco 1 TGIF2 . -HUMAN ELK3_HUMAN.H11MO.0.D hocomoco 1 ELK3 . -HUMAN BATF_HUMAN.H11MO.0.A hocomoco 1 BATF . -HUMAN FOXQ1_HUMAN.H11MO.0.C hocomoco 1 FOXQ1 . -HUMAN Z324A_HUMAN.H11MO.0.C hocomoco 1 Z324A . -HUMAN PO2F2_HUMAN.H11MO.0.A hocomoco 1 PO2F2 . -HUMAN INSM1_HUMAN.H11MO.0.C hocomoco 1 INSM1 . -HUMAN BATF_HUMAN.H11MO.1.A hocomoco 1 BATF . -HUMAN ZSC22_HUMAN.H11MO.0.C hocomoco 1 ZSC22 . -HUMAN MEIS1_HUMAN.H11MO.1.B hocomoco 1 MEIS1 . -HUMAN BRAC_HUMAN.H11MO.1.B hocomoco 1 BRAC . -HUMAN NR6A1_HUMAN.H11MO.0.B hocomoco 1 NR6A1 . -HUMAN ZN423_HUMAN.H11MO.0.D hocomoco 1 ZN423 . -HUMAN TFEB_HUMAN.H11MO.0.C hocomoco 1 TFEB . -HUMAN NF2L2_HUMAN.H11MO.0.A hocomoco 1 NF2L2 . -HUMAN SCRT2_HUMAN.H11MO.0.D hocomoco 1 SCRT2 . -HUMAN ZN341_HUMAN.H11MO.0.C hocomoco 1 ZN341 . -HUMAN ATF6A_HUMAN.H11MO.0.B hocomoco 1 ATF6A . -HUMAN HLF_HUMAN.H11MO.0.C hocomoco 1 HLF . -HUMAN ZN816_HUMAN.H11MO.1.C hocomoco 1 ZN816 . -HUMAN MEOX2_HUMAN.H11MO.0.D hocomoco 1 MEOX2 . -HUMAN HIF1A_HUMAN.H11MO.0.C hocomoco 1 HIF1A . -HUMAN MEIS2_HUMAN.H11MO.0.B hocomoco 1 MEIS2 . -HUMAN ARI5B_HUMAN.H11MO.0.C hocomoco 1 ARI5B . -HUMAN ZBTB6_HUMAN.H11MO.0.C hocomoco 1 ZBTB6 . -HUMAN KLF3_HUMAN.H11MO.0.B hocomoco 1 KLF3 . -HUMAN NR2C1_HUMAN.H11MO.0.C hocomoco 1 NR2C1 . -HUMAN FOXP3_HUMAN.H11MO.0.D hocomoco 1 FOXP3 . -HUMAN ANDR_HUMAN.H11MO.2.A hocomoco 1 ANDR . -HUMAN NFYC_HUMAN.H11MO.0.A hocomoco 1 NFYC . -HUMAN ZN528_HUMAN.H11MO.0.C hocomoco 1 ZN528 . -HUMAN ZN322_HUMAN.H11MO.0.B hocomoco 1 ZN322 . -HUMAN E4F1_HUMAN.H11MO.0.D hocomoco 1 E4F1 . -HUMAN BRCA1_HUMAN.H11MO.0.D hocomoco 1 BRCA1 . -HUMAN GSX2_HUMAN.H11MO.0.D hocomoco 1 GSX2 . -HUMAN SPIC_HUMAN.H11MO.0.D hocomoco 1 SPIC . -HUMAN ZBT7A_HUMAN.H11MO.0.A hocomoco 1 ZBT7A . -HUMAN HBP1_HUMAN.H11MO.0.D hocomoco 1 HBP1 . -HUMAN TBX4_HUMAN.H11MO.0.D hocomoco 1 TBX4 . -HUMAN FOXM1_HUMAN.H11MO.0.A hocomoco 1 FOXM1 . -HUMAN NR0B1_HUMAN.H11MO.0.D hocomoco 1 NR0B1 . -HUMAN BHE22_HUMAN.H11MO.0.D hocomoco 1 BHE22 . -HUMAN NFIB_HUMAN.H11MO.0.D hocomoco 1 NFIB . -HUMAN CRX_HUMAN.H11MO.0.B hocomoco 1 CRX . -HUMAN ZN274_HUMAN.H11MO.0.A hocomoco 1 ZN274 . -HUMAN PO3F1_HUMAN.H11MO.0.C hocomoco 1 PO3F1 . -HUMAN HXD9_HUMAN.H11MO.0.D hocomoco 1 HXD9 . -HUMAN VEZF1_HUMAN.H11MO.1.C hocomoco 1 VEZF1 . -HUMAN RELB_HUMAN.H11MO.0.C hocomoco 1 RELB . -HUMAN NR2E1_HUMAN.H11MO.0.D hocomoco 1 NR2E1 . -HUMAN HXA7_HUMAN.H11MO.0.D hocomoco 1 HXA7 . -HUMAN FOXD3_HUMAN.H11MO.0.D hocomoco 1 FOXD3 . -HUMAN ZIC3_HUMAN.H11MO.0.B hocomoco 1 ZIC3 . -HUMAN HES1_HUMAN.H11MO.0.D hocomoco 1 HES1 . -HUMAN ZBT17_HUMAN.H11MO.0.A hocomoco 1 ZBT17 . -HUMAN NFIC_HUMAN.H11MO.1.A hocomoco 1 NFIC . -HUMAN MEIS1_HUMAN.H11MO.0.A hocomoco 1 MEIS1 . -HUMAN ZN589_HUMAN.H11MO.0.D hocomoco 1 ZN589 . -HUMAN FOXK1_HUMAN.H11MO.0.A hocomoco 1 FOXK1 . -HUMAN ELK4_HUMAN.H11MO.0.A hocomoco 1 ELK4 . -HUMAN PTF1A_HUMAN.H11MO.1.B hocomoco 1 PTF1A . -HUMAN TYY2_HUMAN.H11MO.0.D hocomoco 1 TYY2 . -HUMAN E2F1_HUMAN.H11MO.0.A hocomoco 1 E2F1 . -HUMAN HSF1_HUMAN.H11MO.1.A hocomoco 1 HSF1 . -HUMAN TBX5_HUMAN.H11MO.0.D hocomoco 1 TBX5 . -HUMAN FEV_HUMAN.H11MO.0.B hocomoco 1 FEV . -HUMAN MEF2C_HUMAN.H11MO.0.A hocomoco 1 MEF2C . -HUMAN MAX_HUMAN.H11MO.0.A hocomoco 1 MAX . -HUMAN IRF3_HUMAN.H11MO.0.B hocomoco 1 IRF3 . -HUMAN NR1D1_HUMAN.H11MO.1.D hocomoco 1 NR1D1 . -HUMAN SPI1_HUMAN.H11MO.0.A hocomoco 1 SPI1 . -HUMAN PO3F2_HUMAN.H11MO.0.A hocomoco 1 PO3F2 . -HUMAN CR3L1_HUMAN.H11MO.0.D hocomoco 1 CR3L1 . -HUMAN SOX17_HUMAN.H11MO.0.C hocomoco 1 SOX17 . -HUMAN MEIS3_HUMAN.H11MO.0.D hocomoco 1 MEIS3 . -HUMAN ZN652_HUMAN.H11MO.0.D hocomoco 1 ZN652 . -HUMAN HXD10_HUMAN.H11MO.0.D hocomoco 1 HXD10 . -HUMAN PEBB_HUMAN.H11MO.0.C hocomoco 1 PEBB . -HUMAN NFYA_HUMAN.H11MO.0.A hocomoco 1 NFYA . -HUMAN HINFP_HUMAN.H11MO.0.C hocomoco 1 HINFP . -HUMAN IRF4_HUMAN.H11MO.0.A hocomoco 1 IRF4 . -HUMAN P63_HUMAN.H11MO.1.A hocomoco 1 P63 . -HUMAN CREB1_HUMAN.H11MO.0.A hocomoco 1 CREB1 . -HUMAN MIXL1_HUMAN.H11MO.0.D hocomoco 1 MIXL1 . -HUMAN DPRX_HUMAN.H11MO.0.D hocomoco 1 DPRX . -HUMAN HES5_HUMAN.H11MO.0.D hocomoco 1 HES5 . -HUMAN PROP1_HUMAN.H11MO.0.D hocomoco 1 PROP1 . -HUMAN OLIG1_HUMAN.H11MO.0.D hocomoco 1 OLIG1 . -HUMAN CUX1_HUMAN.H11MO.0.C hocomoco 1 CUX1 . -HUMAN SOX18_HUMAN.H11MO.0.D hocomoco 1 SOX18 . -HUMAN MYF6_HUMAN.H11MO.0.C hocomoco 1 MYF6 . -HUMAN LHX6_HUMAN.H11MO.0.D hocomoco 1 LHX6 . -HUMAN VSX1_HUMAN.H11MO.0.D hocomoco 1 VSX1 . -HUMAN ATF4_HUMAN.H11MO.0.A hocomoco 1 ATF4 . -HUMAN FOSL1_HUMAN.H11MO.0.A hocomoco 1 FOSL1 . -HUMAN NR1I2_HUMAN.H11MO.0.C hocomoco 1 NR1I2 . -HUMAN JUN_HUMAN.H11MO.0.A hocomoco 1 JUN . -HUMAN HNF1A_HUMAN.H11MO.0.C hocomoco 1 HNF1A . -HUMAN REL_HUMAN.H11MO.0.B hocomoco 1 REL . -HUMAN MYOD1_HUMAN.H11MO.0.A hocomoco 1 MYOD1 . -HUMAN HEY2_HUMAN.H11MO.0.D hocomoco 1 HEY2 . -HUMAN TBX15_HUMAN.H11MO.0.D hocomoco 1 TBX15 . -HUMAN ALX4_HUMAN.H11MO.0.D hocomoco 1 ALX4 . -HUMAN P73_HUMAN.H11MO.0.A hocomoco 1 P73 . -HUMAN ZEP2_HUMAN.H11MO.0.D hocomoco 1 ZEP2 . -HUMAN RHXF1_HUMAN.H11MO.0.D hocomoco 1 RHXF1 . -HUMAN EGR2_HUMAN.H11MO.1.A hocomoco 1 EGR2 . -HUMAN ZN418_HUMAN.H11MO.0.C hocomoco 1 ZN418 . -HUMAN PKNX1_HUMAN.H11MO.0.B hocomoco 1 PKNX1 . -HUMAN ZN563_HUMAN.H11MO.0.C hocomoco 1 ZN563 . -HUMAN MAFF_HUMAN.H11MO.1.B hocomoco 1 MAFF . -HUMAN ELK1_HUMAN.H11MO.0.B hocomoco 1 ELK1 . -HUMAN KAISO_HUMAN.H11MO.1.A hocomoco 1 KAISO . -HUMAN OLIG2_HUMAN.H11MO.0.B hocomoco 1 OLIG2 . -HUMAN UBIP1_HUMAN.H11MO.0.D hocomoco 1 UBIP1 . -HUMAN HIC2_HUMAN.H11MO.0.D hocomoco 1 HIC2 . -HUMAN CLOCK_HUMAN.H11MO.0.C hocomoco 1 CLOCK . -HUMAN PBX3_HUMAN.H11MO.0.A hocomoco 1 PBX3 . -HUMAN FLI1_HUMAN.H11MO.0.A hocomoco 1 FLI1 . -HUMAN KLF16_HUMAN.H11MO.0.D hocomoco 1 KLF16 . -HUMAN HXA5_HUMAN.H11MO.0.D hocomoco 1 HXA5 . -HUMAN KLF1_HUMAN.H11MO.0.A hocomoco 1 KLF1 . -HUMAN COT1_HUMAN.H11MO.0.C hocomoco 1 COT1 . -HUMAN OTX2_HUMAN.H11MO.0.A hocomoco 1 OTX2 . +AHR AHR_HUMAN.H11MO.0.B hocomoco 1 AHR PAS domain factors P35869 Integrative +AIRE AIRE_HUMAN.H11MO.0.C hocomoco 1 AIRE AIRE O43918 Integrative +ALX1 ALX1_HUMAN.H11MO.0.B hocomoco 1 ALX1 Paired-related HD factors Q15699 Integrative +ALX3 ALX3_HUMAN.H11MO.0.D hocomoco 1 ALX3 Paired-related HD factors O95076 HT-SELEX +ALX4 ALX4_HUMAN.H11MO.0.D hocomoco 1 ALX4 Paired-related HD factors Q9H161 HT-SELEX +ANDR ANDR_HUMAN.H11MO.1.A hocomoco 1 AR Steroid hormone receptors (NR3) P10275 ChIP-Seq +ANDR ANDR_HUMAN.H11MO.0.A hocomoco 1 AR Steroid hormone receptors (NR3) P10275 ChIP-Seq +ANDR ANDR_HUMAN.H11MO.2.A hocomoco 1 AR Steroid hormone receptors (NR3) P10275 ChIP-Seq +AP2A AP2A_HUMAN.H11MO.0.A hocomoco 1 TFAP2A AP-2 P05549 ChIP-Seq +AP2B AP2B_HUMAN.H11MO.0.B hocomoco 1 TFAP2B AP-2 Q92481 Integrative +AP2C AP2C_HUMAN.H11MO.0.A hocomoco 1 TFAP2C AP-2 Q92754 ChIP-Seq +AP2D AP2D_HUMAN.H11MO.0.D hocomoco 1 TFAP2D AP-2 Q7Z6R9 Integrative +ARI3A ARI3A_HUMAN.H11MO.0.D hocomoco 1 ARID3A ARID-related factors Q99856 Integrative +ARI5B ARI5B_HUMAN.H11MO.0.C hocomoco 1 ARID5B ARID-related factors Q14865 Integrative +ARNT ARNT_HUMAN.H11MO.0.B hocomoco 1 ARNT PAS domain factors P27540 ChIP-Seq +ARNT2 ARNT2_HUMAN.H11MO.0.D hocomoco 1 ARNT2 PAS domain factors Q9HBZ2 Integrative +ARX ARX_HUMAN.H11MO.0.D hocomoco 1 ARX Paired-related HD factors Q96QS3 HT-SELEX +ASCL1 ASCL1_HUMAN.H11MO.0.A hocomoco 1 ASCL1 MyoD / ASC-related factors P50553 ChIP-Seq +ASCL2 ASCL2_HUMAN.H11MO.0.D hocomoco 1 ASCL2 MyoD / ASC-related factors Q99929 ChIP-Seq +ATF1 ATF1_HUMAN.H11MO.0.B hocomoco 1 ATF1 CREB-related factors P18846 ChIP-Seq +ATF2 ATF2_HUMAN.H11MO.2.C hocomoco 1 ATF2 Jun-related factors P15336 ChIP-Seq +ATF2 ATF2_HUMAN.H11MO.0.B hocomoco 1 ATF2 Jun-related factors P15336 ChIP-Seq +ATF2 ATF2_HUMAN.H11MO.1.B hocomoco 1 ATF2 Jun-related factors P15336 ChIP-Seq +ATF3 ATF3_HUMAN.H11MO.0.A hocomoco 1 ATF3 Fos-related factors P18847 ChIP-Seq +ATF4 ATF4_HUMAN.H11MO.0.A hocomoco 1 ATF4 ATF-4-related factors P18848 ChIP-Seq +ATF6A ATF6A_HUMAN.H11MO.0.B hocomoco 1 ATF6 CREB-related factors P18850 Integrative +ATF7 ATF7_HUMAN.H11MO.0.D hocomoco 1 ATF7 Jun-related factors P17544 HT-SELEX +ATOH1 ATOH1_HUMAN.H11MO.0.B hocomoco 1 ATOH1 Tal-related factors Q92858 ChIP-Seq +BACH1 BACH1_HUMAN.H11MO.0.A hocomoco 1 BACH1 Jun-related factors O14867 ChIP-Seq +BACH2 BACH2_HUMAN.H11MO.0.A hocomoco 1 BACH2 Jun-related factors Q9BYV9 ChIP-Seq +BARH1 BARH1_HUMAN.H11MO.0.D hocomoco 1 BARHL1 NK-related factors Q9BZE3 HT-SELEX +BARH2 BARH2_HUMAN.H11MO.0.D hocomoco 1 BARHL2 NK-related factors Q9NY43 HT-SELEX +BARX1 BARX1_HUMAN.H11MO.0.D hocomoco 1 BARX1 NK-related factors Q9HBU1 ChIP-Seq +BARX2 BARX2_HUMAN.H11MO.0.D hocomoco 1 BARX2 NK-related factors Q9UMQ3 Integrative +BATF BATF_HUMAN.H11MO.1.A hocomoco 1 BATF B-ATF-related factors Q16520 ChIP-Seq +BATF BATF_HUMAN.H11MO.0.A hocomoco 1 BATF B-ATF-related factors Q16520 ChIP-Seq +BATF3 BATF3_HUMAN.H11MO.0.B hocomoco 1 BATF3 B-ATF-related factors Q9NR55 ChIP-Seq +BC11A BC11A_HUMAN.H11MO.0.A hocomoco 1 BCL11A Factors with multiple dispersed zinc fingers Q9H165 ChIP-Seq +BCL6 BCL6_HUMAN.H11MO.0.A hocomoco 1 BCL6 More than 3 adjacent zinc finger factors P41182 ChIP-Seq +BCL6B BCL6B_HUMAN.H11MO.0.D hocomoco 1 BCL6B More than 3 adjacent zinc finger factors Q8N143 HT-SELEX +BHA15 BHA15_HUMAN.H11MO.0.B hocomoco 1 BHLHA15 Tal-related factors Q7RTS1 ChIP-Seq +BHE22 BHE22_HUMAN.H11MO.0.D hocomoco 1 BHLHE22 Tal-related factors Q8NFJ8 HT-SELEX +BHE23 BHE23_HUMAN.H11MO.0.D hocomoco 1 BHLHE23 Tal-related factors Q8NDY6 HT-SELEX +BHE40 BHE40_HUMAN.H11MO.0.A hocomoco 1 BHLHE40 Hairy-related factors O14503 ChIP-Seq +BHE41 BHE41_HUMAN.H11MO.0.D hocomoco 1 BHLHE41 Hairy-related factors Q9C0J9 Integrative +BMAL1 BMAL1_HUMAN.H11MO.0.A hocomoco 1 ARNTL PAS domain factors O00327 ChIP-Seq +BPTF BPTF_HUMAN.H11MO.0.D hocomoco 1 BPTF . Q12830 Integrative +BRAC BRAC_HUMAN.H11MO.0.A hocomoco 1 T Brachyury-related factors O15178 ChIP-Seq +BRAC BRAC_HUMAN.H11MO.1.B hocomoco 1 T Brachyury-related factors O15178 ChIP-Seq +BRCA1 BRCA1_HUMAN.H11MO.0.D hocomoco 1 BRCA1 . P38398 Integrative +BSH BSH_HUMAN.H11MO.0.D hocomoco 1 BSX NK-related factors Q3C1V8 HT-SELEX +CDC5L CDC5L_HUMAN.H11MO.0.D hocomoco 1 CDC5L Myb/SANT domain factors Q99459 Integrative +CDX1 CDX1_HUMAN.H11MO.0.C hocomoco 1 CDX1 HOX-related factors P47902 Integrative +CDX2 CDX2_HUMAN.H11MO.0.A hocomoco 1 CDX2 HOX-related factors Q99626 ChIP-Seq +CEBPA CEBPA_HUMAN.H11MO.0.A hocomoco 1 CEBPA C/EBP-related P49715 ChIP-Seq +CEBPB CEBPB_HUMAN.H11MO.0.A hocomoco 1 CEBPB C/EBP-related P17676 ChIP-Seq +CEBPD CEBPD_HUMAN.H11MO.0.C hocomoco 1 CEBPD C/EBP-related P49716 ChIP-Seq +CEBPE CEBPE_HUMAN.H11MO.0.A hocomoco 1 CEBPE C/EBP-related Q15744 Integrative +CEBPG CEBPG_HUMAN.H11MO.0.B hocomoco 1 CEBPG C/EBP-related P53567 ChIP-Seq +CEBPZ CEBPZ_HUMAN.H11MO.0.D hocomoco 1 CEBPZ . Q03701 Integrative +CENPB CENPB_HUMAN.H11MO.0.D hocomoco 1 CENPB . P07199 HT-SELEX +CLOCK CLOCK_HUMAN.H11MO.0.C hocomoco 1 CLOCK PAS domain factors O15516 ChIP-Seq +COE1 COE1_HUMAN.H11MO.0.A hocomoco 1 EBF1 Early B-Cell Factor-related factors Q9UH73 ChIP-Seq +COT1 COT1_HUMAN.H11MO.0.C hocomoco 1 NR2F1 RXR-related receptors (NR2) P10589 ChIP-Seq +COT1 COT1_HUMAN.H11MO.1.C hocomoco 1 NR2F1 RXR-related receptors (NR2) P10589 ChIP-Seq +COT2 COT2_HUMAN.H11MO.1.A hocomoco 1 NR2F2 RXR-related receptors (NR2) P24468 ChIP-Seq +COT2 COT2_HUMAN.H11MO.0.A hocomoco 1 NR2F2 RXR-related receptors (NR2) P24468 ChIP-Seq +CPEB1 CPEB1_HUMAN.H11MO.0.D hocomoco 1 CPEB1 . Q9BZB8 HT-SELEX +CR3L1 CR3L1_HUMAN.H11MO.0.D hocomoco 1 CREB3L1 CREB-related factors Q96BA8 HT-SELEX +CR3L2 CR3L2_HUMAN.H11MO.0.D hocomoco 1 CREB3L2 CREB-related factors Q70SY1 HT-SELEX +CREB1 CREB1_HUMAN.H11MO.0.A hocomoco 1 CREB1 CREB-related factors P16220 ChIP-Seq +CREB3 CREB3_HUMAN.H11MO.0.D hocomoco 1 CREB3 CREB-related factors O43889 HT-SELEX +CREB5 CREB5_HUMAN.H11MO.0.D hocomoco 1 CREB5 Jun-related factors Q02930 HT-SELEX +CREM CREM_HUMAN.H11MO.0.C hocomoco 1 CREM CREB-related factors Q03060 Integrative +CRX CRX_HUMAN.H11MO.0.B hocomoco 1 CRX Paired-related HD factors O43186 ChIP-Seq +CTCF CTCF_HUMAN.H11MO.0.A hocomoco 1 CTCF More than 3 adjacent zinc finger factors P49711 ChIP-Seq +CTCFL CTCFL_HUMAN.H11MO.0.A hocomoco 1 CTCFL More than 3 adjacent zinc finger factors Q8NI51 ChIP-Seq +CUX1 CUX1_HUMAN.H11MO.0.C hocomoco 1 CUX1 HD-CUT factors P39880 Integrative +CUX2 CUX2_HUMAN.H11MO.0.D hocomoco 1 CUX2 HD-CUT factors O14529 ChIP-Seq +CXXC1 CXXC1_HUMAN.H11MO.0.D hocomoco 1 CXXC1 CpG-binding proteins Q9P0U4 Integrative +DBP DBP_HUMAN.H11MO.0.B hocomoco 1 DBP C/EBP-related Q10586 Integrative +DDIT3 DDIT3_HUMAN.H11MO.0.D hocomoco 1 DDIT3 C/EBP-related P35638 ChIP-Seq +DLX1 DLX1_HUMAN.H11MO.0.D hocomoco 1 DLX1 NK-related factors P56177 HT-SELEX +DLX2 DLX2_HUMAN.H11MO.0.D hocomoco 1 DLX2 NK-related factors Q07687 Integrative +DLX3 DLX3_HUMAN.H11MO.0.C hocomoco 1 DLX3 NK-related factors O60479 Integrative +DLX4 DLX4_HUMAN.H11MO.0.D hocomoco 1 DLX4 NK-related factors Q92988 HT-SELEX +DLX5 DLX5_HUMAN.H11MO.0.D hocomoco 1 DLX5 NK-related factors P56178 ChIP-Seq +DLX6 DLX6_HUMAN.H11MO.0.D hocomoco 1 DLX6 NK-related factors P56179 HT-SELEX +DMBX1 DMBX1_HUMAN.H11MO.0.D hocomoco 1 DMBX1 Paired-related HD factors Q8NFW5 HT-SELEX +DMRT1 DMRT1_HUMAN.H11MO.0.D hocomoco 1 DMRT1 DMRT Q9Y5R6 ChIP-Seq +DPRX DPRX_HUMAN.H11MO.0.D hocomoco 1 DPRX Paired-related HD factors A6NFQ7 HT-SELEX +DRGX DRGX_HUMAN.H11MO.0.D hocomoco 1 DRGX Paired-related HD factors A6NNA5 HT-SELEX +DUX4 DUX4_HUMAN.H11MO.0.A hocomoco 1 DUX4 Paired-related HD factors Q9UBX2 ChIP-Seq +DUXA DUXA_HUMAN.H11MO.0.D hocomoco 1 DUXA Paired-related HD factors A6NLW8 HT-SELEX +E2F1 E2F1_HUMAN.H11MO.0.A hocomoco 1 E2F1 E2F-related factors Q01094 ChIP-Seq +E2F2 E2F2_HUMAN.H11MO.0.B hocomoco 1 E2F2 E2F-related factors Q14209 Integrative +E2F3 E2F3_HUMAN.H11MO.0.A hocomoco 1 E2F3 E2F-related factors O00716 ChIP-Seq +E2F4 E2F4_HUMAN.H11MO.1.A hocomoco 1 E2F4 E2F-related factors Q16254 ChIP-Seq +E2F4 E2F4_HUMAN.H11MO.0.A hocomoco 1 E2F4 E2F-related factors Q16254 ChIP-Seq +E2F5 E2F5_HUMAN.H11MO.0.B hocomoco 1 E2F5 E2F-related factors Q15329 Integrative +E2F6 E2F6_HUMAN.H11MO.0.A hocomoco 1 E2F6 E2F-related factors O75461 ChIP-Seq +E2F7 E2F7_HUMAN.H11MO.0.B hocomoco 1 E2F7 E2F-related factors Q96AV8 ChIP-Seq +E2F8 E2F8_HUMAN.H11MO.0.D hocomoco 1 E2F8 E2F-related factors A0AVK6 HT-SELEX +E4F1 E4F1_HUMAN.H11MO.0.D hocomoco 1 E4F1 Factors with multiple dispersed zinc fingers Q66K89 Integrative +EGR1 EGR1_HUMAN.H11MO.0.A hocomoco 1 EGR1 Three-zinc finger Krüppel-related factors P18146 ChIP-Seq +EGR2 EGR2_HUMAN.H11MO.0.A hocomoco 1 EGR2 Three-zinc finger Krüppel-related factors P11161 ChIP-Seq +EGR2 EGR2_HUMAN.H11MO.1.A hocomoco 1 EGR2 Three-zinc finger Krüppel-related factors P11161 ChIP-Seq +EGR3 EGR3_HUMAN.H11MO.0.D hocomoco 1 EGR3 Three-zinc finger Krüppel-related factors Q06889 Integrative +EGR4 EGR4_HUMAN.H11MO.0.D hocomoco 1 EGR4 Three-zinc finger Krüppel-related factors Q05215 Integrative +EHF EHF_HUMAN.H11MO.0.B hocomoco 1 EHF Ets-related factors Q9NZC4 ChIP-Seq +ELF1 ELF1_HUMAN.H11MO.0.A hocomoco 1 ELF1 Ets-related factors P32519 ChIP-Seq +ELF2 ELF2_HUMAN.H11MO.0.C hocomoco 1 ELF2 Ets-related factors Q15723 ChIP-Seq +ELF3 ELF3_HUMAN.H11MO.0.A hocomoco 1 ELF3 Ets-related factors P78545 ChIP-Seq +ELF5 ELF5_HUMAN.H11MO.0.A hocomoco 1 ELF5 Ets-related factors Q9UKW6 ChIP-Seq +ELK1 ELK1_HUMAN.H11MO.0.B hocomoco 1 ELK1 Ets-related factors P19419 ChIP-Seq +ELK3 ELK3_HUMAN.H11MO.0.D hocomoco 1 ELK3 Ets-related factors P41970 Integrative +ELK4 ELK4_HUMAN.H11MO.0.A hocomoco 1 ELK4 Ets-related factors P28324 ChIP-Seq +EMX1 EMX1_HUMAN.H11MO.0.D hocomoco 1 EMX1 NK-related factors Q04741 HT-SELEX +EMX2 EMX2_HUMAN.H11MO.0.D hocomoco 1 EMX2 NK-related factors Q04743 HT-SELEX +EOMES EOMES_HUMAN.H11MO.0.D hocomoco 1 EOMES TBrain-related factors O95936 ChIP-Seq +EPAS1 EPAS1_HUMAN.H11MO.0.B hocomoco 1 EPAS1 PAS domain factors Q99814 ChIP-Seq +ERG ERG_HUMAN.H11MO.0.A hocomoco 1 ERG Ets-related factors P11308 ChIP-Seq +ERR1 ERR1_HUMAN.H11MO.0.A hocomoco 1 ESRRA Steroid hormone receptors (NR3) P11474 ChIP-Seq +ERR2 ERR2_HUMAN.H11MO.0.A hocomoco 1 ESRRB Steroid hormone receptors (NR3) O95718 Integrative +ERR3 ERR3_HUMAN.H11MO.0.B hocomoco 1 ESRRG Steroid hormone receptors (NR3) P62508 Integrative +ESR1 ESR1_HUMAN.H11MO.1.A hocomoco 1 ESR1 Steroid hormone receptors (NR3) P03372 ChIP-Seq +ESR1 ESR1_HUMAN.H11MO.0.A hocomoco 1 ESR1 Steroid hormone receptors (NR3) P03372 ChIP-Seq +ESR2 ESR2_HUMAN.H11MO.1.A hocomoco 1 ESR2 Steroid hormone receptors (NR3) Q92731 ChIP-Seq +ESR2 ESR2_HUMAN.H11MO.0.A hocomoco 1 ESR2 Steroid hormone receptors (NR3) Q92731 ChIP-Seq +ESX1 ESX1_HUMAN.H11MO.0.D hocomoco 1 ESX1 Paired-related HD factors Q8N693 HT-SELEX +ETS1 ETS1_HUMAN.H11MO.0.A hocomoco 1 ETS1 Ets-related factors P14921 ChIP-Seq +ETS2 ETS2_HUMAN.H11MO.0.B hocomoco 1 ETS2 Ets-related factors P15036 ChIP-Seq +ETV1 ETV1_HUMAN.H11MO.0.A hocomoco 1 ETV1 Ets-related factors P50549 ChIP-Seq +ETV2 ETV2_HUMAN.H11MO.0.B hocomoco 1 ETV2 Ets-related factors O00321 ChIP-Seq +ETV3 ETV3_HUMAN.H11MO.0.D hocomoco 1 ETV3 Ets-related factors P41162 HT-SELEX +ETV4 ETV4_HUMAN.H11MO.0.B hocomoco 1 ETV4 Ets-related factors P43268 ChIP-Seq +ETV5 ETV5_HUMAN.H11MO.0.C hocomoco 1 ETV5 Ets-related factors P41161 ChIP-Seq +ETV6 ETV6_HUMAN.H11MO.0.D hocomoco 1 ETV6 Ets-related factors P41212 ChIP-Seq +ETV7 ETV7_HUMAN.H11MO.0.D hocomoco 1 ETV7 Ets-related factors Q9Y603 ChIP-Seq +EVI1 EVI1_HUMAN.H11MO.0.B hocomoco 1 MECOM Factors with multiple dispersed zinc fingers Q03112 Integrative +EVX1 EVX1_HUMAN.H11MO.0.D hocomoco 1 EVX1 HOX-related factors P49640 ChIP-Seq +EVX2 EVX2_HUMAN.H11MO.0.A hocomoco 1 EVX2 HOX-related factors Q03828 ChIP-Seq +FEV FEV_HUMAN.H11MO.0.B hocomoco 1 FEV Ets-related factors Q99581 ChIP-Seq +FEZF1 FEZF1_HUMAN.H11MO.0.C hocomoco 1 FEZF1 More than 3 adjacent zinc finger factors A0PJY2 ChIP-Seq +FIGLA FIGLA_HUMAN.H11MO.0.D hocomoco 1 FIGLA Tal-related factors Q6QHK4 HT-SELEX +FLI1 FLI1_HUMAN.H11MO.0.A hocomoco 1 FLI1 Ets-related factors Q01543 ChIP-Seq +FLI1 FLI1_HUMAN.H11MO.1.A hocomoco 1 FLI1 Ets-related factors Q01543 ChIP-Seq +FOS FOS_HUMAN.H11MO.0.A hocomoco 1 FOS Fos-related factors P01100 ChIP-Seq +FOSB FOSB_HUMAN.H11MO.0.A hocomoco 1 FOSB Fos-related factors P53539 ChIP-Seq +FOSL1 FOSL1_HUMAN.H11MO.0.A hocomoco 1 FOSL1 Fos-related factors P15407 ChIP-Seq +FOSL2 FOSL2_HUMAN.H11MO.0.A hocomoco 1 FOSL2 Fos-related factors P15408 ChIP-Seq +FOXA1 FOXA1_HUMAN.H11MO.0.A hocomoco 1 FOXA1 Forkhead box (FOX) factors P55317 ChIP-Seq +FOXA2 FOXA2_HUMAN.H11MO.0.A hocomoco 1 FOXA2 Forkhead box (FOX) factors Q9Y261 ChIP-Seq +FOXA3 FOXA3_HUMAN.H11MO.0.B hocomoco 1 FOXA3 Forkhead box (FOX) factors P55318 ChIP-Seq +FOXB1 FOXB1_HUMAN.H11MO.0.D hocomoco 1 FOXB1 Forkhead box (FOX) factors Q99853 HT-SELEX +FOXC1 FOXC1_HUMAN.H11MO.0.C hocomoco 1 FOXC1 Forkhead box (FOX) factors Q12948 Integrative +FOXC2 FOXC2_HUMAN.H11MO.0.D hocomoco 1 FOXC2 Forkhead box (FOX) factors Q99958 Integrative +FOXD1 FOXD1_HUMAN.H11MO.0.D hocomoco 1 FOXD1 Forkhead box (FOX) factors Q16676 Integrative +FOXD2 FOXD2_HUMAN.H11MO.0.D hocomoco 1 FOXD2 Forkhead box (FOX) factors O60548 HT-SELEX +FOXD3 FOXD3_HUMAN.H11MO.0.D hocomoco 1 FOXD3 Forkhead box (FOX) factors Q9UJU5 ChIP-Seq +FOXF1 FOXF1_HUMAN.H11MO.0.D hocomoco 1 FOXF1 Forkhead box (FOX) factors Q12946 Integrative +FOXF2 FOXF2_HUMAN.H11MO.0.D hocomoco 1 FOXF2 Forkhead box (FOX) factors Q12947 Integrative +FOXG1 FOXG1_HUMAN.H11MO.0.D hocomoco 1 FOXG1 Forkhead box (FOX) factors P55316 HT-SELEX +FOXH1 FOXH1_HUMAN.H11MO.0.A hocomoco 1 FOXH1 Forkhead box (FOX) factors O75593 ChIP-Seq +FOXI1 FOXI1_HUMAN.H11MO.0.B hocomoco 1 FOXI1 Forkhead box (FOX) factors Q12951 Integrative +FOXJ2 FOXJ2_HUMAN.H11MO.0.C hocomoco 1 FOXJ2 Forkhead box (FOX) factors Q9P0K8 Integrative +FOXJ3 FOXJ3_HUMAN.H11MO.0.A hocomoco 1 FOXJ3 Forkhead box (FOX) factors Q9UPW0 Integrative +FOXJ3 FOXJ3_HUMAN.H11MO.1.B hocomoco 1 FOXJ3 Forkhead box (FOX) factors Q9UPW0 Integrative +FOXK1 FOXK1_HUMAN.H11MO.0.A hocomoco 1 FOXK1 Forkhead box (FOX) factors P85037 ChIP-Seq +FOXL1 FOXL1_HUMAN.H11MO.0.D hocomoco 1 FOXL1 Forkhead box (FOX) factors Q12952 HT-SELEX +FOXM1 FOXM1_HUMAN.H11MO.0.A hocomoco 1 FOXM1 Forkhead box (FOX) factors Q08050 ChIP-Seq +FOXO1 FOXO1_HUMAN.H11MO.0.A hocomoco 1 FOXO1 Forkhead box (FOX) factors Q12778 ChIP-Seq +FOXO3 FOXO3_HUMAN.H11MO.0.B hocomoco 1 FOXO3 Forkhead box (FOX) factors O43524 ChIP-Seq +FOXO4 FOXO4_HUMAN.H11MO.0.C hocomoco 1 FOXO4 Forkhead box (FOX) factors P98177 Integrative +FOXO6 FOXO6_HUMAN.H11MO.0.D hocomoco 1 FOXO6 Forkhead box (FOX) factors A8MYZ6 HT-SELEX +FOXP1 FOXP1_HUMAN.H11MO.0.A hocomoco 1 FOXP1 Forkhead box (FOX) factors Q9H334 ChIP-Seq +FOXP2 FOXP2_HUMAN.H11MO.0.C hocomoco 1 FOXP2 Forkhead box (FOX) factors O15409 ChIP-Seq +FOXP3 FOXP3_HUMAN.H11MO.0.D hocomoco 1 FOXP3 Forkhead box (FOX) factors Q9BZS1 Integrative +FOXQ1 FOXQ1_HUMAN.H11MO.0.C hocomoco 1 FOXQ1 Forkhead box (FOX) factors Q9C009 Integrative +FUBP1 FUBP1_HUMAN.H11MO.0.D hocomoco 1 FUBP1 . Q96AE4 Integrative +GABPA GABPA_HUMAN.H11MO.0.A hocomoco 1 GABPA Ets-related factors Q06546 ChIP-Seq +GATA1 GATA1_HUMAN.H11MO.1.A hocomoco 1 GATA1 GATA-type zinc fingers P15976 ChIP-Seq +GATA1 GATA1_HUMAN.H11MO.0.A hocomoco 1 GATA1 GATA-type zinc fingers P15976 ChIP-Seq +GATA2 GATA2_HUMAN.H11MO.0.A hocomoco 1 GATA2 GATA-type zinc fingers P23769 ChIP-Seq +GATA2 GATA2_HUMAN.H11MO.1.A hocomoco 1 GATA2 GATA-type zinc fingers P23769 ChIP-Seq +GATA3 GATA3_HUMAN.H11MO.0.A hocomoco 1 GATA3 GATA-type zinc fingers P23771 ChIP-Seq +GATA4 GATA4_HUMAN.H11MO.0.A hocomoco 1 GATA4 GATA-type zinc fingers P43694 ChIP-Seq +GATA5 GATA5_HUMAN.H11MO.0.D hocomoco 1 GATA5 GATA-type zinc fingers Q9BWX5 Integrative +GATA6 GATA6_HUMAN.H11MO.0.A hocomoco 1 GATA6 GATA-type zinc fingers Q92908 ChIP-Seq +GBX1 GBX1_HUMAN.H11MO.0.D hocomoco 1 GBX1 HOX-related factors Q14549 HT-SELEX +GBX2 GBX2_HUMAN.H11MO.0.D hocomoco 1 GBX2 HOX-related factors P52951 HT-SELEX +GCM1 GCM1_HUMAN.H11MO.0.D hocomoco 1 GCM1 GCM factors Q9NP62 Integrative +GCM2 GCM2_HUMAN.H11MO.0.D hocomoco 1 GCM2 GCM factors O75603 HT-SELEX +GCR GCR_HUMAN.H11MO.1.A hocomoco 1 NR3C1 Steroid hormone receptors (NR3) P04150 ChIP-Seq +GCR GCR_HUMAN.H11MO.0.A hocomoco 1 NR3C1 Steroid hormone receptors (NR3) P04150 ChIP-Seq +GFI1 GFI1_HUMAN.H11MO.0.C hocomoco 1 GFI1 More than 3 adjacent zinc finger factors Q99684 Integrative +GFI1B GFI1B_HUMAN.H11MO.0.A hocomoco 1 GFI1B More than 3 adjacent zinc finger factors Q5VTD9 ChIP-Seq +GLI1 GLI1_HUMAN.H11MO.0.D hocomoco 1 GLI1 More than 3 adjacent zinc finger factors P08151 ChIP-Seq +GLI2 GLI2_HUMAN.H11MO.0.D hocomoco 1 GLI2 More than 3 adjacent zinc finger factors P10070 Integrative +GLI3 GLI3_HUMAN.H11MO.0.B hocomoco 1 GLI3 More than 3 adjacent zinc finger factors P10071 Integrative +GLIS1 GLIS1_HUMAN.H11MO.0.D hocomoco 1 GLIS1 More than 3 adjacent zinc finger factors Q8NBF1 HT-SELEX +GLIS2 GLIS2_HUMAN.H11MO.0.D hocomoco 1 GLIS2 More than 3 adjacent zinc finger factors Q9BZE0 HT-SELEX +GLIS3 GLIS3_HUMAN.H11MO.0.D hocomoco 1 GLIS3 More than 3 adjacent zinc finger factors Q8NEA6 Integrative +GMEB2 GMEB2_HUMAN.H11MO.0.D hocomoco 1 GMEB2 GMEB Q9UKD1 HT-SELEX +GRHL1 GRHL1_HUMAN.H11MO.0.D hocomoco 1 GRHL1 Grainyhead-related factors Q9NZI5 ChIP-Seq +GRHL2 GRHL2_HUMAN.H11MO.0.A hocomoco 1 GRHL2 Grainyhead-related factors Q6ISB3 ChIP-Seq +GSC GSC_HUMAN.H11MO.0.D hocomoco 1 GSC Paired-related HD factors P56915 HT-SELEX +GSC2 GSC2_HUMAN.H11MO.0.D hocomoco 1 GSC2 Paired-related HD factors O15499 HT-SELEX +GSX1 GSX1_HUMAN.H11MO.0.D hocomoco 1 GSX1 HOX-related factors Q9H4S2 HT-SELEX +GSX2 GSX2_HUMAN.H11MO.0.D hocomoco 1 GSX2 HOX-related factors Q9BZM3 HT-SELEX +HAND1 HAND1_HUMAN.H11MO.0.D hocomoco 1 HAND1 Tal-related factors O96004 ChIP-Seq +HAND1 HAND1_HUMAN.H11MO.1.D hocomoco 1 HAND1 Tal-related factors O96004 ChIP-Seq +HBP1 HBP1_HUMAN.H11MO.0.D hocomoco 1 HBP1 SOX-related factors O60381 Integrative +HEN1 HEN1_HUMAN.H11MO.0.C hocomoco 1 NHLH1 Tal-related factors Q02575 Integrative +HES1 HES1_HUMAN.H11MO.0.D hocomoco 1 HES1 Hairy-related factors Q14469 Integrative +HES5 HES5_HUMAN.H11MO.0.D hocomoco 1 HES5 Hairy-related factors Q5TA89 HT-SELEX +HES7 HES7_HUMAN.H11MO.0.D hocomoco 1 HES7 Hairy-related factors Q9BYE0 HT-SELEX +HESX1 HESX1_HUMAN.H11MO.0.D hocomoco 1 HESX1 Paired-related HD factors Q9UBX0 Integrative +HEY1 HEY1_HUMAN.H11MO.0.D hocomoco 1 HEY1 Hairy-related factors Q9Y5J3 HT-SELEX +HEY2 HEY2_HUMAN.H11MO.0.D hocomoco 1 HEY2 Hairy-related factors Q9UBP5 Integrative +HIC1 HIC1_HUMAN.H11MO.0.C hocomoco 1 HIC1 Factors with multiple dispersed zinc fingers Q14526 Integrative +HIC2 HIC2_HUMAN.H11MO.0.D hocomoco 1 HIC2 Factors with multiple dispersed zinc fingers Q96JB3 HT-SELEX +HIF1A HIF1A_HUMAN.H11MO.0.C hocomoco 1 HIF1A PAS domain factors Q16665 ChIP-Seq +HINFP HINFP_HUMAN.H11MO.0.C hocomoco 1 HINFP Factors with multiple dispersed zinc fingers Q9BQA5 Integrative +HLF HLF_HUMAN.H11MO.0.C hocomoco 1 HLF C/EBP-related Q16534 Integrative +HLTF HLTF_HUMAN.H11MO.0.D hocomoco 1 HLTF . Q14527 Integrative +HMBX1 HMBX1_HUMAN.H11MO.0.D hocomoco 1 HMBOX1 POU domain factors Q6NT76 ChIP-Seq +HME1 HME1_HUMAN.H11MO.0.D hocomoco 1 EN1 NK-related factors Q05925 HT-SELEX +HME2 HME2_HUMAN.H11MO.0.D hocomoco 1 EN2 NK-related factors P19622 HT-SELEX +HMGA1 HMGA1_HUMAN.H11MO.0.D hocomoco 1 HMGA1 HMGA factors P17096 Integrative +HMGA2 HMGA2_HUMAN.H11MO.0.D hocomoco 1 HMGA2 HMGA factors P52926 Integrative +HMX1 HMX1_HUMAN.H11MO.0.D hocomoco 1 HMX1 NK-related factors Q9NP08 HT-SELEX +HMX2 HMX2_HUMAN.H11MO.0.D hocomoco 1 HMX2 NK-related factors A2RU54 HT-SELEX +HMX3 HMX3_HUMAN.H11MO.0.D hocomoco 1 HMX3 NK-related factors A6NHT5 HT-SELEX +HNF1A HNF1A_HUMAN.H11MO.0.C hocomoco 1 HNF1A POU domain factors P20823 ChIP-Seq +HNF1B HNF1B_HUMAN.H11MO.1.A hocomoco 1 HNF1B POU domain factors P35680 ChIP-Seq +HNF1B HNF1B_HUMAN.H11MO.0.A hocomoco 1 HNF1B POU domain factors P35680 ChIP-Seq +HNF4A HNF4A_HUMAN.H11MO.0.A hocomoco 1 HNF4A RXR-related receptors (NR2) P41235 ChIP-Seq +HNF4G HNF4G_HUMAN.H11MO.0.B hocomoco 1 HNF4G RXR-related receptors (NR2) Q14541 ChIP-Seq +HNF6 HNF6_HUMAN.H11MO.0.B hocomoco 1 ONECUT1 HD-CUT factors Q9UBC0 ChIP-Seq +HOMEZ HOMEZ_HUMAN.H11MO.0.D hocomoco 1 HOMEZ HD-ZF factors Q8IX15 HT-SELEX +HSF1 HSF1_HUMAN.H11MO.0.A hocomoco 1 HSF1 HSF factors Q00613 ChIP-Seq +HSF1 HSF1_HUMAN.H11MO.1.A hocomoco 1 HSF1 HSF factors Q00613 ChIP-Seq +HSF2 HSF2_HUMAN.H11MO.0.A hocomoco 1 HSF2 HSF factors Q03933 Integrative +HSF4 HSF4_HUMAN.H11MO.0.D hocomoco 1 HSF4 HSF factors Q9ULV5 HT-SELEX +HSFY1 HSFY1_HUMAN.H11MO.0.D hocomoco 1 HSFY1; HSFY2 HSF factors Q96LI6 HT-SELEX +HTF4 HTF4_HUMAN.H11MO.0.A hocomoco 1 TCF12 E2A-related factors Q99081 ChIP-Seq +HXA1 HXA1_HUMAN.H11MO.0.C hocomoco 1 HOXA1 HOX-related factors P49639 Integrative +HXA10 HXA10_HUMAN.H11MO.0.C hocomoco 1 HOXA10 HOX-related factors P31260 Integrative +HXA11 HXA11_HUMAN.H11MO.0.D hocomoco 1 HOXA11 HOX-related factors P31270 HT-SELEX +HXA13 HXA13_HUMAN.H11MO.0.C hocomoco 1 HOXA13 HOX-related factors P31271 Integrative +HXA2 HXA2_HUMAN.H11MO.0.D hocomoco 1 HOXA2 HOX-related factors O43364 HT-SELEX +HXA5 HXA5_HUMAN.H11MO.0.D hocomoco 1 HOXA5 HOX-related factors P20719 Integrative +HXA7 HXA7_HUMAN.H11MO.0.D hocomoco 1 HOXA7 HOX-related factors P31268 Integrative +HXA9 HXA9_HUMAN.H11MO.0.B hocomoco 1 HOXA9 HOX-related factors P31269 ChIP-Seq +HXB1 HXB1_HUMAN.H11MO.0.D hocomoco 1 HOXB1 HOX-related factors P14653 Integrative +HXB13 HXB13_HUMAN.H11MO.0.A hocomoco 1 HOXB13 HOX-related factors Q92826 ChIP-Seq +HXB2 HXB2_HUMAN.H11MO.0.D hocomoco 1 HOXB2 HOX-related factors P14652 HT-SELEX +HXB3 HXB3_HUMAN.H11MO.0.D hocomoco 1 HOXB3 HOX-related factors P14651 HT-SELEX +HXB4 HXB4_HUMAN.H11MO.0.B hocomoco 1 HOXB4 HOX-related factors P17483 ChIP-Seq +HXB6 HXB6_HUMAN.H11MO.0.D hocomoco 1 HOXB6 HOX-related factors P17509 Integrative +HXB7 HXB7_HUMAN.H11MO.0.C hocomoco 1 HOXB7 HOX-related factors P09629 Integrative +HXB8 HXB8_HUMAN.H11MO.0.C hocomoco 1 HOXB8 HOX-related factors P17481 Integrative +HXC10 HXC10_HUMAN.H11MO.0.D hocomoco 1 HOXC10 HOX-related factors Q9NYD6 HT-SELEX +HXC11 HXC11_HUMAN.H11MO.0.D hocomoco 1 HOXC11 HOX-related factors O43248 HT-SELEX +HXC12 HXC12_HUMAN.H11MO.0.D hocomoco 1 HOXC12 HOX-related factors P31275 HT-SELEX +HXC13 HXC13_HUMAN.H11MO.0.D hocomoco 1 HOXC13 HOX-related factors P31276 HT-SELEX +HXC6 HXC6_HUMAN.H11MO.0.D hocomoco 1 HOXC6 HOX-related factors P09630 Integrative +HXC8 HXC8_HUMAN.H11MO.0.D hocomoco 1 HOXC8 HOX-related factors P31273 Integrative +HXC9 HXC9_HUMAN.H11MO.0.C hocomoco 1 HOXC9 HOX-related factors P31274 ChIP-Seq +HXD10 HXD10_HUMAN.H11MO.0.D hocomoco 1 HOXD10 HOX-related factors P28358 Integrative +HXD11 HXD11_HUMAN.H11MO.0.D hocomoco 1 HOXD11 HOX-related factors P31277 HT-SELEX +HXD12 HXD12_HUMAN.H11MO.0.D hocomoco 1 HOXD12 HOX-related factors P35452 HT-SELEX +HXD13 HXD13_HUMAN.H11MO.0.D hocomoco 1 HOXD13 HOX-related factors P35453 Integrative +HXD3 HXD3_HUMAN.H11MO.0.D hocomoco 1 HOXD3 HOX-related factors P31249 HT-SELEX +HXD4 HXD4_HUMAN.H11MO.0.D hocomoco 1 HOXD4 HOX-related factors P09016 Integrative +HXD8 HXD8_HUMAN.H11MO.0.D hocomoco 1 HOXD8 HOX-related factors P13378 HT-SELEX +HXD9 HXD9_HUMAN.H11MO.0.D hocomoco 1 HOXD9 HOX-related factors P28356 Integrative +ID4 ID4_HUMAN.H11MO.0.D hocomoco 1 ID4 HLH domain only P47928 HT-SELEX +IKZF1 IKZF1_HUMAN.H11MO.0.C hocomoco 1 IKZF1 Factors with multiple dispersed zinc fingers Q13422 Integrative +INSM1 INSM1_HUMAN.H11MO.0.C hocomoco 1 INSM1 Factors with multiple dispersed zinc fingers Q01101 Integrative +IRF1 IRF1_HUMAN.H11MO.0.A hocomoco 1 IRF1 Interferon-regulatory factors P10914 ChIP-Seq +IRF2 IRF2_HUMAN.H11MO.0.A hocomoco 1 IRF2 Interferon-regulatory factors P14316 ChIP-Seq +IRF3 IRF3_HUMAN.H11MO.0.B hocomoco 1 IRF3 Interferon-regulatory factors Q14653 ChIP-Seq +IRF4 IRF4_HUMAN.H11MO.0.A hocomoco 1 IRF4 Interferon-regulatory factors Q15306 ChIP-Seq +IRF5 IRF5_HUMAN.H11MO.0.D hocomoco 1 IRF5 Interferon-regulatory factors Q13568 Integrative +IRF7 IRF7_HUMAN.H11MO.0.C hocomoco 1 IRF7 Interferon-regulatory factors Q92985 Integrative +IRF8 IRF8_HUMAN.H11MO.0.B hocomoco 1 IRF8 Interferon-regulatory factors Q02556 ChIP-Seq +IRF9 IRF9_HUMAN.H11MO.0.C hocomoco 1 IRF9 Interferon-regulatory factors Q00978 Integrative +IRX2 IRX2_HUMAN.H11MO.0.D hocomoco 1 IRX2 TALE-type homeo domain factors Q9BZI1 HT-SELEX +IRX3 IRX3_HUMAN.H11MO.0.D hocomoco 1 IRX3 TALE-type homeo domain factors P78415 HT-SELEX +ISL1 ISL1_HUMAN.H11MO.0.A hocomoco 1 ISL1 HD-LIM factors P61371 ChIP-Seq +ISL2 ISL2_HUMAN.H11MO.0.D hocomoco 1 ISL2 HD-LIM factors Q96A47 HT-SELEX +ISX ISX_HUMAN.H11MO.0.D hocomoco 1 ISX Paired-related HD factors Q2M1V0 HT-SELEX +ITF2 ITF2_HUMAN.H11MO.0.C hocomoco 1 TCF4 E2A-related factors P15884 ChIP-Seq +JDP2 JDP2_HUMAN.H11MO.0.D hocomoco 1 JDP2 Fos-related factors Q8WYK2 HT-SELEX +JUN JUN_HUMAN.H11MO.0.A hocomoco 1 JUN Jun-related factors P05412 ChIP-Seq +JUNB JUNB_HUMAN.H11MO.0.A hocomoco 1 JUNB Jun-related factors P17275 ChIP-Seq +JUND JUND_HUMAN.H11MO.0.A hocomoco 1 JUND Jun-related factors P17535 ChIP-Seq +KAISO KAISO_HUMAN.H11MO.1.A hocomoco 1 ZBTB33 Other factors with up to three adjacent zinc fingers Q86T24 ChIP-Seq +KAISO KAISO_HUMAN.H11MO.2.A hocomoco 1 ZBTB33 Other factors with up to three adjacent zinc fingers Q86T24 Integrative +KAISO KAISO_HUMAN.H11MO.0.A hocomoco 1 ZBTB33 Other factors with up to three adjacent zinc fingers Q86T24 ChIP-Seq +KLF1 KLF1_HUMAN.H11MO.0.A hocomoco 1 KLF1 Three-zinc finger Krüppel-related factors Q13351 ChIP-Seq +KLF12 KLF12_HUMAN.H11MO.0.C hocomoco 1 KLF12 Three-zinc finger Krüppel-related factors Q9Y4X4 ChIP-Seq +KLF13 KLF13_HUMAN.H11MO.0.D hocomoco 1 KLF13 Three-zinc finger Krüppel-related factors Q9Y2Y9 HT-SELEX +KLF14 KLF14_HUMAN.H11MO.0.D hocomoco 1 KLF14 Three-zinc finger Krüppel-related factors Q8TD94 HT-SELEX +KLF15 KLF15_HUMAN.H11MO.0.A hocomoco 1 KLF15 Three-zinc finger Krüppel-related factors Q9UIH9 ChIP-Seq +KLF16 KLF16_HUMAN.H11MO.0.D hocomoco 1 KLF16 Three-zinc finger Krüppel-related factors Q9BXK1 HT-SELEX +KLF3 KLF3_HUMAN.H11MO.0.B hocomoco 1 KLF3 Three-zinc finger Krüppel-related factors P57682 ChIP-Seq +KLF4 KLF4_HUMAN.H11MO.0.A hocomoco 1 KLF4 Three-zinc finger Krüppel-related factors O43474 ChIP-Seq +KLF5 KLF5_HUMAN.H11MO.0.A hocomoco 1 KLF5 Three-zinc finger Krüppel-related factors Q13887 ChIP-Seq +KLF6 KLF6_HUMAN.H11MO.0.A hocomoco 1 KLF6 Three-zinc finger Krüppel-related factors Q99612 ChIP-Seq +KLF8 KLF8_HUMAN.H11MO.0.C hocomoco 1 KLF8 Three-zinc finger Krüppel-related factors O95600 Integrative +KLF9 KLF9_HUMAN.H11MO.0.C hocomoco 1 KLF9 Three-zinc finger Krüppel-related factors Q13886 ChIP-Seq +LBX2 LBX2_HUMAN.H11MO.0.D hocomoco 1 LBX2 NK-related factors Q6XYB7 HT-SELEX +LEF1 LEF1_HUMAN.H11MO.0.A hocomoco 1 LEF1 TCF-7-related factors Q9UJU2 ChIP-Seq +LHX2 LHX2_HUMAN.H11MO.0.A hocomoco 1 LHX2 HD-LIM factors P50458 ChIP-Seq +LHX3 LHX3_HUMAN.H11MO.0.C hocomoco 1 LHX3 HD-LIM factors Q9UBR4 Integrative +LHX4 LHX4_HUMAN.H11MO.0.D hocomoco 1 LHX4 HD-LIM factors Q969G2 HT-SELEX +LHX6 LHX6_HUMAN.H11MO.0.D hocomoco 1 LHX6 HD-LIM factors Q9UPM6 ChIP-Seq +LHX8 LHX8_HUMAN.H11MO.0.D hocomoco 1 LHX8 HD-LIM factors Q68G74 HT-SELEX +LHX9 LHX9_HUMAN.H11MO.0.D hocomoco 1 LHX9 HD-LIM factors Q9NQ69 HT-SELEX +LMX1A LMX1A_HUMAN.H11MO.0.D hocomoco 1 LMX1A HD-LIM factors Q8TE12 HT-SELEX +LMX1B LMX1B_HUMAN.H11MO.0.D hocomoco 1 LMX1B HD-LIM factors O60663 HT-SELEX +LYL1 LYL1_HUMAN.H11MO.0.A hocomoco 1 LYL1 Tal-related factors P12980 ChIP-Seq +MAF MAF_HUMAN.H11MO.0.A hocomoco 1 MAF Maf-related factors O75444 ChIP-Seq +MAF MAF_HUMAN.H11MO.1.B hocomoco 1 MAF Maf-related factors O75444 ChIP-Seq +MAFA MAFA_HUMAN.H11MO.0.D hocomoco 1 MAFA Maf-related factors Q8NHW3 Integrative +MAFB MAFB_HUMAN.H11MO.0.B hocomoco 1 MAFB Maf-related factors Q9Y5Q3 ChIP-Seq +MAFF MAFF_HUMAN.H11MO.0.B hocomoco 1 MAFF Maf-related factors Q9ULX9 ChIP-Seq +MAFF MAFF_HUMAN.H11MO.1.B hocomoco 1 MAFF Maf-related factors Q9ULX9 ChIP-Seq +MAFG MAFG_HUMAN.H11MO.1.A hocomoco 1 MAFG Maf-related factors O15525 ChIP-Seq +MAFG MAFG_HUMAN.H11MO.0.A hocomoco 1 MAFG Maf-related factors O15525 ChIP-Seq +MAFK MAFK_HUMAN.H11MO.0.A hocomoco 1 MAFK Maf-related factors O60675 ChIP-Seq +MAFK MAFK_HUMAN.H11MO.1.A hocomoco 1 MAFK Maf-related factors O60675 ChIP-Seq +MAX MAX_HUMAN.H11MO.0.A hocomoco 1 MAX bHLH-ZIP factors P61244 ChIP-Seq +MAZ MAZ_HUMAN.H11MO.1.A hocomoco 1 MAZ Factors with multiple dispersed zinc fingers P56270 ChIP-Seq +MAZ MAZ_HUMAN.H11MO.0.A hocomoco 1 MAZ Factors with multiple dispersed zinc fingers P56270 ChIP-Seq +MBD2 MBD2_HUMAN.H11MO.0.B hocomoco 1 MBD2 . Q9UBB5 Integrative +MCR MCR_HUMAN.H11MO.0.D hocomoco 1 NR3C2 Steroid hormone receptors (NR3) P08235 Integrative +MECP2 MECP2_HUMAN.H11MO.0.C hocomoco 1 MECP2 . P51608 Integrative +MEF2A MEF2A_HUMAN.H11MO.0.A hocomoco 1 MEF2A Regulators of differentiation Q02078 ChIP-Seq +MEF2B MEF2B_HUMAN.H11MO.0.A hocomoco 1 MEF2B Regulators of differentiation Q02080 ChIP-Seq +MEF2C MEF2C_HUMAN.H11MO.0.A hocomoco 1 MEF2C Regulators of differentiation Q06413 ChIP-Seq +MEF2D MEF2D_HUMAN.H11MO.0.A hocomoco 1 MEF2D Regulators of differentiation Q14814 ChIP-Seq +MEIS1 MEIS1_HUMAN.H11MO.1.B hocomoco 1 MEIS1 TALE-type homeo domain factors O00470 ChIP-Seq +MEIS1 MEIS1_HUMAN.H11MO.0.A hocomoco 1 MEIS1 TALE-type homeo domain factors O00470 ChIP-Seq +MEIS2 MEIS2_HUMAN.H11MO.0.B hocomoco 1 MEIS2 TALE-type homeo domain factors O14770 Integrative +MEIS3 MEIS3_HUMAN.H11MO.0.D hocomoco 1 MEIS3 TALE-type homeo domain factors Q99687 HT-SELEX +MEOX1 MEOX1_HUMAN.H11MO.0.D hocomoco 1 MEOX1 HOX-related factors P50221 HT-SELEX +MEOX2 MEOX2_HUMAN.H11MO.0.D hocomoco 1 MEOX2 HOX-related factors P50222 HT-SELEX +MESP1 MESP1_HUMAN.H11MO.0.D hocomoco 1 MESP1 Tal-related factors Q9BRJ9 HT-SELEX +MGAP MGAP_HUMAN.H11MO.0.D hocomoco 1 MGA bHLH-ZIP factors Q8IWI9 HT-SELEX +MITF MITF_HUMAN.H11MO.0.A hocomoco 1 MITF bHLH-ZIP factors O75030 ChIP-Seq +MIXL1 MIXL1_HUMAN.H11MO.0.D hocomoco 1 MIXL1 Paired-related HD factors Q9H2W2 HT-SELEX +MLX MLX_HUMAN.H11MO.0.D hocomoco 1 MLX bHLH-ZIP factors Q9UH92 HT-SELEX +MLXPL MLXPL_HUMAN.H11MO.0.D hocomoco 1 MLXIPL bHLH-ZIP factors Q9NP71 Integrative +MNX1 MNX1_HUMAN.H11MO.0.D hocomoco 1 MNX1 HOX-related factors P50219 HT-SELEX +MSX1 MSX1_HUMAN.H11MO.0.D hocomoco 1 MSX1 NK-related factors P28360 HT-SELEX +MSX2 MSX2_HUMAN.H11MO.0.D hocomoco 1 MSX2 NK-related factors P35548 Integrative +MTF1 MTF1_HUMAN.H11MO.0.C hocomoco 1 MTF1 More than 3 adjacent zinc finger factors Q14872 Integrative +MXI1 MXI1_HUMAN.H11MO.1.A hocomoco 1 MXI1 bHLH-ZIP factors P50539 ChIP-Seq +MXI1 MXI1_HUMAN.H11MO.0.A hocomoco 1 MXI1 bHLH-ZIP factors P50539 ChIP-Seq +MYB MYB_HUMAN.H11MO.0.A hocomoco 1 MYB Myb/SANT domain factors P10242 ChIP-Seq +MYBA MYBA_HUMAN.H11MO.0.D hocomoco 1 MYBL1 Myb/SANT domain factors P10243 ChIP-Seq +MYBB MYBB_HUMAN.H11MO.0.D hocomoco 1 MYBL2 Myb/SANT domain factors P10244 Integrative +MYC MYC_HUMAN.H11MO.0.A hocomoco 1 MYC bHLH-ZIP factors P01106 ChIP-Seq +MYCN MYCN_HUMAN.H11MO.0.A hocomoco 1 MYCN bHLH-ZIP factors P04198 ChIP-Seq +MYF6 MYF6_HUMAN.H11MO.0.C hocomoco 1 MYF6 MyoD / ASC-related factors P23409 Integrative +MYNN MYNN_HUMAN.H11MO.0.D hocomoco 1 MYNN More than 3 adjacent zinc finger factors Q9NPC7 ChIP-Seq +MYOD1 MYOD1_HUMAN.H11MO.0.A hocomoco 1 MYOD1 MyoD / ASC-related factors P15172 ChIP-Seq +MYOD1 MYOD1_HUMAN.H11MO.1.A hocomoco 1 MYOD1 MyoD / ASC-related factors P15172 ChIP-Seq +MYOG MYOG_HUMAN.H11MO.0.B hocomoco 1 MYOG MyoD / ASC-related factors P15173 ChIP-Seq +MZF1 MZF1_HUMAN.H11MO.0.B hocomoco 1 MZF1 More than 3 adjacent zinc finger factors P28698 ChIP-Seq +NANOG NANOG_HUMAN.H11MO.0.A hocomoco 1 NANOG NK-related factors Q9H9S0 ChIP-Seq +NANOG NANOG_HUMAN.H11MO.1.B hocomoco 1 NANOG NK-related factors Q9H9S0 ChIP-Seq +NDF1 NDF1_HUMAN.H11MO.0.A hocomoco 1 NEUROD1 Tal-related factors Q13562 ChIP-Seq +NDF2 NDF2_HUMAN.H11MO.0.B hocomoco 1 NEUROD2 Tal-related factors Q15784 ChIP-Seq +NF2L1 NF2L1_HUMAN.H11MO.0.C hocomoco 1 NFE2L1 Jun-related factors Q14494 Integrative +NF2L2 NF2L2_HUMAN.H11MO.0.A hocomoco 1 NFE2L2 Jun-related factors Q16236 ChIP-Seq +NFAC1 NFAC1_HUMAN.H11MO.1.B hocomoco 1 NFATC1 NFAT-related factors O95644 ChIP-Seq +NFAC1 NFAC1_HUMAN.H11MO.0.B hocomoco 1 NFATC1 NFAT-related factors O95644 ChIP-Seq +NFAC2 NFAC2_HUMAN.H11MO.0.B hocomoco 1 NFATC2 NFAT-related factors Q13469 Integrative +NFAC3 NFAC3_HUMAN.H11MO.0.B hocomoco 1 NFATC3 NFAT-related factors Q12968 Integrative +NFAC4 NFAC4_HUMAN.H11MO.0.C hocomoco 1 NFATC4 NFAT-related factors Q14934 Integrative +NFAT5 NFAT5_HUMAN.H11MO.0.D hocomoco 1 NFAT5 NFAT-related factors O94916 ChIP-Seq +NFE2 NFE2_HUMAN.H11MO.0.A hocomoco 1 NFE2 Jun-related factors Q16621 ChIP-Seq +NFIA NFIA_HUMAN.H11MO.1.D hocomoco 1 NFIA Nuclear factor 1 Q12857 Integrative +NFIA NFIA_HUMAN.H11MO.0.C hocomoco 1 NFIA Nuclear factor 1 Q12857 Integrative +NFIB NFIB_HUMAN.H11MO.0.D hocomoco 1 NFIB Nuclear factor 1 O00712 ChIP-Seq +NFIC NFIC_HUMAN.H11MO.1.A hocomoco 1 NFIC Nuclear factor 1 P08651 ChIP-Seq +NFIC NFIC_HUMAN.H11MO.0.A hocomoco 1 NFIC Nuclear factor 1 P08651 ChIP-Seq +NFIL3 NFIL3_HUMAN.H11MO.0.D hocomoco 1 NFIL3 C/EBP-related Q16649 ChIP-Seq +NFKB1 NFKB1_HUMAN.H11MO.0.A hocomoco 1 NFKB1 NF-kappaB-related factors P19838 ChIP-Seq +NFKB2 NFKB2_HUMAN.H11MO.0.B hocomoco 1 NFKB2 NF-kappaB-related factors Q00653 ChIP-Seq +NFYA NFYA_HUMAN.H11MO.0.A hocomoco 1 NFYA Heteromeric CCAAT-binding factors P23511 ChIP-Seq +NFYB NFYB_HUMAN.H11MO.0.A hocomoco 1 NFYB Heteromeric CCAAT-binding factors P25208 Integrative +NFYC NFYC_HUMAN.H11MO.0.A hocomoco 1 NFYC Heteromeric CCAAT-binding factors Q13952 ChIP-Seq +NGN2 NGN2_HUMAN.H11MO.0.D hocomoco 1 NEUROG2 Tal-related factors Q9H2A3 ChIP-Seq +NKX21 NKX21_HUMAN.H11MO.0.A hocomoco 1 NKX2-1 NK-related factors P43699 ChIP-Seq +NKX22 NKX22_HUMAN.H11MO.0.D hocomoco 1 NKX2-2 NK-related factors O95096 ChIP-Seq +NKX23 NKX23_HUMAN.H11MO.0.D hocomoco 1 NKX2-3 NK-related factors Q8TAU0 HT-SELEX +NKX25 NKX25_HUMAN.H11MO.0.B hocomoco 1 NKX2-5 NK-related factors P52952 ChIP-Seq +NKX28 NKX28_HUMAN.H11MO.0.C hocomoco 1 NKX2-8 NK-related factors O15522 Integrative +NKX31 NKX31_HUMAN.H11MO.0.C hocomoco 1 NKX3-1 NK-related factors Q99801 ChIP-Seq +NKX32 NKX32_HUMAN.H11MO.0.C hocomoco 1 NKX3-2 NK-related factors P78367 ChIP-Seq +NKX61 NKX61_HUMAN.H11MO.1.B hocomoco 1 NKX6-1 NK-related factors P78426 ChIP-Seq +NKX61 NKX61_HUMAN.H11MO.0.B hocomoco 1 NKX6-1 NK-related factors P78426 ChIP-Seq +NKX62 NKX62_HUMAN.H11MO.0.D hocomoco 1 NKX6-2 NK-related factors Q9C056 HT-SELEX +NOBOX NOBOX_HUMAN.H11MO.0.C hocomoco 1 NOBOX Paired-related HD factors O60393 Integrative +NOTO NOTO_HUMAN.H11MO.0.D hocomoco 1 NOTO NK-related factors A8MTQ0 HT-SELEX +NR0B1 NR0B1_HUMAN.H11MO.0.D hocomoco 1 NR0B1 DAX-related receptors (NR0) P51843 Integrative +NR1D1 NR1D1_HUMAN.H11MO.0.B hocomoco 1 NR1D1 Thyroid hormone receptor-related factors (NR1) P20393 ChIP-Seq +NR1D1 NR1D1_HUMAN.H11MO.1.D hocomoco 1 NR1D1 Thyroid hormone receptor-related factors (NR1) P20393 Integrative +NR1H2 NR1H2_HUMAN.H11MO.0.D hocomoco 1 NR1H2 Thyroid hormone receptor-related factors (NR1) P55055 Integrative +NR1H3 NR1H3_HUMAN.H11MO.1.B hocomoco 1 NR1H3 Thyroid hormone receptor-related factors (NR1) Q13133 ChIP-Seq +NR1H3 NR1H3_HUMAN.H11MO.0.B hocomoco 1 NR1H3 Thyroid hormone receptor-related factors (NR1) Q13133 ChIP-Seq +NR1H4 NR1H4_HUMAN.H11MO.1.B hocomoco 1 NR1H4 Thyroid hormone receptor-related factors (NR1) Q96RI1 ChIP-Seq +NR1H4 NR1H4_HUMAN.H11MO.0.B hocomoco 1 NR1H4 Thyroid hormone receptor-related factors (NR1) Q96RI1 ChIP-Seq +NR1I2 NR1I2_HUMAN.H11MO.1.D hocomoco 1 NR1I2 Thyroid hormone receptor-related factors (NR1) O75469 Integrative +NR1I2 NR1I2_HUMAN.H11MO.0.C hocomoco 1 NR1I2 Thyroid hormone receptor-related factors (NR1) O75469 Integrative +NR1I3 NR1I3_HUMAN.H11MO.0.C hocomoco 1 NR1I3 Thyroid hormone receptor-related factors (NR1) Q14994 Integrative +NR1I3 NR1I3_HUMAN.H11MO.1.D hocomoco 1 NR1I3 Thyroid hormone receptor-related factors (NR1) Q14994 Integrative +NR2C1 NR2C1_HUMAN.H11MO.0.C hocomoco 1 NR2C1 RXR-related receptors (NR2) P13056 Integrative +NR2C2 NR2C2_HUMAN.H11MO.0.B hocomoco 1 NR2C2 RXR-related receptors (NR2) P49116 ChIP-Seq +NR2E1 NR2E1_HUMAN.H11MO.0.D hocomoco 1 NR2E1 RXR-related receptors (NR2) Q9Y466 HT-SELEX +NR2E3 NR2E3_HUMAN.H11MO.0.C hocomoco 1 NR2E3 RXR-related receptors (NR2) Q9Y5X4 Integrative +NR2F6 NR2F6_HUMAN.H11MO.0.D hocomoco 1 NR2F6 RXR-related receptors (NR2) P10588 Integrative +NR4A1 NR4A1_HUMAN.H11MO.0.A hocomoco 1 NR4A1 NGFI-B-related receptors (NR4) P22736 ChIP-Seq +NR4A2 NR4A2_HUMAN.H11MO.0.C hocomoco 1 NR4A2 NGFI-B-related receptors (NR4) P43354 Integrative +NR4A3 NR4A3_HUMAN.H11MO.0.D hocomoco 1 NR4A3 NGFI-B-related receptors (NR4) Q92570 Integrative +NR5A2 NR5A2_HUMAN.H11MO.0.B hocomoco 1 NR5A2 FTZ-F1-related receptors (NR5) O00482 ChIP-Seq +NR6A1 NR6A1_HUMAN.H11MO.0.B hocomoco 1 NR6A1 GCNF-related receptors (NR6) Q15406 Integrative +NRF1 NRF1_HUMAN.H11MO.0.A hocomoco 1 NRF1 NRF Q16656 ChIP-Seq +NRL NRL_HUMAN.H11MO.0.D hocomoco 1 NRL Maf-related factors P54845 HT-SELEX +OLIG1 OLIG1_HUMAN.H11MO.0.D hocomoco 1 OLIG1 Tal-related factors Q8TAK6 HT-SELEX +OLIG2 OLIG2_HUMAN.H11MO.0.B hocomoco 1 OLIG2 Tal-related factors Q13516 ChIP-Seq +OLIG2 OLIG2_HUMAN.H11MO.1.B hocomoco 1 OLIG2 Tal-related factors Q13516 ChIP-Seq +OLIG3 OLIG3_HUMAN.H11MO.0.D hocomoco 1 OLIG3 Tal-related factors Q7RTU3 HT-SELEX +ONEC2 ONEC2_HUMAN.H11MO.0.D hocomoco 1 ONECUT2 HD-CUT factors O95948 Integrative +ONEC3 ONEC3_HUMAN.H11MO.0.D hocomoco 1 ONECUT3 HD-CUT factors O60422 HT-SELEX +OSR2 OSR2_HUMAN.H11MO.0.C hocomoco 1 OSR2 More than 3 adjacent zinc finger factors Q8N2R0 ChIP-Seq +OTX1 OTX1_HUMAN.H11MO.0.D hocomoco 1 OTX1 Paired-related HD factors P32242 Integrative +OTX2 OTX2_HUMAN.H11MO.0.A hocomoco 1 OTX2 Paired-related HD factors P32243 ChIP-Seq +OVOL1 OVOL1_HUMAN.H11MO.0.C hocomoco 1 OVOL1 More than 3 adjacent zinc finger factors O14753 Integrative +OVOL2 OVOL2_HUMAN.H11MO.0.D hocomoco 1 OVOL2 More than 3 adjacent zinc finger factors Q9BRP0 ChIP-Seq +OZF OZF_HUMAN.H11MO.0.C hocomoco 1 ZNF146 More than 3 adjacent zinc finger factors Q15072 ChIP-Seq +P53 P53_HUMAN.H11MO.1.A hocomoco 1 TP53 p53-related factors P04637 ChIP-Seq +P53 P53_HUMAN.H11MO.0.A hocomoco 1 TP53 p53-related factors P04637 ChIP-Seq +P5F1B P5F1B_HUMAN.H11MO.0.D hocomoco 1 POU5F1B POU domain factors Q06416 HT-SELEX +P63 P63_HUMAN.H11MO.0.A hocomoco 1 TP63 p53-related factors Q9H3D4 ChIP-Seq +P63 P63_HUMAN.H11MO.1.A hocomoco 1 TP63 p53-related factors Q9H3D4 ChIP-Seq +P73 P73_HUMAN.H11MO.0.A hocomoco 1 TP73 p53-related factors O15350 ChIP-Seq +P73 P73_HUMAN.H11MO.1.A hocomoco 1 TP73 p53-related factors O15350 ChIP-Seq +PATZ1 PATZ1_HUMAN.H11MO.0.C hocomoco 1 PATZ1 Factors with multiple dispersed zinc fingers Q9HBE1 ChIP-Seq +PATZ1 PATZ1_HUMAN.H11MO.1.C hocomoco 1 PATZ1 Factors with multiple dispersed zinc fingers Q9HBE1 ChIP-Seq +PAX1 PAX1_HUMAN.H11MO.0.D hocomoco 1 PAX1 Paired domain only P15863 HT-SELEX +PAX2 PAX2_HUMAN.H11MO.0.D hocomoco 1 PAX2 Paired domain only Q02962 Integrative +PAX3 PAX3_HUMAN.H11MO.0.D hocomoco 1 PAX3 Paired plus homeo domain P23760 HT-SELEX +PAX4 PAX4_HUMAN.H11MO.0.D hocomoco 1 PAX4 Paired plus homeo domain O43316 HT-SELEX +PAX5 PAX5_HUMAN.H11MO.0.A hocomoco 1 PAX5 Paired domain only Q02548 ChIP-Seq +PAX6 PAX6_HUMAN.H11MO.0.C hocomoco 1 PAX6 Paired plus homeo domain P26367 ChIP-Seq +PAX7 PAX7_HUMAN.H11MO.0.D hocomoco 1 PAX7 Paired plus homeo domain P23759 HT-SELEX +PAX8 PAX8_HUMAN.H11MO.0.D hocomoco 1 PAX8 Paired domain only Q06710 Integrative +PBX1 PBX1_HUMAN.H11MO.1.C hocomoco 1 PBX1 TALE-type homeo domain factors P40424 Integrative +PBX1 PBX1_HUMAN.H11MO.0.A hocomoco 1 PBX1 TALE-type homeo domain factors P40424 ChIP-Seq +PBX2 PBX2_HUMAN.H11MO.0.C hocomoco 1 PBX2 TALE-type homeo domain factors P40425 Integrative +PBX3 PBX3_HUMAN.H11MO.1.A hocomoco 1 PBX3 TALE-type homeo domain factors P40426 ChIP-Seq +PBX3 PBX3_HUMAN.H11MO.0.A hocomoco 1 PBX3 TALE-type homeo domain factors P40426 ChIP-Seq +PDX1 PDX1_HUMAN.H11MO.1.A hocomoco 1 PDX1 HOX-related factors P52945 ChIP-Seq +PDX1 PDX1_HUMAN.H11MO.0.A hocomoco 1 PDX1 HOX-related factors P52945 ChIP-Seq +PEBB PEBB_HUMAN.H11MO.0.C hocomoco 1 CBFB . Q13951 Integrative +PHX2A PHX2A_HUMAN.H11MO.0.D hocomoco 1 PHOX2A Paired-related HD factors O14813 HT-SELEX +PHX2B PHX2B_HUMAN.H11MO.0.D hocomoco 1 PHOX2B Paired-related HD factors Q99453 HT-SELEX +PIT1 PIT1_HUMAN.H11MO.0.C hocomoco 1 POU1F1 POU domain factors P28069 Integrative +PITX1 PITX1_HUMAN.H11MO.0.D hocomoco 1 PITX1 Paired-related HD factors P78337 ChIP-Seq +PITX2 PITX2_HUMAN.H11MO.0.D hocomoco 1 PITX2 Paired-related HD factors Q99697 Integrative +PITX3 PITX3_HUMAN.H11MO.0.D hocomoco 1 PITX3 Paired-related HD factors O75364 HT-SELEX +PKNX1 PKNX1_HUMAN.H11MO.0.B hocomoco 1 PKNOX1 TALE-type homeo domain factors P55347 ChIP-Seq +PLAG1 PLAG1_HUMAN.H11MO.0.D hocomoco 1 PLAG1 More than 3 adjacent zinc finger factors Q6DJT9 Integrative +PLAL1 PLAL1_HUMAN.H11MO.0.D hocomoco 1 PLAGL1 More than 3 adjacent zinc finger factors Q9UM63 Integrative +PO2F1 PO2F1_HUMAN.H11MO.0.C hocomoco 1 POU2F1 POU domain factors P14859 ChIP-Seq +PO2F2 PO2F2_HUMAN.H11MO.0.A hocomoco 1 POU2F2 POU domain factors P09086 ChIP-Seq +PO2F3 PO2F3_HUMAN.H11MO.0.D hocomoco 1 POU2F3 POU domain factors Q9UKI9 HT-SELEX +PO3F1 PO3F1_HUMAN.H11MO.0.C hocomoco 1 POU3F1 POU domain factors Q03052 Integrative +PO3F2 PO3F2_HUMAN.H11MO.0.A hocomoco 1 POU3F2 POU domain factors P20265 ChIP-Seq +PO3F3 PO3F3_HUMAN.H11MO.0.D hocomoco 1 POU3F3 POU domain factors P20264 HT-SELEX +PO3F4 PO3F4_HUMAN.H11MO.0.D hocomoco 1 POU3F4 POU domain factors P49335 HT-SELEX +PO4F1 PO4F1_HUMAN.H11MO.0.D hocomoco 1 POU4F1 POU domain factors Q01851 HT-SELEX +PO4F2 PO4F2_HUMAN.H11MO.0.D hocomoco 1 POU4F2 POU domain factors Q12837 Integrative +PO4F3 PO4F3_HUMAN.H11MO.0.D hocomoco 1 POU4F3 POU domain factors Q15319 HT-SELEX +PO5F1 PO5F1_HUMAN.H11MO.0.A hocomoco 1 POU5F1 POU domain factors Q01860 ChIP-Seq +PO5F1 PO5F1_HUMAN.H11MO.1.A hocomoco 1 POU5F1 POU domain factors Q01860 ChIP-Seq +PO6F1 PO6F1_HUMAN.H11MO.0.D hocomoco 1 POU6F1 POU domain factors Q14863 Integrative +PO6F2 PO6F2_HUMAN.H11MO.0.D hocomoco 1 POU6F2 POU domain factors P78424 HT-SELEX +PPARA PPARA_HUMAN.H11MO.1.B hocomoco 1 PPARA Thyroid hormone receptor-related factors (NR1) Q07869 ChIP-Seq +PPARA PPARA_HUMAN.H11MO.0.B hocomoco 1 PPARA Thyroid hormone receptor-related factors (NR1) Q07869 ChIP-Seq +PPARD PPARD_HUMAN.H11MO.0.D hocomoco 1 PPARD Thyroid hormone receptor-related factors (NR1) Q03181 Integrative +PPARG PPARG_HUMAN.H11MO.0.A hocomoco 1 PPARG Thyroid hormone receptor-related factors (NR1) P37231 ChIP-Seq +PPARG PPARG_HUMAN.H11MO.1.A hocomoco 1 PPARG Thyroid hormone receptor-related factors (NR1) P37231 ChIP-Seq +PRD14 PRD14_HUMAN.H11MO.0.A hocomoco 1 PRDM14 More than 3 adjacent zinc finger factors Q9GZV8 ChIP-Seq +PRDM1 PRDM1_HUMAN.H11MO.0.A hocomoco 1 PRDM1 More than 3 adjacent zinc finger factors O75626 ChIP-Seq +PRDM4 PRDM4_HUMAN.H11MO.0.D hocomoco 1 PRDM4 Factors with multiple dispersed zinc fingers Q9UKN5 HT-SELEX +PRDM6 PRDM6_HUMAN.H11MO.0.C hocomoco 1 PRDM6 More than 3 adjacent zinc finger factors Q9NQX0 ChIP-Seq +PRGR PRGR_HUMAN.H11MO.1.A hocomoco 1 PGR Steroid hormone receptors (NR3) P06401 ChIP-Seq +PRGR PRGR_HUMAN.H11MO.0.A hocomoco 1 PGR Steroid hormone receptors (NR3) P06401 ChIP-Seq +PROP1 PROP1_HUMAN.H11MO.0.D hocomoco 1 PROP1 Paired-related HD factors O75360 ChIP-Seq +PROX1 PROX1_HUMAN.H11MO.0.D hocomoco 1 PROX1 HD-PROS factors Q92786 HT-SELEX +PRRX1 PRRX1_HUMAN.H11MO.0.D hocomoco 1 PRRX1 Paired-related HD factors P54821 Integrative +PRRX2 PRRX2_HUMAN.H11MO.0.C hocomoco 1 PRRX2 Paired-related HD factors Q99811 Integrative +PTF1A PTF1A_HUMAN.H11MO.0.B hocomoco 1 PTF1A Tal-related factors Q7RTS3 ChIP-Seq +PTF1A PTF1A_HUMAN.H11MO.1.B hocomoco 1 PTF1A Tal-related factors Q7RTS3 ChIP-Seq +PURA PURA_HUMAN.H11MO.0.D hocomoco 1 PURA PUR Q00577 Integrative +RARA RARA_HUMAN.H11MO.1.A hocomoco 1 RARA Thyroid hormone receptor-related factors (NR1) P10276 ChIP-Seq +RARA RARA_HUMAN.H11MO.2.A hocomoco 1 RARA Thyroid hormone receptor-related factors (NR1) P10276 Integrative +RARA RARA_HUMAN.H11MO.0.A hocomoco 1 RARA Thyroid hormone receptor-related factors (NR1) P10276 ChIP-Seq +RARB RARB_HUMAN.H11MO.0.D hocomoco 1 RARB Thyroid hormone receptor-related factors (NR1) P10826 Integrative +RARG RARG_HUMAN.H11MO.2.D hocomoco 1 RARG Thyroid hormone receptor-related factors (NR1) P13631 Integrative +RARG RARG_HUMAN.H11MO.0.B hocomoco 1 RARG Thyroid hormone receptor-related factors (NR1) P13631 ChIP-Seq +RARG RARG_HUMAN.H11MO.1.B hocomoco 1 RARG Thyroid hormone receptor-related factors (NR1) P13631 ChIP-Seq +RAX2 RAX2_HUMAN.H11MO.0.D hocomoco 1 RAX2 Paired-related HD factors Q96IS3 HT-SELEX +REL REL_HUMAN.H11MO.0.B hocomoco 1 REL NF-kappaB-related factors Q04864 ChIP-Seq +RELB RELB_HUMAN.H11MO.0.C hocomoco 1 RELB NF-kappaB-related factors Q01201 Integrative +REST REST_HUMAN.H11MO.0.A hocomoco 1 REST Factors with multiple dispersed zinc fingers Q13127 ChIP-Seq +RFX1 RFX1_HUMAN.H11MO.0.B hocomoco 1 RFX1 RFX-related factors P22670 ChIP-Seq +RFX1 RFX1_HUMAN.H11MO.1.B hocomoco 1 RFX1 RFX-related factors P22670 ChIP-Seq +RFX2 RFX2_HUMAN.H11MO.0.A hocomoco 1 RFX2 RFX-related factors P48378 ChIP-Seq +RFX2 RFX2_HUMAN.H11MO.1.A hocomoco 1 RFX2 RFX-related factors P48378 ChIP-Seq +RFX3 RFX3_HUMAN.H11MO.0.B hocomoco 1 RFX3 RFX-related factors P48380 Integrative +RFX4 RFX4_HUMAN.H11MO.0.D hocomoco 1 RFX4 RFX-related factors Q33E94 HT-SELEX +RFX5 RFX5_HUMAN.H11MO.0.A hocomoco 1 RFX5 RFX-related factors P48382 ChIP-Seq +RFX5 RFX5_HUMAN.H11MO.1.A hocomoco 1 RFX5 RFX-related factors P48382 ChIP-Seq +RHXF1 RHXF1_HUMAN.H11MO.0.D hocomoco 1 RHOXF1 Paired-related HD factors Q8NHV9 HT-SELEX +RORA RORA_HUMAN.H11MO.0.C hocomoco 1 RORA Thyroid hormone receptor-related factors (NR1) P35398 ChIP-Seq +RORG RORG_HUMAN.H11MO.0.C hocomoco 1 RORC Thyroid hormone receptor-related factors (NR1) P51449 ChIP-Seq +RREB1 RREB1_HUMAN.H11MO.0.D hocomoco 1 RREB1 Factors with multiple dispersed zinc fingers Q92766 Integrative +RUNX1 RUNX1_HUMAN.H11MO.0.A hocomoco 1 RUNX1 Runt-related factors Q01196 ChIP-Seq +RUNX2 RUNX2_HUMAN.H11MO.0.A hocomoco 1 RUNX2 Runt-related factors Q13950 ChIP-Seq +RUNX3 RUNX3_HUMAN.H11MO.0.A hocomoco 1 RUNX3 Runt-related factors Q13761 ChIP-Seq +RX RX_HUMAN.H11MO.0.D hocomoco 1 RAX Paired-related HD factors Q9Y2V3 HT-SELEX +RXRA RXRA_HUMAN.H11MO.1.A hocomoco 1 RXRA RXR-related receptors (NR2) P19793 ChIP-Seq +RXRA RXRA_HUMAN.H11MO.0.A hocomoco 1 RXRA RXR-related receptors (NR2) P19793 ChIP-Seq +RXRB RXRB_HUMAN.H11MO.0.C hocomoco 1 RXRB RXR-related receptors (NR2) P28702 Integrative +RXRG RXRG_HUMAN.H11MO.0.B hocomoco 1 RXRG RXR-related receptors (NR2) P48443 Integrative +SALL4 SALL4_HUMAN.H11MO.0.B hocomoco 1 SALL4 Factors with multiple dispersed zinc fingers Q9UJQ4 ChIP-Seq +SCRT1 SCRT1_HUMAN.H11MO.0.D hocomoco 1 SCRT1 More than 3 adjacent zinc finger factors Q9BWW7 HT-SELEX +SCRT2 SCRT2_HUMAN.H11MO.0.D hocomoco 1 SCRT2 More than 3 adjacent zinc finger factors Q9NQ03 HT-SELEX +SHOX SHOX_HUMAN.H11MO.0.D hocomoco 1 SHOX Paired-related HD factors O15266 HT-SELEX +SHOX2 SHOX2_HUMAN.H11MO.0.D hocomoco 1 SHOX2 Paired-related HD factors O60902 HT-SELEX +SIX1 SIX1_HUMAN.H11MO.0.A hocomoco 1 SIX1 HD-SINE factors Q15475 ChIP-Seq +SIX2 SIX2_HUMAN.H11MO.0.A hocomoco 1 SIX2 HD-SINE factors Q9NPC8 ChIP-Seq +SMAD1 SMAD1_HUMAN.H11MO.0.D hocomoco 1 SMAD1 SMAD factors Q15797 Integrative +SMAD2 SMAD2_HUMAN.H11MO.0.A hocomoco 1 SMAD2 SMAD factors Q15796 ChIP-Seq +SMAD3 SMAD3_HUMAN.H11MO.0.B hocomoco 1 SMAD3 SMAD factors P84022 ChIP-Seq +SMAD4 SMAD4_HUMAN.H11MO.0.B hocomoco 1 SMAD4 SMAD factors Q13485 ChIP-Seq +SMCA1 SMCA1_HUMAN.H11MO.0.C hocomoco 1 SMARCA1 Myb/SANT domain factors P28370 ChIP-Seq +SMCA5 SMCA5_HUMAN.H11MO.0.C hocomoco 1 SMARCA5 Myb/SANT domain factors O60264 ChIP-Seq +SNAI1 SNAI1_HUMAN.H11MO.0.C hocomoco 1 SNAI1 More than 3 adjacent zinc finger factors O95863 Integrative +SNAI2 SNAI2_HUMAN.H11MO.0.A hocomoco 1 SNAI2 More than 3 adjacent zinc finger factors O43623 ChIP-Seq +SOX1 SOX1_HUMAN.H11MO.0.D hocomoco 1 SOX1 SOX-related factors O00570 HT-SELEX +SOX10 SOX10_HUMAN.H11MO.1.A hocomoco 1 SOX10 SOX-related factors P56693 ChIP-Seq +SOX10 SOX10_HUMAN.H11MO.0.B hocomoco 1 SOX10 SOX-related factors P56693 ChIP-Seq +SOX11 SOX11_HUMAN.H11MO.0.D hocomoco 1 SOX11 SOX-related factors P35716 HT-SELEX +SOX13 SOX13_HUMAN.H11MO.0.D hocomoco 1 SOX13 SOX-related factors Q9UN79 Integrative +SOX15 SOX15_HUMAN.H11MO.0.D hocomoco 1 SOX15 SOX-related factors O60248 Integrative +SOX17 SOX17_HUMAN.H11MO.0.C hocomoco 1 SOX17 SOX-related factors Q9H6I2 ChIP-Seq +SOX18 SOX18_HUMAN.H11MO.0.D hocomoco 1 SOX18 SOX-related factors P35713 Integrative +SOX2 SOX2_HUMAN.H11MO.1.A hocomoco 1 SOX2 SOX-related factors P48431 ChIP-Seq +SOX2 SOX2_HUMAN.H11MO.0.A hocomoco 1 SOX2 SOX-related factors P48431 ChIP-Seq +SOX21 SOX21_HUMAN.H11MO.0.D hocomoco 1 SOX21 SOX-related factors Q9Y651 HT-SELEX +SOX3 SOX3_HUMAN.H11MO.0.B hocomoco 1 SOX3 SOX-related factors P41225 ChIP-Seq +SOX4 SOX4_HUMAN.H11MO.0.B hocomoco 1 SOX4 SOX-related factors Q06945 ChIP-Seq +SOX5 SOX5_HUMAN.H11MO.0.C hocomoco 1 SOX5 SOX-related factors P35711 Integrative +SOX7 SOX7_HUMAN.H11MO.0.D hocomoco 1 SOX7 SOX-related factors Q9BT81 HT-SELEX +SOX8 SOX8_HUMAN.H11MO.0.D hocomoco 1 SOX8 SOX-related factors P57073 HT-SELEX +SOX9 SOX9_HUMAN.H11MO.1.B hocomoco 1 SOX9 SOX-related factors P48436 ChIP-Seq +SOX9 SOX9_HUMAN.H11MO.0.B hocomoco 1 SOX9 SOX-related factors P48436 ChIP-Seq +SP1 SP1_HUMAN.H11MO.1.A hocomoco 1 SP1 Three-zinc finger Krüppel-related factors P08047 ChIP-Seq +SP1 SP1_HUMAN.H11MO.0.A hocomoco 1 SP1 Three-zinc finger Krüppel-related factors P08047 ChIP-Seq +SP2 SP2_HUMAN.H11MO.0.A hocomoco 1 SP2 Three-zinc finger Krüppel-related factors Q02086 ChIP-Seq +SP2 SP2_HUMAN.H11MO.1.B hocomoco 1 SP2 Three-zinc finger Krüppel-related factors Q02086 ChIP-Seq +SP3 SP3_HUMAN.H11MO.0.B hocomoco 1 SP3 Three-zinc finger Krüppel-related factors Q02447 Integrative +SP4 SP4_HUMAN.H11MO.0.A hocomoco 1 SP4 Three-zinc finger Krüppel-related factors Q02446 ChIP-Seq +SP4 SP4_HUMAN.H11MO.1.A hocomoco 1 SP4 Three-zinc finger Krüppel-related factors Q02446 ChIP-Seq +SPDEF SPDEF_HUMAN.H11MO.0.D hocomoco 1 SPDEF Ets-related factors O95238 HT-SELEX +SPI1 SPI1_HUMAN.H11MO.0.A hocomoco 1 SPI1 Ets-related factors P17947 ChIP-Seq +SPIB SPIB_HUMAN.H11MO.0.A hocomoco 1 SPIB Ets-related factors Q01892 ChIP-Seq +SPIC SPIC_HUMAN.H11MO.0.D hocomoco 1 SPIC Ets-related factors Q8N5J4 HT-SELEX +SPZ1 SPZ1_HUMAN.H11MO.0.D hocomoco 1 SPZ1 . Q9BXG8 Integrative +SRBP1 SRBP1_HUMAN.H11MO.0.A hocomoco 1 SREBF1 bHLH-ZIP factors P36956 ChIP-Seq +SRBP2 SRBP2_HUMAN.H11MO.0.B hocomoco 1 SREBF2 bHLH-ZIP factors Q12772 Integrative +SRF SRF_HUMAN.H11MO.0.A hocomoco 1 SRF Responders to external signals (SRF/RLM1) P11831 ChIP-Seq +SRY SRY_HUMAN.H11MO.0.B hocomoco 1 SRY SOX-related factors Q05066 Integrative +STA5A STA5A_HUMAN.H11MO.0.A hocomoco 1 STAT5A STAT factors P42229 ChIP-Seq +STA5B STA5B_HUMAN.H11MO.0.A hocomoco 1 STAT5B STAT factors P51692 ChIP-Seq +STAT1 STAT1_HUMAN.H11MO.0.A hocomoco 1 STAT1 STAT factors P42224 ChIP-Seq +STAT1 STAT1_HUMAN.H11MO.1.A hocomoco 1 STAT1 STAT factors P42224 ChIP-Seq +STAT2 STAT2_HUMAN.H11MO.0.A hocomoco 1 STAT2 STAT factors P52630 ChIP-Seq +STAT3 STAT3_HUMAN.H11MO.0.A hocomoco 1 STAT3 STAT factors P40763 ChIP-Seq +STAT4 STAT4_HUMAN.H11MO.0.A hocomoco 1 STAT4 STAT factors Q14765 ChIP-Seq +STAT6 STAT6_HUMAN.H11MO.0.B hocomoco 1 STAT6 STAT factors P42226 ChIP-Seq +STF1 STF1_HUMAN.H11MO.0.B hocomoco 1 NR5A1 FTZ-F1-related receptors (NR5) Q13285 ChIP-Seq +SUH SUH_HUMAN.H11MO.0.A hocomoco 1 RBPJ CSL-related factors Q06330 ChIP-Seq +TAF1 TAF1_HUMAN.H11MO.0.A hocomoco 1 TAF1 TCF-7-related factors P21675 ChIP-Seq +TAL1 TAL1_HUMAN.H11MO.0.A hocomoco 1 TAL1 Tal-related factors P17542 ChIP-Seq +TAL1 TAL1_HUMAN.H11MO.1.A hocomoco 1 TAL1 Tal-related factors P17542 ChIP-Seq +TBP TBP_HUMAN.H11MO.0.A hocomoco 1 TBP TBP-related factors P20226 ChIP-Seq +TBR1 TBR1_HUMAN.H11MO.0.D hocomoco 1 TBR1 TBrain-related factors Q16650 HT-SELEX +TBX1 TBX1_HUMAN.H11MO.0.D hocomoco 1 TBX1 TBX1-related factors O43435 HT-SELEX +TBX15 TBX15_HUMAN.H11MO.0.D hocomoco 1 TBX15 TBX1-related factors Q96SF7 HT-SELEX +TBX19 TBX19_HUMAN.H11MO.0.D hocomoco 1 TBX19 Brachyury-related factors O60806 HT-SELEX +TBX2 TBX2_HUMAN.H11MO.0.D hocomoco 1 TBX2 TBX2-related factors Q13207 Integrative +TBX20 TBX20_HUMAN.H11MO.0.D hocomoco 1 TBX20 TBX1-related factors Q9UMR3 ChIP-Seq +TBX21 TBX21_HUMAN.H11MO.0.A hocomoco 1 TBX21 TBrain-related factors Q9UL17 ChIP-Seq +TBX3 TBX3_HUMAN.H11MO.0.C hocomoco 1 TBX3 TBX2-related factors O15119 ChIP-Seq +TBX4 TBX4_HUMAN.H11MO.0.D hocomoco 1 TBX4 TBX2-related factors P57082 HT-SELEX +TBX5 TBX5_HUMAN.H11MO.0.D hocomoco 1 TBX5 TBX2-related factors Q99593 Integrative +TCF7 TCF7_HUMAN.H11MO.0.A hocomoco 1 TCF7 TCF-7-related factors P36402 ChIP-Seq +TEAD1 TEAD1_HUMAN.H11MO.0.A hocomoco 1 TEAD1 TEF-1-related factors P28347 ChIP-Seq +TEAD2 TEAD2_HUMAN.H11MO.0.D hocomoco 1 TEAD2 TEF-1-related factors Q15562 ChIP-Seq +TEAD3 TEAD3_HUMAN.H11MO.0.D hocomoco 1 TEAD3 TEF-1-related factors Q99594 Integrative +TEAD4 TEAD4_HUMAN.H11MO.0.A hocomoco 1 TEAD4 TEF-1-related factors Q15561 ChIP-Seq +TEF TEF_HUMAN.H11MO.0.D hocomoco 1 TEF C/EBP-related Q10587 Integrative +TF2LX TF2LX_HUMAN.H11MO.0.D hocomoco 1 TGIF2LX TALE-type homeo domain factors Q8IUE1 HT-SELEX +TF65 TF65_HUMAN.H11MO.0.A hocomoco 1 RELA NF-kappaB-related factors Q04206 ChIP-Seq +TF7L1 TF7L1_HUMAN.H11MO.0.B hocomoco 1 TCF7L1 TCF-7-related factors Q9HCS4 ChIP-Seq +TF7L2 TF7L2_HUMAN.H11MO.0.A hocomoco 1 TCF7L2 TCF-7-related factors Q9NQB0 ChIP-Seq +TFAP4 TFAP4_HUMAN.H11MO.0.A hocomoco 1 TFAP4 bHLH-ZIP factors Q01664 ChIP-Seq +TFCP2 TFCP2_HUMAN.H11MO.0.D hocomoco 1 TFCP2 CP2-related factors Q12800 Integrative +TFDP1 TFDP1_HUMAN.H11MO.0.C hocomoco 1 TFDP1 E2F-related factors Q14186 ChIP-Seq +TFE2 TFE2_HUMAN.H11MO.0.A hocomoco 1 TCF3 E2A-related factors P15923 ChIP-Seq +TFE3 TFE3_HUMAN.H11MO.0.B hocomoco 1 TFE3 bHLH-ZIP factors P19532 ChIP-Seq +TFEB TFEB_HUMAN.H11MO.0.C hocomoco 1 TFEB bHLH-ZIP factors P19484 Integrative +TGIF1 TGIF1_HUMAN.H11MO.0.A hocomoco 1 TGIF1 TALE-type homeo domain factors Q15583 ChIP-Seq +TGIF2 TGIF2_HUMAN.H11MO.0.D hocomoco 1 TGIF2 TALE-type homeo domain factors Q9GZN2 HT-SELEX +THA THA_HUMAN.H11MO.0.C hocomoco 1 THRA Thyroid hormone receptor-related factors (NR1) P10827 Integrative +THA THA_HUMAN.H11MO.1.D hocomoco 1 THRA Thyroid hormone receptor-related factors (NR1) P10827 Integrative +THA11 THA11_HUMAN.H11MO.0.B hocomoco 1 THAP11 THAP-related factors Q96EK4 ChIP-Seq +THAP1 THAP1_HUMAN.H11MO.0.C hocomoco 1 THAP1 THAP-related factors Q9NVV9 ChIP-Seq +THB THB_HUMAN.H11MO.1.D hocomoco 1 THRB Thyroid hormone receptor-related factors (NR1) P10828 Integrative +THB THB_HUMAN.H11MO.0.C hocomoco 1 THRB Thyroid hormone receptor-related factors (NR1) P10828 Integrative +TLX1 TLX1_HUMAN.H11MO.0.D hocomoco 1 TLX1 NK-related factors P31314 Integrative +TWST1 TWST1_HUMAN.H11MO.0.A hocomoco 1 TWIST1 Tal-related factors Q15672 ChIP-Seq +TWST1 TWST1_HUMAN.H11MO.1.A hocomoco 1 TWIST1 Tal-related factors Q15672 ChIP-Seq +TYY1 TYY1_HUMAN.H11MO.0.A hocomoco 1 YY1 More than 3 adjacent zinc finger factors P25490 ChIP-Seq +TYY2 TYY2_HUMAN.H11MO.0.D hocomoco 1 YY2 More than 3 adjacent zinc finger factors O15391 HT-SELEX +UBIP1 UBIP1_HUMAN.H11MO.0.D hocomoco 1 UBP1 CP2-related factors Q9NZI7 Integrative +UNC4 UNC4_HUMAN.H11MO.0.D hocomoco 1 UNCX Paired-related HD factors A6NJT0 HT-SELEX +USF1 USF1_HUMAN.H11MO.0.A hocomoco 1 USF1 bHLH-ZIP factors P22415 ChIP-Seq +USF2 USF2_HUMAN.H11MO.0.A hocomoco 1 USF2 bHLH-ZIP factors Q15853 ChIP-Seq +VAX1 VAX1_HUMAN.H11MO.0.D hocomoco 1 VAX1 NK-related factors Q5SQQ9 HT-SELEX +VAX2 VAX2_HUMAN.H11MO.0.D hocomoco 1 VAX2 NK-related factors Q9UIW0 HT-SELEX +VDR VDR_HUMAN.H11MO.0.A hocomoco 1 VDR Thyroid hormone receptor-related factors (NR1) P11473 ChIP-Seq +VDR VDR_HUMAN.H11MO.1.A hocomoco 1 VDR Thyroid hormone receptor-related factors (NR1) P11473 ChIP-Seq +VENTX VENTX_HUMAN.H11MO.0.D hocomoco 1 VENTX NK-related factors O95231 HT-SELEX +VEZF1 VEZF1_HUMAN.H11MO.0.C hocomoco 1 VEZF1 Factors with multiple dispersed zinc fingers Q14119 ChIP-Seq +VEZF1 VEZF1_HUMAN.H11MO.1.C hocomoco 1 VEZF1 Factors with multiple dispersed zinc fingers Q14119 ChIP-Seq +VSX1 VSX1_HUMAN.H11MO.0.D hocomoco 1 VSX1 Paired-related HD factors Q9NZR4 HT-SELEX +VSX2 VSX2_HUMAN.H11MO.0.D hocomoco 1 VSX2 Paired-related HD factors P58304 ChIP-Seq +WT1 WT1_HUMAN.H11MO.0.C hocomoco 1 WT1 More than 3 adjacent zinc finger factors P19544 ChIP-Seq +WT1 WT1_HUMAN.H11MO.1.B hocomoco 1 WT1 More than 3 adjacent zinc finger factors P19544 ChIP-Seq +XBP1 XBP1_HUMAN.H11MO.0.D hocomoco 1 XBP1 XBP-1-related factors P17861 ChIP-Seq +Z324A Z324A_HUMAN.H11MO.0.C hocomoco 1 ZNF324 More than 3 adjacent zinc finger factors O75467 ChIP-Seq +Z354A Z354A_HUMAN.H11MO.0.C hocomoco 1 ZNF354A More than 3 adjacent zinc finger factors O60765 ChIP-Seq +ZBED1 ZBED1_HUMAN.H11MO.0.D hocomoco 1 ZBED1 BED zinc finger factors O96006 HT-SELEX +ZBT14 ZBT14_HUMAN.H11MO.0.C hocomoco 1 ZBTB14 More than 3 adjacent zinc finger factors O43829 ChIP-Seq +ZBT17 ZBT17_HUMAN.H11MO.0.A hocomoco 1 ZBTB17 Factors with multiple dispersed zinc fingers Q13105 ChIP-Seq +ZBT18 ZBT18_HUMAN.H11MO.0.C hocomoco 1 ZBTB18 More than 3 adjacent zinc finger factors Q99592 ChIP-Seq +ZBT48 ZBT48_HUMAN.H11MO.0.C hocomoco 1 ZBTB48 More than 3 adjacent zinc finger factors P10074 ChIP-Seq +ZBT49 ZBT49_HUMAN.H11MO.0.D hocomoco 1 ZBTB49 More than 3 adjacent zinc finger factors Q6ZSB9 HT-SELEX +ZBT7A ZBT7A_HUMAN.H11MO.0.A hocomoco 1 ZBTB7A More than 3 adjacent zinc finger factors O95365 ChIP-Seq +ZBT7B ZBT7B_HUMAN.H11MO.0.D hocomoco 1 ZBTB7B More than 3 adjacent zinc finger factors O15156 ChIP-Seq +ZBTB4 ZBTB4_HUMAN.H11MO.0.D hocomoco 1 ZBTB4 Factors with multiple dispersed zinc fingers Q9P1Z0 Integrative +ZBTB4 ZBTB4_HUMAN.H11MO.1.D hocomoco 1 ZBTB4 Factors with multiple dispersed zinc fingers Q9P1Z0 Integrative +ZBTB6 ZBTB6_HUMAN.H11MO.0.C hocomoco 1 ZBTB6 More than 3 adjacent zinc finger factors Q15916 ChIP-Seq +ZEB1 ZEB1_HUMAN.H11MO.0.A hocomoco 1 ZEB1 HD-ZF factors P37275 ChIP-Seq +ZEP1 ZEP1_HUMAN.H11MO.0.D hocomoco 1 HIVEP1 Factors with multiple dispersed zinc fingers P15822 Integrative +ZEP2 ZEP2_HUMAN.H11MO.0.D hocomoco 1 HIVEP2 Factors with multiple dispersed zinc fingers P31629 Integrative +ZF64A ZF64A_HUMAN.H11MO.0.D hocomoco 1 ZFP64 More than 3 adjacent zinc finger factors Q9NPA5 ChIP-Seq +ZFHX3 ZFHX3_HUMAN.H11MO.0.D hocomoco 1 ZFHX3 HD-ZF factors Q15911 Integrative +ZFP28 ZFP28_HUMAN.H11MO.0.C hocomoco 1 ZFP28 More than 3 adjacent zinc finger factors Q8NHY6 ChIP-Seq +ZFP42 ZFP42_HUMAN.H11MO.0.A hocomoco 1 ZFP42 More than 3 adjacent zinc finger factors Q96MM3 ChIP-Seq +ZFP82 ZFP82_HUMAN.H11MO.0.C hocomoco 1 ZFP82 More than 3 adjacent zinc finger factors Q8N141 ChIP-Seq +ZFX ZFX_HUMAN.H11MO.0.A hocomoco 1 ZFX More than 3 adjacent zinc finger factors P17010 ChIP-Seq +ZFX ZFX_HUMAN.H11MO.1.A hocomoco 1 ZFX More than 3 adjacent zinc finger factors P17010 ChIP-Seq +ZIC1 ZIC1_HUMAN.H11MO.0.B hocomoco 1 ZIC1 More than 3 adjacent zinc finger factors Q15915 Integrative +ZIC2 ZIC2_HUMAN.H11MO.0.D hocomoco 1 ZIC2 More than 3 adjacent zinc finger factors O95409 ChIP-Seq +ZIC3 ZIC3_HUMAN.H11MO.0.B hocomoco 1 ZIC3 More than 3 adjacent zinc finger factors O60481 ChIP-Seq +ZIC4 ZIC4_HUMAN.H11MO.0.D hocomoco 1 ZIC4 More than 3 adjacent zinc finger factors Q8N9L1 HT-SELEX +ZIM3 ZIM3_HUMAN.H11MO.0.C hocomoco 1 ZIM3 More than 3 adjacent zinc finger factors Q96PE6 ChIP-Seq +ZKSC1 ZKSC1_HUMAN.H11MO.0.B hocomoco 1 ZKSCAN1 More than 3 adjacent zinc finger factors P17029 ChIP-Seq +ZKSC3 ZKSC3_HUMAN.H11MO.0.D hocomoco 1 ZKSCAN3 More than 3 adjacent zinc finger factors Q9BRR0 HT-SELEX +ZN121 ZN121_HUMAN.H11MO.0.C hocomoco 1 ZNF121 More than 3 adjacent zinc finger factors P58317 ChIP-Seq +ZN134 ZN134_HUMAN.H11MO.1.C hocomoco 1 ZNF134 Factors with multiple dispersed zinc fingers P52741 ChIP-Seq +ZN134 ZN134_HUMAN.H11MO.0.C hocomoco 1 ZNF134 Factors with multiple dispersed zinc fingers P52741 ChIP-Seq +ZN136 ZN136_HUMAN.H11MO.0.C hocomoco 1 ZNF136 More than 3 adjacent zinc finger factors P52737 ChIP-Seq +ZN140 ZN140_HUMAN.H11MO.0.C hocomoco 1 ZNF140 More than 3 adjacent zinc finger factors P52738 ChIP-Seq +ZN143 ZN143_HUMAN.H11MO.0.A hocomoco 1 ZNF143 More than 3 adjacent zinc finger factors P52747 ChIP-Seq +ZN148 ZN148_HUMAN.H11MO.0.D hocomoco 1 ZNF148 More than 3 adjacent zinc finger factors Q9UQR1 Integrative +ZN214 ZN214_HUMAN.H11MO.0.C hocomoco 1 ZNF214 More than 3 adjacent zinc finger factors Q9UL59 ChIP-Seq +ZN219 ZN219_HUMAN.H11MO.0.D hocomoco 1 ZNF219 Factors with multiple dispersed zinc fingers Q9P2Y4 Integrative +ZN232 ZN232_HUMAN.H11MO.0.D hocomoco 1 ZNF232 More than 3 adjacent zinc finger factors Q9UNY5 HT-SELEX +ZN250 ZN250_HUMAN.H11MO.0.C hocomoco 1 ZNF250 More than 3 adjacent zinc finger factors P15622 ChIP-Seq +ZN257 ZN257_HUMAN.H11MO.0.C hocomoco 1 ZNF257 More than 3 adjacent zinc finger factors Q9Y2Q1 ChIP-Seq +ZN260 ZN260_HUMAN.H11MO.0.C hocomoco 1 ZNF260 More than 3 adjacent zinc finger factors Q3ZCT1 ChIP-Seq +ZN263 ZN263_HUMAN.H11MO.1.A hocomoco 1 ZNF263 More than 3 adjacent zinc finger factors O14978 ChIP-Seq +ZN263 ZN263_HUMAN.H11MO.0.A hocomoco 1 ZNF263 More than 3 adjacent zinc finger factors O14978 ChIP-Seq +ZN264 ZN264_HUMAN.H11MO.0.C hocomoco 1 ZNF264 More than 3 adjacent zinc finger factors O43296 ChIP-Seq +ZN274 ZN274_HUMAN.H11MO.0.A hocomoco 1 ZNF274 More than 3 adjacent zinc finger factors Q96GC6 ChIP-Seq +ZN281 ZN281_HUMAN.H11MO.0.A hocomoco 1 ZNF281 More than 3 adjacent zinc finger factors Q9Y2X9 ChIP-Seq +ZN282 ZN282_HUMAN.H11MO.0.D hocomoco 1 ZNF282 More than 3 adjacent zinc finger factors Q9UDV7 HT-SELEX +ZN317 ZN317_HUMAN.H11MO.0.C hocomoco 1 ZNF317 More than 3 adjacent zinc finger factors Q96PQ6 ChIP-Seq +ZN320 ZN320_HUMAN.H11MO.0.C hocomoco 1 ZNF320 More than 3 adjacent zinc finger factors A2RRD8 ChIP-Seq +ZN322 ZN322_HUMAN.H11MO.0.B hocomoco 1 ZNF322 More than 3 adjacent zinc finger factors Q6U7Q0 ChIP-Seq +ZN329 ZN329_HUMAN.H11MO.0.C hocomoco 1 ZNF329 More than 3 adjacent zinc finger factors Q86UD4 ChIP-Seq +ZN331 ZN331_HUMAN.H11MO.0.C hocomoco 1 ZNF331 More than 3 adjacent zinc finger factors Q9NQX6 ChIP-Seq +ZN333 ZN333_HUMAN.H11MO.0.D hocomoco 1 ZNF333 More than 3 adjacent zinc finger factors Q96JL9 Integrative +ZN335 ZN335_HUMAN.H11MO.0.A hocomoco 1 ZNF335 Factors with multiple dispersed zinc fingers Q9H4Z2 ChIP-Seq +ZN335 ZN335_HUMAN.H11MO.1.A hocomoco 1 ZNF335 Factors with multiple dispersed zinc fingers Q9H4Z2 ChIP-Seq +ZN341 ZN341_HUMAN.H11MO.0.C hocomoco 1 ZNF341 Factors with multiple dispersed zinc fingers Q9BYN7 ChIP-Seq +ZN341 ZN341_HUMAN.H11MO.1.C hocomoco 1 ZNF341 Factors with multiple dispersed zinc fingers Q9BYN7 ChIP-Seq +ZN350 ZN350_HUMAN.H11MO.0.C hocomoco 1 ZNF350 More than 3 adjacent zinc finger factors Q9GZX5 ChIP-Seq +ZN350 ZN350_HUMAN.H11MO.1.D hocomoco 1 ZNF350 More than 3 adjacent zinc finger factors Q9GZX5 Integrative +ZN382 ZN382_HUMAN.H11MO.0.C hocomoco 1 ZNF382 Factors with multiple dispersed zinc fingers Q96SR6 ChIP-Seq +ZN384 ZN384_HUMAN.H11MO.0.C hocomoco 1 ZNF384 More than 3 adjacent zinc finger factors Q8TF68 Integrative +ZN394 ZN394_HUMAN.H11MO.1.D hocomoco 1 ZNF394 More than 3 adjacent zinc finger factors Q53GI3 ChIP-Seq +ZN394 ZN394_HUMAN.H11MO.0.C hocomoco 1 ZNF394 More than 3 adjacent zinc finger factors Q53GI3 ChIP-Seq +ZN410 ZN410_HUMAN.H11MO.0.D hocomoco 1 ZNF410 More than 3 adjacent zinc finger factors Q86VK4 HT-SELEX +ZN418 ZN418_HUMAN.H11MO.0.C hocomoco 1 ZNF418 Factors with multiple dispersed zinc fingers Q8TF45 ChIP-Seq +ZN418 ZN418_HUMAN.H11MO.1.D hocomoco 1 ZNF418 Factors with multiple dispersed zinc fingers Q8TF45 ChIP-Seq +ZN423 ZN423_HUMAN.H11MO.0.D hocomoco 1 ZNF423 Factors with multiple dispersed zinc fingers Q2M1K9 Integrative +ZN436 ZN436_HUMAN.H11MO.0.C hocomoco 1 ZNF436 More than 3 adjacent zinc finger factors Q9C0F3 ChIP-Seq +ZN449 ZN449_HUMAN.H11MO.0.C hocomoco 1 ZNF449 More than 3 adjacent zinc finger factors Q6P9G9 ChIP-Seq +ZN467 ZN467_HUMAN.H11MO.0.C hocomoco 1 ZNF467 Factors with multiple dispersed zinc fingers Q7Z7K2 ChIP-Seq +ZN490 ZN490_HUMAN.H11MO.0.C hocomoco 1 ZNF490 More than 3 adjacent zinc finger factors Q9ULM2 ChIP-Seq +ZN502 ZN502_HUMAN.H11MO.0.C hocomoco 1 ZNF502 More than 3 adjacent zinc finger factors Q8TBZ5 ChIP-Seq +ZN524 ZN524_HUMAN.H11MO.0.D hocomoco 1 ZNF524 More than 3 adjacent zinc finger factors Q96C55 HT-SELEX +ZN528 ZN528_HUMAN.H11MO.0.C hocomoco 1 ZNF528 More than 3 adjacent zinc finger factors Q3MIS6 ChIP-Seq +ZN547 ZN547_HUMAN.H11MO.0.C hocomoco 1 ZNF547 More than 3 adjacent zinc finger factors Q8IVP9 ChIP-Seq +ZN549 ZN549_HUMAN.H11MO.0.C hocomoco 1 ZNF549 More than 3 adjacent zinc finger factors Q6P9A3 ChIP-Seq +ZN554 ZN554_HUMAN.H11MO.1.D hocomoco 1 ZNF554 More than 3 adjacent zinc finger factors Q86TJ5 ChIP-Seq +ZN554 ZN554_HUMAN.H11MO.0.C hocomoco 1 ZNF554 More than 3 adjacent zinc finger factors Q86TJ5 ChIP-Seq +ZN563 ZN563_HUMAN.H11MO.0.C hocomoco 1 ZNF563 More than 3 adjacent zinc finger factors Q8TA94 ChIP-Seq +ZN563 ZN563_HUMAN.H11MO.1.C hocomoco 1 ZNF563 More than 3 adjacent zinc finger factors Q8TA94 ChIP-Seq +ZN582 ZN582_HUMAN.H11MO.0.C hocomoco 1 ZNF582 More than 3 adjacent zinc finger factors Q96NG8 ChIP-Seq +ZN586 ZN586_HUMAN.H11MO.0.C hocomoco 1 ZNF586 More than 3 adjacent zinc finger factors Q9NXT0 ChIP-Seq +ZN589 ZN589_HUMAN.H11MO.0.D hocomoco 1 ZNF589 More than 3 adjacent zinc finger factors Q86UQ0 Integrative +ZN652 ZN652_HUMAN.H11MO.0.D hocomoco 1 ZNF652 More than 3 adjacent zinc finger factors Q9Y2D9 HT-SELEX +ZN667 ZN667_HUMAN.H11MO.0.C hocomoco 1 ZNF667 More than 3 adjacent zinc finger factors Q5HYK9 ChIP-Seq +ZN680 ZN680_HUMAN.H11MO.0.C hocomoco 1 ZNF680 More than 3 adjacent zinc finger factors Q8NEM1 ChIP-Seq +ZN708 ZN708_HUMAN.H11MO.0.C hocomoco 1 ZNF708 More than 3 adjacent zinc finger factors P17019 ChIP-Seq +ZN708 ZN708_HUMAN.H11MO.1.D hocomoco 1 ZNF708 More than 3 adjacent zinc finger factors P17019 ChIP-Seq +ZN713 ZN713_HUMAN.H11MO.0.D hocomoco 1 ZNF713 More than 3 adjacent zinc finger factors Q8N859 HT-SELEX +ZN740 ZN740_HUMAN.H11MO.0.D hocomoco 1 ZNF740 Other factors with up to three adjacent zinc fingers Q8NDX6 HT-SELEX +ZN768 ZN768_HUMAN.H11MO.0.C hocomoco 1 ZNF768 More than 3 adjacent zinc finger factors Q9H5H4 ChIP-Seq +ZN770 ZN770_HUMAN.H11MO.1.C hocomoco 1 ZNF770 Factors with multiple dispersed zinc fingers Q6IQ21 ChIP-Seq +ZN770 ZN770_HUMAN.H11MO.0.C hocomoco 1 ZNF770 Factors with multiple dispersed zinc fingers Q6IQ21 ChIP-Seq +ZN784 ZN784_HUMAN.H11MO.0.D hocomoco 1 ZNF784 Factors with multiple dispersed zinc fingers Q8NCA9 HT-SELEX +ZN816 ZN816_HUMAN.H11MO.1.C hocomoco 1 ZNF816 More than 3 adjacent zinc finger factors Q0VGE8 ChIP-Seq +ZN816 ZN816_HUMAN.H11MO.0.C hocomoco 1 ZNF816 More than 3 adjacent zinc finger factors Q0VGE8 ChIP-Seq +ZNF18 ZNF18_HUMAN.H11MO.0.C hocomoco 1 ZNF18 More than 3 adjacent zinc finger factors P17022 ChIP-Seq +ZNF41 ZNF41_HUMAN.H11MO.0.C hocomoco 1 ZNF41 More than 3 adjacent zinc finger factors P51814 ChIP-Seq +ZNF41 ZNF41_HUMAN.H11MO.1.C hocomoco 1 ZNF41 More than 3 adjacent zinc finger factors P51814 ChIP-Seq +ZNF76 ZNF76_HUMAN.H11MO.0.C hocomoco 1 ZNF76 More than 3 adjacent zinc finger factors P36508 ChIP-Seq +ZNF8 ZNF8_HUMAN.H11MO.0.C hocomoco 1 ZNF8 Factors with multiple dispersed zinc fingers P17098 ChIP-Seq +ZNF85 ZNF85_HUMAN.H11MO.1.C hocomoco 1 ZNF85 More than 3 adjacent zinc finger factors Q03923 ChIP-Seq +ZNF85 ZNF85_HUMAN.H11MO.0.C hocomoco 1 ZNF85 More than 3 adjacent zinc finger factors Q03923 ChIP-Seq +ZSC16 ZSC16_HUMAN.H11MO.0.D hocomoco 1 ZSCAN16 More than 3 adjacent zinc finger factors Q9H4T2 HT-SELEX +ZSC22 ZSC22_HUMAN.H11MO.0.C hocomoco 1 ZSCAN22 More than 3 adjacent zinc finger factors P10073 ChIP-Seq +ZSC31 ZSC31_HUMAN.H11MO.0.C hocomoco 1 ZSCAN31 More than 3 adjacent zinc finger factors Q96LW9 ChIP-Seq +ZSCA4 ZSCA4_HUMAN.H11MO.0.D hocomoco 1 ZSCAN4 More than 3 adjacent zinc finger factors Q8NAM6 HT-SELEX diff --git a/data/motifs/hocomoco/.nfs00000001e5aee3dd00006215 b/data/motifs/hocomoco/.nfs00000001e5aee3dd00006215 deleted file mode 100644 index 6d832f741..000000000 --- a/data/motifs/hocomoco/.nfs00000001e5aee3dd00006215 +++ /dev/null @@ -1,4 +0,0 @@ -1 1 0 0 1 0 0 1 2 0 1 0 5 0 2 -0 1 1 0 0 0 0 0 0 2 2 3 0 1 1 -3 0 0 0 4 0 0 1 0 3 0 0 0 4 1 -1 3 4 5 0 5 5 3 3 0 2 2 0 0 1 diff --git a/data/motifs/hocomoco_anno.csv b/data/motifs/hocomoco_anno.csv new file mode 100644 index 000000000..74ee1e937 --- /dev/null +++ b/data/motifs/hocomoco_anno.csv @@ -0,0 +1,772 @@ +TfName,GeneName,Family,UniProt,Source +AHR_HUMAN.H11MO.0.B,AHR,PAS domain factors,P35869,Integrative, +AIRE_HUMAN.H11MO.0.C,AIRE,AIRE,O43918,Integrative, +ALX1_HUMAN.H11MO.0.B,ALX1,Paired-related HD factors,Q15699,Integrative, +ALX3_HUMAN.H11MO.0.D,ALX3,Paired-related HD factors,O95076,HT-SELEX, +ALX4_HUMAN.H11MO.0.D,ALX4,Paired-related HD factors,Q9H161,HT-SELEX, +ANDR_HUMAN.H11MO.0.A,AR,Steroid hormone receptors (NR3),P10275,ChIP-Seq, +ANDR_HUMAN.H11MO.1.A,AR,Steroid hormone receptors (NR3),P10275,ChIP-Seq, +ANDR_HUMAN.H11MO.2.A,AR,Steroid hormone receptors (NR3),P10275,ChIP-Seq, +AP2A_HUMAN.H11MO.0.A,TFAP2A,AP-2,P05549,ChIP-Seq, +AP2B_HUMAN.H11MO.0.B,TFAP2B,AP-2,Q92481,Integrative, +AP2C_HUMAN.H11MO.0.A,TFAP2C,AP-2,Q92754,ChIP-Seq, +AP2D_HUMAN.H11MO.0.D,TFAP2D,AP-2,Q7Z6R9,Integrative, +ARI3A_HUMAN.H11MO.0.D,ARID3A,ARID-related factors,Q99856,Integrative, +ARI5B_HUMAN.H11MO.0.C,ARID5B,ARID-related factors,Q14865,Integrative, +ARNT2_HUMAN.H11MO.0.D,ARNT2,PAS domain factors,Q9HBZ2,Integrative, +ARNT_HUMAN.H11MO.0.B,ARNT,PAS domain factors,P27540,ChIP-Seq, +ARX_HUMAN.H11MO.0.D,ARX,Paired-related HD factors,Q96QS3,HT-SELEX, +ASCL1_HUMAN.H11MO.0.A,ASCL1,MyoD / ASC-related factors,P50553,ChIP-Seq, +ASCL2_HUMAN.H11MO.0.D,ASCL2,MyoD / ASC-related factors,Q99929,ChIP-Seq, +ATF1_HUMAN.H11MO.0.B,ATF1,CREB-related factors,P18846,ChIP-Seq, +ATF2_HUMAN.H11MO.0.B,ATF2,Jun-related factors,P15336,ChIP-Seq, +ATF2_HUMAN.H11MO.1.B,ATF2,Jun-related factors,P15336,ChIP-Seq, +ATF2_HUMAN.H11MO.2.C,ATF2,Jun-related factors,P15336,ChIP-Seq, +ATF3_HUMAN.H11MO.0.A,ATF3,Fos-related factors,P18847,ChIP-Seq, +ATF4_HUMAN.H11MO.0.A,ATF4,ATF-4-related factors,P18848,ChIP-Seq, +ATF6A_HUMAN.H11MO.0.B,ATF6,CREB-related factors,P18850,Integrative, +ATF7_HUMAN.H11MO.0.D,ATF7,Jun-related factors,P17544,HT-SELEX, +ATOH1_HUMAN.H11MO.0.B,ATOH1,Tal-related factors,Q92858,ChIP-Seq, +BACH1_HUMAN.H11MO.0.A,BACH1,Jun-related factors,O14867,ChIP-Seq, +BACH2_HUMAN.H11MO.0.A,BACH2,Jun-related factors,Q9BYV9,ChIP-Seq, +BARH1_HUMAN.H11MO.0.D,BARHL1,NK-related factors,Q9BZE3,HT-SELEX, +BARH2_HUMAN.H11MO.0.D,BARHL2,NK-related factors,Q9NY43,HT-SELEX, +BARX1_HUMAN.H11MO.0.D,BARX1,NK-related factors,Q9HBU1,ChIP-Seq, +BARX2_HUMAN.H11MO.0.D,BARX2,NK-related factors,Q9UMQ3,Integrative, +BATF3_HUMAN.H11MO.0.B,BATF3,B-ATF-related factors,Q9NR55,ChIP-Seq, +BATF_HUMAN.H11MO.0.A,BATF,B-ATF-related factors,Q16520,ChIP-Seq, +BATF_HUMAN.H11MO.1.A,BATF,B-ATF-related factors,Q16520,ChIP-Seq, +BC11A_HUMAN.H11MO.0.A,BCL11A,Factors with multiple dispersed zinc fingers,Q9H165,ChIP-Seq, +BCL6_HUMAN.H11MO.0.A,BCL6,More than 3 adjacent zinc finger factors,P41182,ChIP-Seq, +BCL6B_HUMAN.H11MO.0.D,BCL6B,More than 3 adjacent zinc finger factors,Q8N143,HT-SELEX, +BHA15_HUMAN.H11MO.0.B,BHLHA15,Tal-related factors,Q7RTS1,ChIP-Seq, +BHE22_HUMAN.H11MO.0.D,BHLHE22,Tal-related factors,Q8NFJ8,HT-SELEX, +BHE23_HUMAN.H11MO.0.D,BHLHE23,Tal-related factors,Q8NDY6,HT-SELEX, +BHE40_HUMAN.H11MO.0.A,BHLHE40,Hairy-related factors,O14503,ChIP-Seq, +BHE41_HUMAN.H11MO.0.D,BHLHE41,Hairy-related factors,Q9C0J9,Integrative, +BMAL1_HUMAN.H11MO.0.A,ARNTL,PAS domain factors,O00327,ChIP-Seq, +BPTF_HUMAN.H11MO.0.D,BPTF,,Q12830,Integrative, +BRAC_HUMAN.H11MO.0.A,T,Brachyury-related factors,O15178,ChIP-Seq, +BRAC_HUMAN.H11MO.1.B,T,Brachyury-related factors,O15178,ChIP-Seq, +BRCA1_HUMAN.H11MO.0.D,BRCA1,,P38398,Integrative, +BSH_HUMAN.H11MO.0.D,BSX,NK-related factors,Q3C1V8,HT-SELEX, +CDC5L_HUMAN.H11MO.0.D,CDC5L,Myb/SANT domain factors,Q99459,Integrative, +CDX1_HUMAN.H11MO.0.C,CDX1,HOX-related factors,P47902,Integrative, +CDX2_HUMAN.H11MO.0.A,CDX2,HOX-related factors,Q99626,ChIP-Seq, +CEBPA_HUMAN.H11MO.0.A,CEBPA,C/EBP-related,P49715,ChIP-Seq, +CEBPB_HUMAN.H11MO.0.A,CEBPB,C/EBP-related,P17676,ChIP-Seq, +CEBPD_HUMAN.H11MO.0.C,CEBPD,C/EBP-related,P49716,ChIP-Seq, +CEBPE_HUMAN.H11MO.0.A,CEBPE,C/EBP-related,Q15744,Integrative, +CEBPG_HUMAN.H11MO.0.B,CEBPG,C/EBP-related,P53567,ChIP-Seq, +CEBPZ_HUMAN.H11MO.0.D,CEBPZ,,Q03701,Integrative, +CENPB_HUMAN.H11MO.0.D,CENPB,,P07199,HT-SELEX, +CLOCK_HUMAN.H11MO.0.C,CLOCK,PAS domain factors,O15516,ChIP-Seq, +COE1_HUMAN.H11MO.0.A,EBF1,Early B-Cell Factor-related factors,Q9UH73,ChIP-Seq, +COT1_HUMAN.H11MO.0.C,NR2F1,RXR-related receptors (NR2),P10589,ChIP-Seq, +COT1_HUMAN.H11MO.1.C,NR2F1,RXR-related receptors (NR2),P10589,ChIP-Seq, +COT2_HUMAN.H11MO.0.A,NR2F2,RXR-related receptors (NR2),P24468,ChIP-Seq, +COT2_HUMAN.H11MO.1.A,NR2F2,RXR-related receptors (NR2),P24468,ChIP-Seq, +CPEB1_HUMAN.H11MO.0.D,CPEB1,,Q9BZB8,HT-SELEX, +CR3L1_HUMAN.H11MO.0.D,CREB3L1,CREB-related factors,Q96BA8,HT-SELEX, +CR3L2_HUMAN.H11MO.0.D,CREB3L2,CREB-related factors,Q70SY1,HT-SELEX, +CREB1_HUMAN.H11MO.0.A,CREB1,CREB-related factors,P16220,ChIP-Seq, +CREB3_HUMAN.H11MO.0.D,CREB3,CREB-related factors,O43889,HT-SELEX, +CREB5_HUMAN.H11MO.0.D,CREB5,Jun-related factors,Q02930,HT-SELEX, +CREM_HUMAN.H11MO.0.C,CREM,CREB-related factors,Q03060,Integrative, +CRX_HUMAN.H11MO.0.B,CRX,Paired-related HD factors,O43186,ChIP-Seq, +CTCF_HUMAN.H11MO.0.A,CTCF,More than 3 adjacent zinc finger factors,P49711,ChIP-Seq, +CTCFL_HUMAN.H11MO.0.A,CTCFL,More than 3 adjacent zinc finger factors,Q8NI51,ChIP-Seq, +CUX1_HUMAN.H11MO.0.C,CUX1,HD-CUT factors,P39880,Integrative, +CUX2_HUMAN.H11MO.0.D,CUX2,HD-CUT factors,O14529,ChIP-Seq, +CXXC1_HUMAN.H11MO.0.D,CXXC1,CpG-binding proteins,Q9P0U4,Integrative, +DBP_HUMAN.H11MO.0.B,DBP,C/EBP-related,Q10586,Integrative, +DDIT3_HUMAN.H11MO.0.D,DDIT3,C/EBP-related,P35638,ChIP-Seq, +DLX1_HUMAN.H11MO.0.D,DLX1,NK-related factors,P56177,HT-SELEX, +DLX2_HUMAN.H11MO.0.D,DLX2,NK-related factors,Q07687,Integrative, +DLX3_HUMAN.H11MO.0.C,DLX3,NK-related factors,O60479,Integrative, +DLX4_HUMAN.H11MO.0.D,DLX4,NK-related factors,Q92988,HT-SELEX, +DLX5_HUMAN.H11MO.0.D,DLX5,NK-related factors,P56178,ChIP-Seq, +DLX6_HUMAN.H11MO.0.D,DLX6,NK-related factors,P56179,HT-SELEX, +DMBX1_HUMAN.H11MO.0.D,DMBX1,Paired-related HD factors,Q8NFW5,HT-SELEX, +DMRT1_HUMAN.H11MO.0.D,DMRT1,DMRT,Q9Y5R6,ChIP-Seq, +DPRX_HUMAN.H11MO.0.D,DPRX,Paired-related HD factors,A6NFQ7,HT-SELEX, +DRGX_HUMAN.H11MO.0.D,DRGX,Paired-related HD factors,A6NNA5,HT-SELEX, +DUX4_HUMAN.H11MO.0.A,DUX4,Paired-related HD factors,Q9UBX2,ChIP-Seq, +DUXA_HUMAN.H11MO.0.D,DUXA,Paired-related HD factors,A6NLW8,HT-SELEX, +E2F1_HUMAN.H11MO.0.A,E2F1,E2F-related factors,Q01094,ChIP-Seq, +E2F2_HUMAN.H11MO.0.B,E2F2,E2F-related factors,Q14209,Integrative, +E2F3_HUMAN.H11MO.0.A,E2F3,E2F-related factors,O00716,ChIP-Seq, +E2F4_HUMAN.H11MO.0.A,E2F4,E2F-related factors,Q16254,ChIP-Seq, +E2F4_HUMAN.H11MO.1.A,E2F4,E2F-related factors,Q16254,ChIP-Seq, +E2F5_HUMAN.H11MO.0.B,E2F5,E2F-related factors,Q15329,Integrative, +E2F6_HUMAN.H11MO.0.A,E2F6,E2F-related factors,O75461,ChIP-Seq, +E2F7_HUMAN.H11MO.0.B,E2F7,E2F-related factors,Q96AV8,ChIP-Seq, +E2F8_HUMAN.H11MO.0.D,E2F8,E2F-related factors,A0AVK6,HT-SELEX, +E4F1_HUMAN.H11MO.0.D,E4F1,Factors with multiple dispersed zinc fingers,Q66K89,Integrative, +EGR1_HUMAN.H11MO.0.A,EGR1,Three-zinc finger Krüppel-related factors,P18146,ChIP-Seq, +EGR2_HUMAN.H11MO.0.A,EGR2,Three-zinc finger Krüppel-related factors,P11161,ChIP-Seq, +EGR2_HUMAN.H11MO.1.A,EGR2,Three-zinc finger Krüppel-related factors,P11161,ChIP-Seq, +EGR3_HUMAN.H11MO.0.D,EGR3,Three-zinc finger Krüppel-related factors,Q06889,Integrative, +EGR4_HUMAN.H11MO.0.D,EGR4,Three-zinc finger Krüppel-related factors,Q05215,Integrative, +EHF_HUMAN.H11MO.0.B,EHF,Ets-related factors,Q9NZC4,ChIP-Seq, +ELF1_HUMAN.H11MO.0.A,ELF1,Ets-related factors,P32519,ChIP-Seq, +ELF2_HUMAN.H11MO.0.C,ELF2,Ets-related factors,Q15723,ChIP-Seq, +ELF3_HUMAN.H11MO.0.A,ELF3,Ets-related factors,P78545,ChIP-Seq, +ELF5_HUMAN.H11MO.0.A,ELF5,Ets-related factors,Q9UKW6,ChIP-Seq, +ELK1_HUMAN.H11MO.0.B,ELK1,Ets-related factors,P19419,ChIP-Seq, +ELK3_HUMAN.H11MO.0.D,ELK3,Ets-related factors,P41970,Integrative, +ELK4_HUMAN.H11MO.0.A,ELK4,Ets-related factors,P28324,ChIP-Seq, +EMX1_HUMAN.H11MO.0.D,EMX1,NK-related factors,Q04741,HT-SELEX, +EMX2_HUMAN.H11MO.0.D,EMX2,NK-related factors,Q04743,HT-SELEX, +EOMES_HUMAN.H11MO.0.D,EOMES,TBrain-related factors,O95936,ChIP-Seq, +EPAS1_HUMAN.H11MO.0.B,EPAS1,PAS domain factors,Q99814,ChIP-Seq, +ERG_HUMAN.H11MO.0.A,ERG,Ets-related factors,P11308,ChIP-Seq, +ERR1_HUMAN.H11MO.0.A,ESRRA,Steroid hormone receptors (NR3),P11474,ChIP-Seq, +ERR2_HUMAN.H11MO.0.A,ESRRB,Steroid hormone receptors (NR3),O95718,Integrative, +ERR3_HUMAN.H11MO.0.B,ESRRG,Steroid hormone receptors (NR3),P62508,Integrative, +ESR1_HUMAN.H11MO.0.A,ESR1,Steroid hormone receptors (NR3),P03372,ChIP-Seq, +ESR1_HUMAN.H11MO.1.A,ESR1,Steroid hormone receptors (NR3),P03372,ChIP-Seq, +ESR2_HUMAN.H11MO.0.A,ESR2,Steroid hormone receptors (NR3),Q92731,ChIP-Seq, +ESR2_HUMAN.H11MO.1.A,ESR2,Steroid hormone receptors (NR3),Q92731,ChIP-Seq, +ESX1_HUMAN.H11MO.0.D,ESX1,Paired-related HD factors,Q8N693,HT-SELEX, +ETS1_HUMAN.H11MO.0.A,ETS1,Ets-related factors,P14921,ChIP-Seq, +ETS2_HUMAN.H11MO.0.B,ETS2,Ets-related factors,P15036,ChIP-Seq, +ETV1_HUMAN.H11MO.0.A,ETV1,Ets-related factors,P50549,ChIP-Seq, +ETV2_HUMAN.H11MO.0.B,ETV2,Ets-related factors,O00321,ChIP-Seq, +ETV3_HUMAN.H11MO.0.D,ETV3,Ets-related factors,P41162,HT-SELEX, +ETV4_HUMAN.H11MO.0.B,ETV4,Ets-related factors,P43268,ChIP-Seq, +ETV5_HUMAN.H11MO.0.C,ETV5,Ets-related factors,P41161,ChIP-Seq, +ETV6_HUMAN.H11MO.0.D,ETV6,Ets-related factors,P41212,ChIP-Seq, +ETV7_HUMAN.H11MO.0.D,ETV7,Ets-related factors,Q9Y603,ChIP-Seq, +EVI1_HUMAN.H11MO.0.B,MECOM,Factors with multiple dispersed zinc fingers,Q03112,Integrative, +EVX1_HUMAN.H11MO.0.D,EVX1,HOX-related factors,P49640,ChIP-Seq, +EVX2_HUMAN.H11MO.0.A,EVX2,HOX-related factors,Q03828,ChIP-Seq, +FEV_HUMAN.H11MO.0.B,FEV,Ets-related factors,Q99581,ChIP-Seq, +FEZF1_HUMAN.H11MO.0.C,FEZF1,More than 3 adjacent zinc finger factors,A0PJY2,ChIP-Seq, +FIGLA_HUMAN.H11MO.0.D,FIGLA,Tal-related factors,Q6QHK4,HT-SELEX, +FLI1_HUMAN.H11MO.0.A,FLI1,Ets-related factors,Q01543,ChIP-Seq, +FLI1_HUMAN.H11MO.1.A,FLI1,Ets-related factors,Q01543,ChIP-Seq, +FOS_HUMAN.H11MO.0.A,FOS,Fos-related factors,P01100,ChIP-Seq, +FOSB_HUMAN.H11MO.0.A,FOSB,Fos-related factors,P53539,ChIP-Seq, +FOSL1_HUMAN.H11MO.0.A,FOSL1,Fos-related factors,P15407,ChIP-Seq, +FOSL2_HUMAN.H11MO.0.A,FOSL2,Fos-related factors,P15408,ChIP-Seq, +FOXA1_HUMAN.H11MO.0.A,FOXA1,Forkhead box (FOX) factors,P55317,ChIP-Seq, +FOXA2_HUMAN.H11MO.0.A,FOXA2,Forkhead box (FOX) factors,Q9Y261,ChIP-Seq, +FOXA3_HUMAN.H11MO.0.B,FOXA3,Forkhead box (FOX) factors,P55318,ChIP-Seq, +FOXB1_HUMAN.H11MO.0.D,FOXB1,Forkhead box (FOX) factors,Q99853,HT-SELEX, +FOXC1_HUMAN.H11MO.0.C,FOXC1,Forkhead box (FOX) factors,Q12948,Integrative, +FOXC2_HUMAN.H11MO.0.D,FOXC2,Forkhead box (FOX) factors,Q99958,Integrative, +FOXD1_HUMAN.H11MO.0.D,FOXD1,Forkhead box (FOX) factors,Q16676,Integrative, +FOXD2_HUMAN.H11MO.0.D,FOXD2,Forkhead box (FOX) factors,O60548,HT-SELEX, +FOXD3_HUMAN.H11MO.0.D,FOXD3,Forkhead box (FOX) factors,Q9UJU5,ChIP-Seq, +FOXF1_HUMAN.H11MO.0.D,FOXF1,Forkhead box (FOX) factors,Q12946,Integrative, +FOXF2_HUMAN.H11MO.0.D,FOXF2,Forkhead box (FOX) factors,Q12947,Integrative, +FOXG1_HUMAN.H11MO.0.D,FOXG1,Forkhead box (FOX) factors,P55316,HT-SELEX, +FOXH1_HUMAN.H11MO.0.A,FOXH1,Forkhead box (FOX) factors,O75593,ChIP-Seq, +FOXI1_HUMAN.H11MO.0.B,FOXI1,Forkhead box (FOX) factors,Q12951,Integrative, +FOXJ2_HUMAN.H11MO.0.C,FOXJ2,Forkhead box (FOX) factors,Q9P0K8,Integrative, +FOXJ3_HUMAN.H11MO.0.A,FOXJ3,Forkhead box (FOX) factors,Q9UPW0,Integrative, +FOXJ3_HUMAN.H11MO.1.B,FOXJ3,Forkhead box (FOX) factors,Q9UPW0,Integrative, +FOXK1_HUMAN.H11MO.0.A,FOXK1,Forkhead box (FOX) factors,P85037,ChIP-Seq, +FOXL1_HUMAN.H11MO.0.D,FOXL1,Forkhead box (FOX) factors,Q12952,HT-SELEX, +FOXM1_HUMAN.H11MO.0.A,FOXM1,Forkhead box (FOX) factors,Q08050,ChIP-Seq, +FOXO1_HUMAN.H11MO.0.A,FOXO1,Forkhead box (FOX) factors,Q12778,ChIP-Seq, +FOXO3_HUMAN.H11MO.0.B,FOXO3,Forkhead box (FOX) factors,O43524,ChIP-Seq, +FOXO4_HUMAN.H11MO.0.C,FOXO4,Forkhead box (FOX) factors,P98177,Integrative, +FOXO6_HUMAN.H11MO.0.D,FOXO6,Forkhead box (FOX) factors,A8MYZ6,HT-SELEX, +FOXP1_HUMAN.H11MO.0.A,FOXP1,Forkhead box (FOX) factors,Q9H334,ChIP-Seq, +FOXP2_HUMAN.H11MO.0.C,FOXP2,Forkhead box (FOX) factors,O15409,ChIP-Seq, +FOXP3_HUMAN.H11MO.0.D,FOXP3,Forkhead box (FOX) factors,Q9BZS1,Integrative, +FOXQ1_HUMAN.H11MO.0.C,FOXQ1,Forkhead box (FOX) factors,Q9C009,Integrative, +FUBP1_HUMAN.H11MO.0.D,FUBP1,,Q96AE4,Integrative, +GABPA_HUMAN.H11MO.0.A,GABPA,Ets-related factors,Q06546,ChIP-Seq, +GATA1_HUMAN.H11MO.0.A,GATA1,GATA-type zinc fingers,P15976,ChIP-Seq, +GATA1_HUMAN.H11MO.1.A,GATA1,GATA-type zinc fingers,P15976,ChIP-Seq, +GATA2_HUMAN.H11MO.0.A,GATA2,GATA-type zinc fingers,P23769,ChIP-Seq, +GATA2_HUMAN.H11MO.1.A,GATA2,GATA-type zinc fingers,P23769,ChIP-Seq, +GATA3_HUMAN.H11MO.0.A,GATA3,GATA-type zinc fingers,P23771,ChIP-Seq, +GATA4_HUMAN.H11MO.0.A,GATA4,GATA-type zinc fingers,P43694,ChIP-Seq, +GATA5_HUMAN.H11MO.0.D,GATA5,GATA-type zinc fingers,Q9BWX5,Integrative, +GATA6_HUMAN.H11MO.0.A,GATA6,GATA-type zinc fingers,Q92908,ChIP-Seq, +GBX1_HUMAN.H11MO.0.D,GBX1,HOX-related factors,Q14549,HT-SELEX, +GBX2_HUMAN.H11MO.0.D,GBX2,HOX-related factors,P52951,HT-SELEX, +GCM1_HUMAN.H11MO.0.D,GCM1,GCM factors,Q9NP62,Integrative, +GCM2_HUMAN.H11MO.0.D,GCM2,GCM factors,O75603,HT-SELEX, +GCR_HUMAN.H11MO.0.A,NR3C1,Steroid hormone receptors (NR3),P04150,ChIP-Seq, +GCR_HUMAN.H11MO.1.A,NR3C1,Steroid hormone receptors (NR3),P04150,ChIP-Seq, +GFI1_HUMAN.H11MO.0.C,GFI1,More than 3 adjacent zinc finger factors,Q99684,Integrative, +GFI1B_HUMAN.H11MO.0.A,GFI1B,More than 3 adjacent zinc finger factors,Q5VTD9,ChIP-Seq, +GLI1_HUMAN.H11MO.0.D,GLI1,More than 3 adjacent zinc finger factors,P08151,ChIP-Seq, +GLI2_HUMAN.H11MO.0.D,GLI2,More than 3 adjacent zinc finger factors,P10070,Integrative, +GLI3_HUMAN.H11MO.0.B,GLI3,More than 3 adjacent zinc finger factors,P10071,Integrative, +GLIS1_HUMAN.H11MO.0.D,GLIS1,More than 3 adjacent zinc finger factors,Q8NBF1,HT-SELEX, +GLIS2_HUMAN.H11MO.0.D,GLIS2,More than 3 adjacent zinc finger factors,Q9BZE0,HT-SELEX, +GLIS3_HUMAN.H11MO.0.D,GLIS3,More than 3 adjacent zinc finger factors,Q8NEA6,Integrative, +GMEB2_HUMAN.H11MO.0.D,GMEB2,GMEB,Q9UKD1,HT-SELEX, +GRHL1_HUMAN.H11MO.0.D,GRHL1,Grainyhead-related factors,Q9NZI5,ChIP-Seq, +GRHL2_HUMAN.H11MO.0.A,GRHL2,Grainyhead-related factors,Q6ISB3,ChIP-Seq, +GSC2_HUMAN.H11MO.0.D,GSC2,Paired-related HD factors,O15499,HT-SELEX, +GSC_HUMAN.H11MO.0.D,GSC,Paired-related HD factors,P56915,HT-SELEX, +GSX1_HUMAN.H11MO.0.D,GSX1,HOX-related factors,Q9H4S2,HT-SELEX, +GSX2_HUMAN.H11MO.0.D,GSX2,HOX-related factors,Q9BZM3,HT-SELEX, +HAND1_HUMAN.H11MO.0.D,HAND1,Tal-related factors,O96004,ChIP-Seq, +HAND1_HUMAN.H11MO.1.D,HAND1,Tal-related factors,O96004,ChIP-Seq, +HBP1_HUMAN.H11MO.0.D,HBP1,SOX-related factors,O60381,Integrative, +HEN1_HUMAN.H11MO.0.C,NHLH1,Tal-related factors,Q02575,Integrative, +HES1_HUMAN.H11MO.0.D,HES1,Hairy-related factors,Q14469,Integrative, +HES5_HUMAN.H11MO.0.D,HES5,Hairy-related factors,Q5TA89,HT-SELEX, +HES7_HUMAN.H11MO.0.D,HES7,Hairy-related factors,Q9BYE0,HT-SELEX, +HESX1_HUMAN.H11MO.0.D,HESX1,Paired-related HD factors,Q9UBX0,Integrative, +HEY1_HUMAN.H11MO.0.D,HEY1,Hairy-related factors,Q9Y5J3,HT-SELEX, +HEY2_HUMAN.H11MO.0.D,HEY2,Hairy-related factors,Q9UBP5,Integrative, +HIC1_HUMAN.H11MO.0.C,HIC1,Factors with multiple dispersed zinc fingers,Q14526,Integrative, +HIC2_HUMAN.H11MO.0.D,HIC2,Factors with multiple dispersed zinc fingers,Q96JB3,HT-SELEX, +HIF1A_HUMAN.H11MO.0.C,HIF1A,PAS domain factors,Q16665,ChIP-Seq, +HINFP_HUMAN.H11MO.0.C,HINFP,Factors with multiple dispersed zinc fingers,Q9BQA5,Integrative, +HLF_HUMAN.H11MO.0.C,HLF,C/EBP-related,Q16534,Integrative, +HLTF_HUMAN.H11MO.0.D,HLTF,,Q14527,Integrative, +HMBX1_HUMAN.H11MO.0.D,HMBOX1,POU domain factors,Q6NT76,ChIP-Seq, +HME1_HUMAN.H11MO.0.D,EN1,NK-related factors,Q05925,HT-SELEX, +HME2_HUMAN.H11MO.0.D,EN2,NK-related factors,P19622,HT-SELEX, +HMGA1_HUMAN.H11MO.0.D,HMGA1,HMGA factors,P17096,Integrative, +HMGA2_HUMAN.H11MO.0.D,HMGA2,HMGA factors,P52926,Integrative, +HMX1_HUMAN.H11MO.0.D,HMX1,NK-related factors,Q9NP08,HT-SELEX, +HMX2_HUMAN.H11MO.0.D,HMX2,NK-related factors,A2RU54,HT-SELEX, +HMX3_HUMAN.H11MO.0.D,HMX3,NK-related factors,A6NHT5,HT-SELEX, +HNF1A_HUMAN.H11MO.0.C,HNF1A,POU domain factors,P20823,ChIP-Seq, +HNF1B_HUMAN.H11MO.0.A,HNF1B,POU domain factors,P35680,ChIP-Seq, +HNF1B_HUMAN.H11MO.1.A,HNF1B,POU domain factors,P35680,ChIP-Seq, +HNF4A_HUMAN.H11MO.0.A,HNF4A,RXR-related receptors (NR2),P41235,ChIP-Seq, +HNF4G_HUMAN.H11MO.0.B,HNF4G,RXR-related receptors (NR2),Q14541,ChIP-Seq, +HNF6_HUMAN.H11MO.0.B,ONECUT1,HD-CUT factors,Q9UBC0,ChIP-Seq, +HOMEZ_HUMAN.H11MO.0.D,HOMEZ,HD-ZF factors,Q8IX15,HT-SELEX, +HSF1_HUMAN.H11MO.0.A,HSF1,HSF factors,Q00613,ChIP-Seq, +HSF1_HUMAN.H11MO.1.A,HSF1,HSF factors,Q00613,ChIP-Seq, +HSF2_HUMAN.H11MO.0.A,HSF2,HSF factors,Q03933,Integrative, +HSF4_HUMAN.H11MO.0.D,HSF4,HSF factors,Q9ULV5,HT-SELEX, +HSFY1_HUMAN.H11MO.0.D,HSFY1; HSFY2,HSF factors,Q96LI6,HT-SELEX, +HTF4_HUMAN.H11MO.0.A,TCF12,E2A-related factors,Q99081,ChIP-Seq, +HXA1_HUMAN.H11MO.0.C,HOXA1,HOX-related factors,P49639,Integrative, +HXA2_HUMAN.H11MO.0.D,HOXA2,HOX-related factors,O43364,HT-SELEX, +HXA5_HUMAN.H11MO.0.D,HOXA5,HOX-related factors,P20719,Integrative, +HXA7_HUMAN.H11MO.0.D,HOXA7,HOX-related factors,P31268,Integrative, +HXA9_HUMAN.H11MO.0.B,HOXA9,HOX-related factors,P31269,ChIP-Seq, +HXA10_HUMAN.H11MO.0.C,HOXA10,HOX-related factors,P31260,Integrative, +HXA11_HUMAN.H11MO.0.D,HOXA11,HOX-related factors,P31270,HT-SELEX, +HXA13_HUMAN.H11MO.0.C,HOXA13,HOX-related factors,P31271,Integrative, +HXB1_HUMAN.H11MO.0.D,HOXB1,HOX-related factors,P14653,Integrative, +HXB2_HUMAN.H11MO.0.D,HOXB2,HOX-related factors,P14652,HT-SELEX, +HXB3_HUMAN.H11MO.0.D,HOXB3,HOX-related factors,P14651,HT-SELEX, +HXB4_HUMAN.H11MO.0.B,HOXB4,HOX-related factors,P17483,ChIP-Seq, +HXB6_HUMAN.H11MO.0.D,HOXB6,HOX-related factors,P17509,Integrative, +HXB7_HUMAN.H11MO.0.C,HOXB7,HOX-related factors,P09629,Integrative, +HXB8_HUMAN.H11MO.0.C,HOXB8,HOX-related factors,P17481,Integrative, +HXB13_HUMAN.H11MO.0.A,HOXB13,HOX-related factors,Q92826,ChIP-Seq, +HXC6_HUMAN.H11MO.0.D,HOXC6,HOX-related factors,P09630,Integrative, +HXC8_HUMAN.H11MO.0.D,HOXC8,HOX-related factors,P31273,Integrative, +HXC9_HUMAN.H11MO.0.C,HOXC9,HOX-related factors,P31274,ChIP-Seq, +HXC10_HUMAN.H11MO.0.D,HOXC10,HOX-related factors,Q9NYD6,HT-SELEX, +HXC11_HUMAN.H11MO.0.D,HOXC11,HOX-related factors,O43248,HT-SELEX, +HXC12_HUMAN.H11MO.0.D,HOXC12,HOX-related factors,P31275,HT-SELEX, +HXC13_HUMAN.H11MO.0.D,HOXC13,HOX-related factors,P31276,HT-SELEX, +HXD3_HUMAN.H11MO.0.D,HOXD3,HOX-related factors,P31249,HT-SELEX, +HXD4_HUMAN.H11MO.0.D,HOXD4,HOX-related factors,P09016,Integrative, +HXD8_HUMAN.H11MO.0.D,HOXD8,HOX-related factors,P13378,HT-SELEX, +HXD9_HUMAN.H11MO.0.D,HOXD9,HOX-related factors,P28356,Integrative, +HXD10_HUMAN.H11MO.0.D,HOXD10,HOX-related factors,P28358,Integrative, +HXD11_HUMAN.H11MO.0.D,HOXD11,HOX-related factors,P31277,HT-SELEX, +HXD12_HUMAN.H11MO.0.D,HOXD12,HOX-related factors,P35452,HT-SELEX, +HXD13_HUMAN.H11MO.0.D,HOXD13,HOX-related factors,P35453,Integrative, +ID4_HUMAN.H11MO.0.D,ID4,HLH domain only,P47928,HT-SELEX, +IKZF1_HUMAN.H11MO.0.C,IKZF1,Factors with multiple dispersed zinc fingers,Q13422,Integrative, +INSM1_HUMAN.H11MO.0.C,INSM1,Factors with multiple dispersed zinc fingers,Q01101,Integrative, +IRF1_HUMAN.H11MO.0.A,IRF1,Interferon-regulatory factors,P10914,ChIP-Seq, +IRF2_HUMAN.H11MO.0.A,IRF2,Interferon-regulatory factors,P14316,ChIP-Seq, +IRF3_HUMAN.H11MO.0.B,IRF3,Interferon-regulatory factors,Q14653,ChIP-Seq, +IRF4_HUMAN.H11MO.0.A,IRF4,Interferon-regulatory factors,Q15306,ChIP-Seq, +IRF5_HUMAN.H11MO.0.D,IRF5,Interferon-regulatory factors,Q13568,Integrative, +IRF7_HUMAN.H11MO.0.C,IRF7,Interferon-regulatory factors,Q92985,Integrative, +IRF8_HUMAN.H11MO.0.B,IRF8,Interferon-regulatory factors,Q02556,ChIP-Seq, +IRF9_HUMAN.H11MO.0.C,IRF9,Interferon-regulatory factors,Q00978,Integrative, +IRX2_HUMAN.H11MO.0.D,IRX2,TALE-type homeo domain factors,Q9BZI1,HT-SELEX, +IRX3_HUMAN.H11MO.0.D,IRX3,TALE-type homeo domain factors,P78415,HT-SELEX, +ISL1_HUMAN.H11MO.0.A,ISL1,HD-LIM factors,P61371,ChIP-Seq, +ISL2_HUMAN.H11MO.0.D,ISL2,HD-LIM factors,Q96A47,HT-SELEX, +ISX_HUMAN.H11MO.0.D,ISX,Paired-related HD factors,Q2M1V0,HT-SELEX, +ITF2_HUMAN.H11MO.0.C,TCF4,E2A-related factors,P15884,ChIP-Seq, +JDP2_HUMAN.H11MO.0.D,JDP2,Fos-related factors,Q8WYK2,HT-SELEX, +JUN_HUMAN.H11MO.0.A,JUN,Jun-related factors,P05412,ChIP-Seq, +JUNB_HUMAN.H11MO.0.A,JUNB,Jun-related factors,P17275,ChIP-Seq, +JUND_HUMAN.H11MO.0.A,JUND,Jun-related factors,P17535,ChIP-Seq, +KAISO_HUMAN.H11MO.0.A,ZBTB33,Other factors with up to three adjacent zinc fingers,Q86T24,ChIP-Seq, +KAISO_HUMAN.H11MO.1.A,ZBTB33,Other factors with up to three adjacent zinc fingers,Q86T24,ChIP-Seq, +KAISO_HUMAN.H11MO.2.A,ZBTB33,Other factors with up to three adjacent zinc fingers,Q86T24,Integrative, +KLF1_HUMAN.H11MO.0.A,KLF1,Three-zinc finger Krüppel-related factors,Q13351,ChIP-Seq, +KLF3_HUMAN.H11MO.0.B,KLF3,Three-zinc finger Krüppel-related factors,P57682,ChIP-Seq, +KLF4_HUMAN.H11MO.0.A,KLF4,Three-zinc finger Krüppel-related factors,O43474,ChIP-Seq, +KLF5_HUMAN.H11MO.0.A,KLF5,Three-zinc finger Krüppel-related factors,Q13887,ChIP-Seq, +KLF6_HUMAN.H11MO.0.A,KLF6,Three-zinc finger Krüppel-related factors,Q99612,ChIP-Seq, +KLF8_HUMAN.H11MO.0.C,KLF8,Three-zinc finger Krüppel-related factors,O95600,Integrative, +KLF9_HUMAN.H11MO.0.C,KLF9,Three-zinc finger Krüppel-related factors,Q13886,ChIP-Seq, +KLF12_HUMAN.H11MO.0.C,KLF12,Three-zinc finger Krüppel-related factors,Q9Y4X4,ChIP-Seq, +KLF13_HUMAN.H11MO.0.D,KLF13,Three-zinc finger Krüppel-related factors,Q9Y2Y9,HT-SELEX, +KLF14_HUMAN.H11MO.0.D,KLF14,Three-zinc finger Krüppel-related factors,Q8TD94,HT-SELEX, +KLF15_HUMAN.H11MO.0.A,KLF15,Three-zinc finger Krüppel-related factors,Q9UIH9,ChIP-Seq, +KLF16_HUMAN.H11MO.0.D,KLF16,Three-zinc finger Krüppel-related factors,Q9BXK1,HT-SELEX, +LBX2_HUMAN.H11MO.0.D,LBX2,NK-related factors,Q6XYB7,HT-SELEX, +LEF1_HUMAN.H11MO.0.A,LEF1,TCF-7-related factors,Q9UJU2,ChIP-Seq, +LHX2_HUMAN.H11MO.0.A,LHX2,HD-LIM factors,P50458,ChIP-Seq, +LHX3_HUMAN.H11MO.0.C,LHX3,HD-LIM factors,Q9UBR4,Integrative, +LHX4_HUMAN.H11MO.0.D,LHX4,HD-LIM factors,Q969G2,HT-SELEX, +LHX6_HUMAN.H11MO.0.D,LHX6,HD-LIM factors,Q9UPM6,ChIP-Seq, +LHX8_HUMAN.H11MO.0.D,LHX8,HD-LIM factors,Q68G74,HT-SELEX, +LHX9_HUMAN.H11MO.0.D,LHX9,HD-LIM factors,Q9NQ69,HT-SELEX, +LMX1A_HUMAN.H11MO.0.D,LMX1A,HD-LIM factors,Q8TE12,HT-SELEX, +LMX1B_HUMAN.H11MO.0.D,LMX1B,HD-LIM factors,O60663,HT-SELEX, +LYL1_HUMAN.H11MO.0.A,LYL1,Tal-related factors,P12980,ChIP-Seq, +MAF_HUMAN.H11MO.0.A,MAF,Maf-related factors,O75444,ChIP-Seq, +MAF_HUMAN.H11MO.1.B,MAF,Maf-related factors,O75444,ChIP-Seq, +MAFA_HUMAN.H11MO.0.D,MAFA,Maf-related factors,Q8NHW3,Integrative, +MAFB_HUMAN.H11MO.0.B,MAFB,Maf-related factors,Q9Y5Q3,ChIP-Seq, +MAFF_HUMAN.H11MO.0.B,MAFF,Maf-related factors,Q9ULX9,ChIP-Seq, +MAFF_HUMAN.H11MO.1.B,MAFF,Maf-related factors,Q9ULX9,ChIP-Seq, +MAFG_HUMAN.H11MO.0.A,MAFG,Maf-related factors,O15525,ChIP-Seq, +MAFG_HUMAN.H11MO.1.A,MAFG,Maf-related factors,O15525,ChIP-Seq, +MAFK_HUMAN.H11MO.0.A,MAFK,Maf-related factors,O60675,ChIP-Seq, +MAFK_HUMAN.H11MO.1.A,MAFK,Maf-related factors,O60675,ChIP-Seq, +MAX_HUMAN.H11MO.0.A,MAX,bHLH-ZIP factors,P61244,ChIP-Seq, +MAZ_HUMAN.H11MO.0.A,MAZ,Factors with multiple dispersed zinc fingers,P56270,ChIP-Seq, +MAZ_HUMAN.H11MO.1.A,MAZ,Factors with multiple dispersed zinc fingers,P56270,ChIP-Seq, +MBD2_HUMAN.H11MO.0.B,MBD2,,Q9UBB5,Integrative, +MCR_HUMAN.H11MO.0.D,NR3C2,Steroid hormone receptors (NR3),P08235,Integrative, +MECP2_HUMAN.H11MO.0.C,MECP2,,P51608,Integrative, +MEF2A_HUMAN.H11MO.0.A,MEF2A,Regulators of differentiation,Q02078,ChIP-Seq, +MEF2B_HUMAN.H11MO.0.A,MEF2B,Regulators of differentiation,Q02080,ChIP-Seq, +MEF2C_HUMAN.H11MO.0.A,MEF2C,Regulators of differentiation,Q06413,ChIP-Seq, +MEF2D_HUMAN.H11MO.0.A,MEF2D,Regulators of differentiation,Q14814,ChIP-Seq, +MEIS1_HUMAN.H11MO.0.A,MEIS1,TALE-type homeo domain factors,O00470,ChIP-Seq, +MEIS1_HUMAN.H11MO.1.B,MEIS1,TALE-type homeo domain factors,O00470,ChIP-Seq, +MEIS2_HUMAN.H11MO.0.B,MEIS2,TALE-type homeo domain factors,O14770,Integrative, +MEIS3_HUMAN.H11MO.0.D,MEIS3,TALE-type homeo domain factors,Q99687,HT-SELEX, +MEOX1_HUMAN.H11MO.0.D,MEOX1,HOX-related factors,P50221,HT-SELEX, +MEOX2_HUMAN.H11MO.0.D,MEOX2,HOX-related factors,P50222,HT-SELEX, +MESP1_HUMAN.H11MO.0.D,MESP1,Tal-related factors,Q9BRJ9,HT-SELEX, +MGAP_HUMAN.H11MO.0.D,MGA,bHLH-ZIP factors,Q8IWI9,HT-SELEX, +MITF_HUMAN.H11MO.0.A,MITF,bHLH-ZIP factors,O75030,ChIP-Seq, +MIXL1_HUMAN.H11MO.0.D,MIXL1,Paired-related HD factors,Q9H2W2,HT-SELEX, +MLX_HUMAN.H11MO.0.D,MLX,bHLH-ZIP factors,Q9UH92,HT-SELEX, +MLXPL_HUMAN.H11MO.0.D,MLXIPL,bHLH-ZIP factors,Q9NP71,Integrative, +MNX1_HUMAN.H11MO.0.D,MNX1,HOX-related factors,P50219,HT-SELEX, +MSX1_HUMAN.H11MO.0.D,MSX1,NK-related factors,P28360,HT-SELEX, +MSX2_HUMAN.H11MO.0.D,MSX2,NK-related factors,P35548,Integrative, +MTF1_HUMAN.H11MO.0.C,MTF1,More than 3 adjacent zinc finger factors,Q14872,Integrative, +MXI1_HUMAN.H11MO.0.A,MXI1,bHLH-ZIP factors,P50539,ChIP-Seq, +MXI1_HUMAN.H11MO.1.A,MXI1,bHLH-ZIP factors,P50539,ChIP-Seq, +MYB_HUMAN.H11MO.0.A,MYB,Myb/SANT domain factors,P10242,ChIP-Seq, +MYBA_HUMAN.H11MO.0.D,MYBL1,Myb/SANT domain factors,P10243,ChIP-Seq, +MYBB_HUMAN.H11MO.0.D,MYBL2,Myb/SANT domain factors,P10244,Integrative, +MYC_HUMAN.H11MO.0.A,MYC,bHLH-ZIP factors,P01106,ChIP-Seq, +MYCN_HUMAN.H11MO.0.A,MYCN,bHLH-ZIP factors,P04198,ChIP-Seq, +MYF6_HUMAN.H11MO.0.C,MYF6,MyoD / ASC-related factors,P23409,Integrative, +MYNN_HUMAN.H11MO.0.D,MYNN,More than 3 adjacent zinc finger factors,Q9NPC7,ChIP-Seq, +MYOD1_HUMAN.H11MO.0.A,MYOD1,MyoD / ASC-related factors,P15172,ChIP-Seq, +MYOD1_HUMAN.H11MO.1.A,MYOD1,MyoD / ASC-related factors,P15172,ChIP-Seq, +MYOG_HUMAN.H11MO.0.B,MYOG,MyoD / ASC-related factors,P15173,ChIP-Seq, +MZF1_HUMAN.H11MO.0.B,MZF1,More than 3 adjacent zinc finger factors,P28698,ChIP-Seq, +NANOG_HUMAN.H11MO.0.A,NANOG,NK-related factors,Q9H9S0,ChIP-Seq, +NANOG_HUMAN.H11MO.1.B,NANOG,NK-related factors,Q9H9S0,ChIP-Seq, +NDF1_HUMAN.H11MO.0.A,NEUROD1,Tal-related factors,Q13562,ChIP-Seq, +NDF2_HUMAN.H11MO.0.B,NEUROD2,Tal-related factors,Q15784,ChIP-Seq, +NF2L1_HUMAN.H11MO.0.C,NFE2L1,Jun-related factors,Q14494,Integrative, +NF2L2_HUMAN.H11MO.0.A,NFE2L2,Jun-related factors,Q16236,ChIP-Seq, +NFAC1_HUMAN.H11MO.0.B,NFATC1,NFAT-related factors,O95644,ChIP-Seq, +NFAC1_HUMAN.H11MO.1.B,NFATC1,NFAT-related factors,O95644,ChIP-Seq, +NFAC2_HUMAN.H11MO.0.B,NFATC2,NFAT-related factors,Q13469,Integrative, +NFAC3_HUMAN.H11MO.0.B,NFATC3,NFAT-related factors,Q12968,Integrative, +NFAC4_HUMAN.H11MO.0.C,NFATC4,NFAT-related factors,Q14934,Integrative, +NFAT5_HUMAN.H11MO.0.D,NFAT5,NFAT-related factors,O94916,ChIP-Seq, +NFE2_HUMAN.H11MO.0.A,NFE2,Jun-related factors,Q16621,ChIP-Seq, +NFIA_HUMAN.H11MO.0.C,NFIA,Nuclear factor 1,Q12857,Integrative, +NFIA_HUMAN.H11MO.1.D,NFIA,Nuclear factor 1,Q12857,Integrative, +NFIB_HUMAN.H11MO.0.D,NFIB,Nuclear factor 1,O00712,ChIP-Seq, +NFIC_HUMAN.H11MO.0.A,NFIC,Nuclear factor 1,P08651,ChIP-Seq, +NFIC_HUMAN.H11MO.1.A,NFIC,Nuclear factor 1,P08651,ChIP-Seq, +NFIL3_HUMAN.H11MO.0.D,NFIL3,C/EBP-related,Q16649,ChIP-Seq, +NFKB1_HUMAN.H11MO.0.A,NFKB1,NF-kappaB-related factors,P19838,ChIP-Seq, +NFKB2_HUMAN.H11MO.0.B,NFKB2,NF-kappaB-related factors,Q00653,ChIP-Seq, +NFYA_HUMAN.H11MO.0.A,NFYA,Heteromeric CCAAT-binding factors,P23511,ChIP-Seq, +NFYB_HUMAN.H11MO.0.A,NFYB,Heteromeric CCAAT-binding factors,P25208,Integrative, +NFYC_HUMAN.H11MO.0.A,NFYC,Heteromeric CCAAT-binding factors,Q13952,ChIP-Seq, +NGN2_HUMAN.H11MO.0.D,NEUROG2,Tal-related factors,Q9H2A3,ChIP-Seq, +NKX21_HUMAN.H11MO.0.A,NKX2-1,NK-related factors,P43699,ChIP-Seq, +NKX22_HUMAN.H11MO.0.D,NKX2-2,NK-related factors,O95096,ChIP-Seq, +NKX23_HUMAN.H11MO.0.D,NKX2-3,NK-related factors,Q8TAU0,HT-SELEX, +NKX25_HUMAN.H11MO.0.B,NKX2-5,NK-related factors,P52952,ChIP-Seq, +NKX28_HUMAN.H11MO.0.C,NKX2-8,NK-related factors,O15522,Integrative, +NKX31_HUMAN.H11MO.0.C,NKX3-1,NK-related factors,Q99801,ChIP-Seq, +NKX32_HUMAN.H11MO.0.C,NKX3-2,NK-related factors,P78367,ChIP-Seq, +NKX61_HUMAN.H11MO.0.B,NKX6-1,NK-related factors,P78426,ChIP-Seq, +NKX61_HUMAN.H11MO.1.B,NKX6-1,NK-related factors,P78426,ChIP-Seq, +NKX62_HUMAN.H11MO.0.D,NKX6-2,NK-related factors,Q9C056,HT-SELEX, +NOBOX_HUMAN.H11MO.0.C,NOBOX,Paired-related HD factors,O60393,Integrative, +NOTO_HUMAN.H11MO.0.D,NOTO,NK-related factors,A8MTQ0,HT-SELEX, +NR0B1_HUMAN.H11MO.0.D,NR0B1,DAX-related receptors (NR0),P51843,Integrative, +NR1D1_HUMAN.H11MO.0.B,NR1D1,Thyroid hormone receptor-related factors (NR1),P20393,ChIP-Seq, +NR1D1_HUMAN.H11MO.1.D,NR1D1,Thyroid hormone receptor-related factors (NR1),P20393,Integrative, +NR1H2_HUMAN.H11MO.0.D,NR1H2,Thyroid hormone receptor-related factors (NR1),P55055,Integrative, +NR1H3_HUMAN.H11MO.0.B,NR1H3,Thyroid hormone receptor-related factors (NR1),Q13133,ChIP-Seq, +NR1H3_HUMAN.H11MO.1.B,NR1H3,Thyroid hormone receptor-related factors (NR1),Q13133,ChIP-Seq, +NR1H4_HUMAN.H11MO.0.B,NR1H4,Thyroid hormone receptor-related factors (NR1),Q96RI1,ChIP-Seq, +NR1H4_HUMAN.H11MO.1.B,NR1H4,Thyroid hormone receptor-related factors (NR1),Q96RI1,ChIP-Seq, +NR1I2_HUMAN.H11MO.0.C,NR1I2,Thyroid hormone receptor-related factors (NR1),O75469,Integrative, +NR1I2_HUMAN.H11MO.1.D,NR1I2,Thyroid hormone receptor-related factors (NR1),O75469,Integrative, +NR1I3_HUMAN.H11MO.0.C,NR1I3,Thyroid hormone receptor-related factors (NR1),Q14994,Integrative, +NR1I3_HUMAN.H11MO.1.D,NR1I3,Thyroid hormone receptor-related factors (NR1),Q14994,Integrative, +NR2C1_HUMAN.H11MO.0.C,NR2C1,RXR-related receptors (NR2),P13056,Integrative, +NR2C2_HUMAN.H11MO.0.B,NR2C2,RXR-related receptors (NR2),P49116,ChIP-Seq, +NR2E1_HUMAN.H11MO.0.D,NR2E1,RXR-related receptors (NR2),Q9Y466,HT-SELEX, +NR2E3_HUMAN.H11MO.0.C,NR2E3,RXR-related receptors (NR2),Q9Y5X4,Integrative, +NR2F6_HUMAN.H11MO.0.D,NR2F6,RXR-related receptors (NR2),P10588,Integrative, +NR4A1_HUMAN.H11MO.0.A,NR4A1,NGFI-B-related receptors (NR4),P22736,ChIP-Seq, +NR4A2_HUMAN.H11MO.0.C,NR4A2,NGFI-B-related receptors (NR4),P43354,Integrative, +NR4A3_HUMAN.H11MO.0.D,NR4A3,NGFI-B-related receptors (NR4),Q92570,Integrative, +NR5A2_HUMAN.H11MO.0.B,NR5A2,FTZ-F1-related receptors (NR5),O00482,ChIP-Seq, +NR6A1_HUMAN.H11MO.0.B,NR6A1,GCNF-related receptors (NR6),Q15406,Integrative, +NRF1_HUMAN.H11MO.0.A,NRF1,NRF,Q16656,ChIP-Seq, +NRL_HUMAN.H11MO.0.D,NRL,Maf-related factors,P54845,HT-SELEX, +OLIG1_HUMAN.H11MO.0.D,OLIG1,Tal-related factors,Q8TAK6,HT-SELEX, +OLIG2_HUMAN.H11MO.0.B,OLIG2,Tal-related factors,Q13516,ChIP-Seq, +OLIG2_HUMAN.H11MO.1.B,OLIG2,Tal-related factors,Q13516,ChIP-Seq, +OLIG3_HUMAN.H11MO.0.D,OLIG3,Tal-related factors,Q7RTU3,HT-SELEX, +ONEC2_HUMAN.H11MO.0.D,ONECUT2,HD-CUT factors,O95948,Integrative, +ONEC3_HUMAN.H11MO.0.D,ONECUT3,HD-CUT factors,O60422,HT-SELEX, +OSR2_HUMAN.H11MO.0.C,OSR2,More than 3 adjacent zinc finger factors,Q8N2R0,ChIP-Seq, +OTX1_HUMAN.H11MO.0.D,OTX1,Paired-related HD factors,P32242,Integrative, +OTX2_HUMAN.H11MO.0.A,OTX2,Paired-related HD factors,P32243,ChIP-Seq, +OVOL1_HUMAN.H11MO.0.C,OVOL1,More than 3 adjacent zinc finger factors,O14753,Integrative, +OVOL2_HUMAN.H11MO.0.D,OVOL2,More than 3 adjacent zinc finger factors,Q9BRP0,ChIP-Seq, +OZF_HUMAN.H11MO.0.C,ZNF146,More than 3 adjacent zinc finger factors,Q15072,ChIP-Seq, +P5F1B_HUMAN.H11MO.0.D,POU5F1B,POU domain factors,Q06416,HT-SELEX, +P53_HUMAN.H11MO.0.A,TP53,p53-related factors,P04637,ChIP-Seq, +P53_HUMAN.H11MO.1.A,TP53,p53-related factors,P04637,ChIP-Seq, +P63_HUMAN.H11MO.0.A,TP63,p53-related factors,Q9H3D4,ChIP-Seq, +P63_HUMAN.H11MO.1.A,TP63,p53-related factors,Q9H3D4,ChIP-Seq, +P73_HUMAN.H11MO.0.A,TP73,p53-related factors,O15350,ChIP-Seq, +P73_HUMAN.H11MO.1.A,TP73,p53-related factors,O15350,ChIP-Seq, +PATZ1_HUMAN.H11MO.0.C,PATZ1,Factors with multiple dispersed zinc fingers,Q9HBE1,ChIP-Seq, +PATZ1_HUMAN.H11MO.1.C,PATZ1,Factors with multiple dispersed zinc fingers,Q9HBE1,ChIP-Seq, +PAX1_HUMAN.H11MO.0.D,PAX1,Paired domain only,P15863,HT-SELEX, +PAX2_HUMAN.H11MO.0.D,PAX2,Paired domain only,Q02962,Integrative, +PAX3_HUMAN.H11MO.0.D,PAX3,Paired plus homeo domain,P23760,HT-SELEX, +PAX4_HUMAN.H11MO.0.D,PAX4,Paired plus homeo domain,O43316,HT-SELEX, +PAX5_HUMAN.H11MO.0.A,PAX5,Paired domain only,Q02548,ChIP-Seq, +PAX6_HUMAN.H11MO.0.C,PAX6,Paired plus homeo domain,P26367,ChIP-Seq, +PAX7_HUMAN.H11MO.0.D,PAX7,Paired plus homeo domain,P23759,HT-SELEX, +PAX8_HUMAN.H11MO.0.D,PAX8,Paired domain only,Q06710,Integrative, +PBX1_HUMAN.H11MO.0.A,PBX1,TALE-type homeo domain factors,P40424,ChIP-Seq, +PBX1_HUMAN.H11MO.1.C,PBX1,TALE-type homeo domain factors,P40424,Integrative, +PBX2_HUMAN.H11MO.0.C,PBX2,TALE-type homeo domain factors,P40425,Integrative, +PBX3_HUMAN.H11MO.0.A,PBX3,TALE-type homeo domain factors,P40426,ChIP-Seq, +PBX3_HUMAN.H11MO.1.A,PBX3,TALE-type homeo domain factors,P40426,ChIP-Seq, +PDX1_HUMAN.H11MO.0.A,PDX1,HOX-related factors,P52945,ChIP-Seq, +PDX1_HUMAN.H11MO.1.A,PDX1,HOX-related factors,P52945,ChIP-Seq, +PEBB_HUMAN.H11MO.0.C,CBFB,,Q13951,Integrative, +PHX2A_HUMAN.H11MO.0.D,PHOX2A,Paired-related HD factors,O14813,HT-SELEX, +PHX2B_HUMAN.H11MO.0.D,PHOX2B,Paired-related HD factors,Q99453,HT-SELEX, +PIT1_HUMAN.H11MO.0.C,POU1F1,POU domain factors,P28069,Integrative, +PITX1_HUMAN.H11MO.0.D,PITX1,Paired-related HD factors,P78337,ChIP-Seq, +PITX2_HUMAN.H11MO.0.D,PITX2,Paired-related HD factors,Q99697,Integrative, +PITX3_HUMAN.H11MO.0.D,PITX3,Paired-related HD factors,O75364,HT-SELEX, +PKNX1_HUMAN.H11MO.0.B,PKNOX1,TALE-type homeo domain factors,P55347,ChIP-Seq, +PLAG1_HUMAN.H11MO.0.D,PLAG1,More than 3 adjacent zinc finger factors,Q6DJT9,Integrative, +PLAL1_HUMAN.H11MO.0.D,PLAGL1,More than 3 adjacent zinc finger factors,Q9UM63,Integrative, +PO2F1_HUMAN.H11MO.0.C,POU2F1,POU domain factors,P14859,ChIP-Seq, +PO2F2_HUMAN.H11MO.0.A,POU2F2,POU domain factors,P09086,ChIP-Seq, +PO2F3_HUMAN.H11MO.0.D,POU2F3,POU domain factors,Q9UKI9,HT-SELEX, +PO3F1_HUMAN.H11MO.0.C,POU3F1,POU domain factors,Q03052,Integrative, +PO3F2_HUMAN.H11MO.0.A,POU3F2,POU domain factors,P20265,ChIP-Seq, +PO3F3_HUMAN.H11MO.0.D,POU3F3,POU domain factors,P20264,HT-SELEX, +PO3F4_HUMAN.H11MO.0.D,POU3F4,POU domain factors,P49335,HT-SELEX, +PO4F1_HUMAN.H11MO.0.D,POU4F1,POU domain factors,Q01851,HT-SELEX, +PO4F2_HUMAN.H11MO.0.D,POU4F2,POU domain factors,Q12837,Integrative, +PO4F3_HUMAN.H11MO.0.D,POU4F3,POU domain factors,Q15319,HT-SELEX, +PO5F1_HUMAN.H11MO.0.A,POU5F1,POU domain factors,Q01860,ChIP-Seq, +PO5F1_HUMAN.H11MO.1.A,POU5F1,POU domain factors,Q01860,ChIP-Seq, +PO6F1_HUMAN.H11MO.0.D,POU6F1,POU domain factors,Q14863,Integrative, +PO6F2_HUMAN.H11MO.0.D,POU6F2,POU domain factors,P78424,HT-SELEX, +PPARA_HUMAN.H11MO.0.B,PPARA,Thyroid hormone receptor-related factors (NR1),Q07869,ChIP-Seq, +PPARA_HUMAN.H11MO.1.B,PPARA,Thyroid hormone receptor-related factors (NR1),Q07869,ChIP-Seq, +PPARD_HUMAN.H11MO.0.D,PPARD,Thyroid hormone receptor-related factors (NR1),Q03181,Integrative, +PPARG_HUMAN.H11MO.0.A,PPARG,Thyroid hormone receptor-related factors (NR1),P37231,ChIP-Seq, +PPARG_HUMAN.H11MO.1.A,PPARG,Thyroid hormone receptor-related factors (NR1),P37231,ChIP-Seq, +PRD14_HUMAN.H11MO.0.A,PRDM14,More than 3 adjacent zinc finger factors,Q9GZV8,ChIP-Seq, +PRDM1_HUMAN.H11MO.0.A,PRDM1,More than 3 adjacent zinc finger factors,O75626,ChIP-Seq, +PRDM4_HUMAN.H11MO.0.D,PRDM4,Factors with multiple dispersed zinc fingers,Q9UKN5,HT-SELEX, +PRDM6_HUMAN.H11MO.0.C,PRDM6,More than 3 adjacent zinc finger factors,Q9NQX0,ChIP-Seq, +PRGR_HUMAN.H11MO.0.A,PGR,Steroid hormone receptors (NR3),P06401,ChIP-Seq, +PRGR_HUMAN.H11MO.1.A,PGR,Steroid hormone receptors (NR3),P06401,ChIP-Seq, +PROP1_HUMAN.H11MO.0.D,PROP1,Paired-related HD factors,O75360,ChIP-Seq, +PROX1_HUMAN.H11MO.0.D,PROX1,HD-PROS factors,Q92786,HT-SELEX, +PRRX1_HUMAN.H11MO.0.D,PRRX1,Paired-related HD factors,P54821,Integrative, +PRRX2_HUMAN.H11MO.0.C,PRRX2,Paired-related HD factors,Q99811,Integrative, +PTF1A_HUMAN.H11MO.0.B,PTF1A,Tal-related factors,Q7RTS3,ChIP-Seq, +PTF1A_HUMAN.H11MO.1.B,PTF1A,Tal-related factors,Q7RTS3,ChIP-Seq, +PURA_HUMAN.H11MO.0.D,PURA,PUR,Q00577,Integrative, +RARA_HUMAN.H11MO.0.A,RARA,Thyroid hormone receptor-related factors (NR1),P10276,ChIP-Seq, +RARA_HUMAN.H11MO.1.A,RARA,Thyroid hormone receptor-related factors (NR1),P10276,ChIP-Seq, +RARA_HUMAN.H11MO.2.A,RARA,Thyroid hormone receptor-related factors (NR1),P10276,Integrative, +RARB_HUMAN.H11MO.0.D,RARB,Thyroid hormone receptor-related factors (NR1),P10826,Integrative, +RARG_HUMAN.H11MO.0.B,RARG,Thyroid hormone receptor-related factors (NR1),P13631,ChIP-Seq, +RARG_HUMAN.H11MO.1.B,RARG,Thyroid hormone receptor-related factors (NR1),P13631,ChIP-Seq, +RARG_HUMAN.H11MO.2.D,RARG,Thyroid hormone receptor-related factors (NR1),P13631,Integrative, +RAX2_HUMAN.H11MO.0.D,RAX2,Paired-related HD factors,Q96IS3,HT-SELEX, +REL_HUMAN.H11MO.0.B,REL,NF-kappaB-related factors,Q04864,ChIP-Seq, +RELB_HUMAN.H11MO.0.C,RELB,NF-kappaB-related factors,Q01201,Integrative, +REST_HUMAN.H11MO.0.A,REST,Factors with multiple dispersed zinc fingers,Q13127,ChIP-Seq, +RFX1_HUMAN.H11MO.0.B,RFX1,RFX-related factors,P22670,ChIP-Seq, +RFX1_HUMAN.H11MO.1.B,RFX1,RFX-related factors,P22670,ChIP-Seq, +RFX2_HUMAN.H11MO.0.A,RFX2,RFX-related factors,P48378,ChIP-Seq, +RFX2_HUMAN.H11MO.1.A,RFX2,RFX-related factors,P48378,ChIP-Seq, +RFX3_HUMAN.H11MO.0.B,RFX3,RFX-related factors,P48380,Integrative, +RFX4_HUMAN.H11MO.0.D,RFX4,RFX-related factors,Q33E94,HT-SELEX, +RFX5_HUMAN.H11MO.0.A,RFX5,RFX-related factors,P48382,ChIP-Seq, +RFX5_HUMAN.H11MO.1.A,RFX5,RFX-related factors,P48382,ChIP-Seq, +RHXF1_HUMAN.H11MO.0.D,RHOXF1,Paired-related HD factors,Q8NHV9,HT-SELEX, +RORA_HUMAN.H11MO.0.C,RORA,Thyroid hormone receptor-related factors (NR1),P35398,ChIP-Seq, +RORG_HUMAN.H11MO.0.C,RORC,Thyroid hormone receptor-related factors (NR1),P51449,ChIP-Seq, +RREB1_HUMAN.H11MO.0.D,RREB1,Factors with multiple dispersed zinc fingers,Q92766,Integrative, +RUNX1_HUMAN.H11MO.0.A,RUNX1,Runt-related factors,Q01196,ChIP-Seq, +RUNX2_HUMAN.H11MO.0.A,RUNX2,Runt-related factors,Q13950,ChIP-Seq, +RUNX3_HUMAN.H11MO.0.A,RUNX3,Runt-related factors,Q13761,ChIP-Seq, +RX_HUMAN.H11MO.0.D,RAX,Paired-related HD factors,Q9Y2V3,HT-SELEX, +RXRA_HUMAN.H11MO.0.A,RXRA,RXR-related receptors (NR2),P19793,ChIP-Seq, +RXRA_HUMAN.H11MO.1.A,RXRA,RXR-related receptors (NR2),P19793,ChIP-Seq, +RXRB_HUMAN.H11MO.0.C,RXRB,RXR-related receptors (NR2),P28702,Integrative, +RXRG_HUMAN.H11MO.0.B,RXRG,RXR-related receptors (NR2),P48443,Integrative, +SALL4_HUMAN.H11MO.0.B,SALL4,Factors with multiple dispersed zinc fingers,Q9UJQ4,ChIP-Seq, +SCRT1_HUMAN.H11MO.0.D,SCRT1,More than 3 adjacent zinc finger factors,Q9BWW7,HT-SELEX, +SCRT2_HUMAN.H11MO.0.D,SCRT2,More than 3 adjacent zinc finger factors,Q9NQ03,HT-SELEX, +SHOX2_HUMAN.H11MO.0.D,SHOX2,Paired-related HD factors,O60902,HT-SELEX, +SHOX_HUMAN.H11MO.0.D,SHOX,Paired-related HD factors,O15266,HT-SELEX, +SIX1_HUMAN.H11MO.0.A,SIX1,HD-SINE factors,Q15475,ChIP-Seq, +SIX2_HUMAN.H11MO.0.A,SIX2,HD-SINE factors,Q9NPC8,ChIP-Seq, +SMAD1_HUMAN.H11MO.0.D,SMAD1,SMAD factors,Q15797,Integrative, +SMAD2_HUMAN.H11MO.0.A,SMAD2,SMAD factors,Q15796,ChIP-Seq, +SMAD3_HUMAN.H11MO.0.B,SMAD3,SMAD factors,P84022,ChIP-Seq, +SMAD4_HUMAN.H11MO.0.B,SMAD4,SMAD factors,Q13485,ChIP-Seq, +SMCA1_HUMAN.H11MO.0.C,SMARCA1,Myb/SANT domain factors,P28370,ChIP-Seq, +SMCA5_HUMAN.H11MO.0.C,SMARCA5,Myb/SANT domain factors,O60264,ChIP-Seq, +SNAI1_HUMAN.H11MO.0.C,SNAI1,More than 3 adjacent zinc finger factors,O95863,Integrative, +SNAI2_HUMAN.H11MO.0.A,SNAI2,More than 3 adjacent zinc finger factors,O43623,ChIP-Seq, +SOX1_HUMAN.H11MO.0.D,SOX1,SOX-related factors,O00570,HT-SELEX, +SOX2_HUMAN.H11MO.0.A,SOX2,SOX-related factors,P48431,ChIP-Seq, +SOX2_HUMAN.H11MO.1.A,SOX2,SOX-related factors,P48431,ChIP-Seq, +SOX3_HUMAN.H11MO.0.B,SOX3,SOX-related factors,P41225,ChIP-Seq, +SOX4_HUMAN.H11MO.0.B,SOX4,SOX-related factors,Q06945,ChIP-Seq, +SOX5_HUMAN.H11MO.0.C,SOX5,SOX-related factors,P35711,Integrative, +SOX7_HUMAN.H11MO.0.D,SOX7,SOX-related factors,Q9BT81,HT-SELEX, +SOX8_HUMAN.H11MO.0.D,SOX8,SOX-related factors,P57073,HT-SELEX, +SOX9_HUMAN.H11MO.0.B,SOX9,SOX-related factors,P48436,ChIP-Seq, +SOX9_HUMAN.H11MO.1.B,SOX9,SOX-related factors,P48436,ChIP-Seq, +SOX10_HUMAN.H11MO.0.B,SOX10,SOX-related factors,P56693,ChIP-Seq, +SOX10_HUMAN.H11MO.1.A,SOX10,SOX-related factors,P56693,ChIP-Seq, +SOX11_HUMAN.H11MO.0.D,SOX11,SOX-related factors,P35716,HT-SELEX, +SOX13_HUMAN.H11MO.0.D,SOX13,SOX-related factors,Q9UN79,Integrative, +SOX15_HUMAN.H11MO.0.D,SOX15,SOX-related factors,O60248,Integrative, +SOX17_HUMAN.H11MO.0.C,SOX17,SOX-related factors,Q9H6I2,ChIP-Seq, +SOX18_HUMAN.H11MO.0.D,SOX18,SOX-related factors,P35713,Integrative, +SOX21_HUMAN.H11MO.0.D,SOX21,SOX-related factors,Q9Y651,HT-SELEX, +SP1_HUMAN.H11MO.0.A,SP1,Three-zinc finger Krüppel-related factors,P08047,ChIP-Seq, +SP1_HUMAN.H11MO.1.A,SP1,Three-zinc finger Krüppel-related factors,P08047,ChIP-Seq, +SP2_HUMAN.H11MO.0.A,SP2,Three-zinc finger Krüppel-related factors,Q02086,ChIP-Seq, +SP2_HUMAN.H11MO.1.B,SP2,Three-zinc finger Krüppel-related factors,Q02086,ChIP-Seq, +SP3_HUMAN.H11MO.0.B,SP3,Three-zinc finger Krüppel-related factors,Q02447,Integrative, +SP4_HUMAN.H11MO.0.A,SP4,Three-zinc finger Krüppel-related factors,Q02446,ChIP-Seq, +SP4_HUMAN.H11MO.1.A,SP4,Three-zinc finger Krüppel-related factors,Q02446,ChIP-Seq, +SPDEF_HUMAN.H11MO.0.D,SPDEF,Ets-related factors,O95238,HT-SELEX, +SPI1_HUMAN.H11MO.0.A,SPI1,Ets-related factors,P17947,ChIP-Seq, +SPIB_HUMAN.H11MO.0.A,SPIB,Ets-related factors,Q01892,ChIP-Seq, +SPIC_HUMAN.H11MO.0.D,SPIC,Ets-related factors,Q8N5J4,HT-SELEX, +SPZ1_HUMAN.H11MO.0.D,SPZ1,,Q9BXG8,Integrative, +SRBP1_HUMAN.H11MO.0.A,SREBF1,bHLH-ZIP factors,P36956,ChIP-Seq, +SRBP2_HUMAN.H11MO.0.B,SREBF2,bHLH-ZIP factors,Q12772,Integrative, +SRF_HUMAN.H11MO.0.A,SRF,Responders to external signals (SRF/RLM1),P11831,ChIP-Seq, +SRY_HUMAN.H11MO.0.B,SRY,SOX-related factors,Q05066,Integrative, +STA5A_HUMAN.H11MO.0.A,STAT5A,STAT factors,P42229,ChIP-Seq, +STA5B_HUMAN.H11MO.0.A,STAT5B,STAT factors,P51692,ChIP-Seq, +STAT1_HUMAN.H11MO.0.A,STAT1,STAT factors,P42224,ChIP-Seq, +STAT1_HUMAN.H11MO.1.A,STAT1,STAT factors,P42224,ChIP-Seq, +STAT2_HUMAN.H11MO.0.A,STAT2,STAT factors,P52630,ChIP-Seq, +STAT3_HUMAN.H11MO.0.A,STAT3,STAT factors,P40763,ChIP-Seq, +STAT4_HUMAN.H11MO.0.A,STAT4,STAT factors,Q14765,ChIP-Seq, +STAT6_HUMAN.H11MO.0.B,STAT6,STAT factors,P42226,ChIP-Seq, +STF1_HUMAN.H11MO.0.B,NR5A1,FTZ-F1-related receptors (NR5),Q13285,ChIP-Seq, +SUH_HUMAN.H11MO.0.A,RBPJ,CSL-related factors,Q06330,ChIP-Seq, +TAF1_HUMAN.H11MO.0.A,TAF1,TCF-7-related factors,P21675,ChIP-Seq, +TAL1_HUMAN.H11MO.0.A,TAL1,Tal-related factors,P17542,ChIP-Seq, +TAL1_HUMAN.H11MO.1.A,TAL1,Tal-related factors,P17542,ChIP-Seq, +TBP_HUMAN.H11MO.0.A,TBP,TBP-related factors,P20226,ChIP-Seq, +TBR1_HUMAN.H11MO.0.D,TBR1,TBrain-related factors,Q16650,HT-SELEX, +TBX1_HUMAN.H11MO.0.D,TBX1,TBX1-related factors,O43435,HT-SELEX, +TBX2_HUMAN.H11MO.0.D,TBX2,TBX2-related factors,Q13207,Integrative, +TBX3_HUMAN.H11MO.0.C,TBX3,TBX2-related factors,O15119,ChIP-Seq, +TBX4_HUMAN.H11MO.0.D,TBX4,TBX2-related factors,P57082,HT-SELEX, +TBX5_HUMAN.H11MO.0.D,TBX5,TBX2-related factors,Q99593,Integrative, +TBX15_HUMAN.H11MO.0.D,TBX15,TBX1-related factors,Q96SF7,HT-SELEX, +TBX19_HUMAN.H11MO.0.D,TBX19,Brachyury-related factors,O60806,HT-SELEX, +TBX20_HUMAN.H11MO.0.D,TBX20,TBX1-related factors,Q9UMR3,ChIP-Seq, +TBX21_HUMAN.H11MO.0.A,TBX21,TBrain-related factors,Q9UL17,ChIP-Seq, +TCF7_HUMAN.H11MO.0.A,TCF7,TCF-7-related factors,P36402,ChIP-Seq, +TEAD1_HUMAN.H11MO.0.A,TEAD1,TEF-1-related factors,P28347,ChIP-Seq, +TEAD2_HUMAN.H11MO.0.D,TEAD2,TEF-1-related factors,Q15562,ChIP-Seq, +TEAD3_HUMAN.H11MO.0.D,TEAD3,TEF-1-related factors,Q99594,Integrative, +TEAD4_HUMAN.H11MO.0.A,TEAD4,TEF-1-related factors,Q15561,ChIP-Seq, +TEF_HUMAN.H11MO.0.D,TEF,C/EBP-related,Q10587,Integrative, +TF2LX_HUMAN.H11MO.0.D,TGIF2LX,TALE-type homeo domain factors,Q8IUE1,HT-SELEX, +TF7L1_HUMAN.H11MO.0.B,TCF7L1,TCF-7-related factors,Q9HCS4,ChIP-Seq, +TF7L2_HUMAN.H11MO.0.A,TCF7L2,TCF-7-related factors,Q9NQB0,ChIP-Seq, +TF65_HUMAN.H11MO.0.A,RELA,NF-kappaB-related factors,Q04206,ChIP-Seq, +TFAP4_HUMAN.H11MO.0.A,TFAP4,bHLH-ZIP factors,Q01664,ChIP-Seq, +TFCP2_HUMAN.H11MO.0.D,TFCP2,CP2-related factors,Q12800,Integrative, +TFDP1_HUMAN.H11MO.0.C,TFDP1,E2F-related factors,Q14186,ChIP-Seq, +TFE2_HUMAN.H11MO.0.A,TCF3,E2A-related factors,P15923,ChIP-Seq, +TFE3_HUMAN.H11MO.0.B,TFE3,bHLH-ZIP factors,P19532,ChIP-Seq, +TFEB_HUMAN.H11MO.0.C,TFEB,bHLH-ZIP factors,P19484,Integrative, +TGIF1_HUMAN.H11MO.0.A,TGIF1,TALE-type homeo domain factors,Q15583,ChIP-Seq, +TGIF2_HUMAN.H11MO.0.D,TGIF2,TALE-type homeo domain factors,Q9GZN2,HT-SELEX, +THA11_HUMAN.H11MO.0.B,THAP11,THAP-related factors,Q96EK4,ChIP-Seq, +THA_HUMAN.H11MO.0.C,THRA,Thyroid hormone receptor-related factors (NR1),P10827,Integrative, +THA_HUMAN.H11MO.1.D,THRA,Thyroid hormone receptor-related factors (NR1),P10827,Integrative, +THAP1_HUMAN.H11MO.0.C,THAP1,THAP-related factors,Q9NVV9,ChIP-Seq, +THB_HUMAN.H11MO.0.C,THRB,Thyroid hormone receptor-related factors (NR1),P10828,Integrative, +THB_HUMAN.H11MO.1.D,THRB,Thyroid hormone receptor-related factors (NR1),P10828,Integrative, +TLX1_HUMAN.H11MO.0.D,TLX1,NK-related factors,P31314,Integrative, +TWST1_HUMAN.H11MO.0.A,TWIST1,Tal-related factors,Q15672,ChIP-Seq, +TWST1_HUMAN.H11MO.1.A,TWIST1,Tal-related factors,Q15672,ChIP-Seq, +TYY1_HUMAN.H11MO.0.A,YY1,More than 3 adjacent zinc finger factors,P25490,ChIP-Seq, +TYY2_HUMAN.H11MO.0.D,YY2,More than 3 adjacent zinc finger factors,O15391,HT-SELEX, +UBIP1_HUMAN.H11MO.0.D,UBP1,CP2-related factors,Q9NZI7,Integrative, +UNC4_HUMAN.H11MO.0.D,UNCX,Paired-related HD factors,A6NJT0,HT-SELEX, +USF1_HUMAN.H11MO.0.A,USF1,bHLH-ZIP factors,P22415,ChIP-Seq, +USF2_HUMAN.H11MO.0.A,USF2,bHLH-ZIP factors,Q15853,ChIP-Seq, +VAX1_HUMAN.H11MO.0.D,VAX1,NK-related factors,Q5SQQ9,HT-SELEX, +VAX2_HUMAN.H11MO.0.D,VAX2,NK-related factors,Q9UIW0,HT-SELEX, +VDR_HUMAN.H11MO.0.A,VDR,Thyroid hormone receptor-related factors (NR1),P11473,ChIP-Seq, +VDR_HUMAN.H11MO.1.A,VDR,Thyroid hormone receptor-related factors (NR1),P11473,ChIP-Seq, +VENTX_HUMAN.H11MO.0.D,VENTX,NK-related factors,O95231,HT-SELEX, +VEZF1_HUMAN.H11MO.0.C,VEZF1,Factors with multiple dispersed zinc fingers,Q14119,ChIP-Seq, +VEZF1_HUMAN.H11MO.1.C,VEZF1,Factors with multiple dispersed zinc fingers,Q14119,ChIP-Seq, +VSX1_HUMAN.H11MO.0.D,VSX1,Paired-related HD factors,Q9NZR4,HT-SELEX, +VSX2_HUMAN.H11MO.0.D,VSX2,Paired-related HD factors,P58304,ChIP-Seq, +WT1_HUMAN.H11MO.0.C,WT1,More than 3 adjacent zinc finger factors,P19544,ChIP-Seq, +WT1_HUMAN.H11MO.1.B,WT1,More than 3 adjacent zinc finger factors,P19544,ChIP-Seq, +XBP1_HUMAN.H11MO.0.D,XBP1,XBP-1-related factors,P17861,ChIP-Seq, +Z324A_HUMAN.H11MO.0.C,ZNF324,More than 3 adjacent zinc finger factors,O75467,ChIP-Seq, +Z354A_HUMAN.H11MO.0.C,ZNF354A,More than 3 adjacent zinc finger factors,O60765,ChIP-Seq, +ZBED1_HUMAN.H11MO.0.D,ZBED1,BED zinc finger factors,O96006,HT-SELEX, +ZBT7A_HUMAN.H11MO.0.A,ZBTB7A,More than 3 adjacent zinc finger factors,O95365,ChIP-Seq, +ZBT7B_HUMAN.H11MO.0.D,ZBTB7B,More than 3 adjacent zinc finger factors,O15156,ChIP-Seq, +ZBT14_HUMAN.H11MO.0.C,ZBTB14,More than 3 adjacent zinc finger factors,O43829,ChIP-Seq, +ZBT17_HUMAN.H11MO.0.A,ZBTB17,Factors with multiple dispersed zinc fingers,Q13105,ChIP-Seq, +ZBT18_HUMAN.H11MO.0.C,ZBTB18,More than 3 adjacent zinc finger factors,Q99592,ChIP-Seq, +ZBT48_HUMAN.H11MO.0.C,ZBTB48,More than 3 adjacent zinc finger factors,P10074,ChIP-Seq, +ZBT49_HUMAN.H11MO.0.D,ZBTB49,More than 3 adjacent zinc finger factors,Q6ZSB9,HT-SELEX, +ZBTB4_HUMAN.H11MO.0.D,ZBTB4,Factors with multiple dispersed zinc fingers,Q9P1Z0,Integrative, +ZBTB4_HUMAN.H11MO.1.D,ZBTB4,Factors with multiple dispersed zinc fingers,Q9P1Z0,Integrative, +ZBTB6_HUMAN.H11MO.0.C,ZBTB6,More than 3 adjacent zinc finger factors,Q15916,ChIP-Seq, +ZEB1_HUMAN.H11MO.0.A,ZEB1,HD-ZF factors,P37275,ChIP-Seq, +ZEP1_HUMAN.H11MO.0.D,HIVEP1,Factors with multiple dispersed zinc fingers,P15822,Integrative, +ZEP2_HUMAN.H11MO.0.D,HIVEP2,Factors with multiple dispersed zinc fingers,P31629,Integrative, +ZF64A_HUMAN.H11MO.0.D,ZFP64,More than 3 adjacent zinc finger factors,Q9NPA5,ChIP-Seq, +ZFHX3_HUMAN.H11MO.0.D,ZFHX3,HD-ZF factors,Q15911,Integrative, +ZFP28_HUMAN.H11MO.0.C,ZFP28,More than 3 adjacent zinc finger factors,Q8NHY6,ChIP-Seq, +ZFP42_HUMAN.H11MO.0.A,ZFP42,More than 3 adjacent zinc finger factors,Q96MM3,ChIP-Seq, +ZFP82_HUMAN.H11MO.0.C,ZFP82,More than 3 adjacent zinc finger factors,Q8N141,ChIP-Seq, +ZFX_HUMAN.H11MO.0.A,ZFX,More than 3 adjacent zinc finger factors,P17010,ChIP-Seq, +ZFX_HUMAN.H11MO.1.A,ZFX,More than 3 adjacent zinc finger factors,P17010,ChIP-Seq, +ZIC1_HUMAN.H11MO.0.B,ZIC1,More than 3 adjacent zinc finger factors,Q15915,Integrative, +ZIC2_HUMAN.H11MO.0.D,ZIC2,More than 3 adjacent zinc finger factors,O95409,ChIP-Seq, +ZIC3_HUMAN.H11MO.0.B,ZIC3,More than 3 adjacent zinc finger factors,O60481,ChIP-Seq, +ZIC4_HUMAN.H11MO.0.D,ZIC4,More than 3 adjacent zinc finger factors,Q8N9L1,HT-SELEX, +ZIM3_HUMAN.H11MO.0.C,ZIM3,More than 3 adjacent zinc finger factors,Q96PE6,ChIP-Seq, +ZKSC1_HUMAN.H11MO.0.B,ZKSCAN1,More than 3 adjacent zinc finger factors,P17029,ChIP-Seq, +ZKSC3_HUMAN.H11MO.0.D,ZKSCAN3,More than 3 adjacent zinc finger factors,Q9BRR0,HT-SELEX, +ZN121_HUMAN.H11MO.0.C,ZNF121,More than 3 adjacent zinc finger factors,P58317,ChIP-Seq, +ZN134_HUMAN.H11MO.0.C,ZNF134,Factors with multiple dispersed zinc fingers,P52741,ChIP-Seq, +ZN134_HUMAN.H11MO.1.C,ZNF134,Factors with multiple dispersed zinc fingers,P52741,ChIP-Seq, +ZN136_HUMAN.H11MO.0.C,ZNF136,More than 3 adjacent zinc finger factors,P52737,ChIP-Seq, +ZN140_HUMAN.H11MO.0.C,ZNF140,More than 3 adjacent zinc finger factors,P52738,ChIP-Seq, +ZN143_HUMAN.H11MO.0.A,ZNF143,More than 3 adjacent zinc finger factors,P52747,ChIP-Seq, +ZN148_HUMAN.H11MO.0.D,ZNF148,More than 3 adjacent zinc finger factors,Q9UQR1,Integrative, +ZN214_HUMAN.H11MO.0.C,ZNF214,More than 3 adjacent zinc finger factors,Q9UL59,ChIP-Seq, +ZN219_HUMAN.H11MO.0.D,ZNF219,Factors with multiple dispersed zinc fingers,Q9P2Y4,Integrative, +ZN232_HUMAN.H11MO.0.D,ZNF232,More than 3 adjacent zinc finger factors,Q9UNY5,HT-SELEX, +ZN250_HUMAN.H11MO.0.C,ZNF250,More than 3 adjacent zinc finger factors,P15622,ChIP-Seq, +ZN257_HUMAN.H11MO.0.C,ZNF257,More than 3 adjacent zinc finger factors,Q9Y2Q1,ChIP-Seq, +ZN260_HUMAN.H11MO.0.C,ZNF260,More than 3 adjacent zinc finger factors,Q3ZCT1,ChIP-Seq, +ZN263_HUMAN.H11MO.0.A,ZNF263,More than 3 adjacent zinc finger factors,O14978,ChIP-Seq, +ZN263_HUMAN.H11MO.1.A,ZNF263,More than 3 adjacent zinc finger factors,O14978,ChIP-Seq, +ZN264_HUMAN.H11MO.0.C,ZNF264,More than 3 adjacent zinc finger factors,O43296,ChIP-Seq, +ZN274_HUMAN.H11MO.0.A,ZNF274,More than 3 adjacent zinc finger factors,Q96GC6,ChIP-Seq, +ZN281_HUMAN.H11MO.0.A,ZNF281,More than 3 adjacent zinc finger factors,Q9Y2X9,ChIP-Seq, +ZN282_HUMAN.H11MO.0.D,ZNF282,More than 3 adjacent zinc finger factors,Q9UDV7,HT-SELEX, +ZN317_HUMAN.H11MO.0.C,ZNF317,More than 3 adjacent zinc finger factors,Q96PQ6,ChIP-Seq, +ZN320_HUMAN.H11MO.0.C,ZNF320,More than 3 adjacent zinc finger factors,A2RRD8,ChIP-Seq, +ZN322_HUMAN.H11MO.0.B,ZNF322,More than 3 adjacent zinc finger factors,Q6U7Q0,ChIP-Seq, +ZN329_HUMAN.H11MO.0.C,ZNF329,More than 3 adjacent zinc finger factors,Q86UD4,ChIP-Seq, +ZN331_HUMAN.H11MO.0.C,ZNF331,More than 3 adjacent zinc finger factors,Q9NQX6,ChIP-Seq, +ZN333_HUMAN.H11MO.0.D,ZNF333,More than 3 adjacent zinc finger factors,Q96JL9,Integrative, +ZN335_HUMAN.H11MO.0.A,ZNF335,Factors with multiple dispersed zinc fingers,Q9H4Z2,ChIP-Seq, +ZN335_HUMAN.H11MO.1.A,ZNF335,Factors with multiple dispersed zinc fingers,Q9H4Z2,ChIP-Seq, +ZN341_HUMAN.H11MO.0.C,ZNF341,Factors with multiple dispersed zinc fingers,Q9BYN7,ChIP-Seq, +ZN341_HUMAN.H11MO.1.C,ZNF341,Factors with multiple dispersed zinc fingers,Q9BYN7,ChIP-Seq, +ZN350_HUMAN.H11MO.0.C,ZNF350,More than 3 adjacent zinc finger factors,Q9GZX5,ChIP-Seq, +ZN350_HUMAN.H11MO.1.D,ZNF350,More than 3 adjacent zinc finger factors,Q9GZX5,Integrative, +ZN382_HUMAN.H11MO.0.C,ZNF382,Factors with multiple dispersed zinc fingers,Q96SR6,ChIP-Seq, +ZN384_HUMAN.H11MO.0.C,ZNF384,More than 3 adjacent zinc finger factors,Q8TF68,Integrative, +ZN394_HUMAN.H11MO.0.C,ZNF394,More than 3 adjacent zinc finger factors,Q53GI3,ChIP-Seq, +ZN394_HUMAN.H11MO.1.D,ZNF394,More than 3 adjacent zinc finger factors,Q53GI3,ChIP-Seq, +ZN410_HUMAN.H11MO.0.D,ZNF410,More than 3 adjacent zinc finger factors,Q86VK4,HT-SELEX, +ZN418_HUMAN.H11MO.0.C,ZNF418,Factors with multiple dispersed zinc fingers,Q8TF45,ChIP-Seq, +ZN418_HUMAN.H11MO.1.D,ZNF418,Factors with multiple dispersed zinc fingers,Q8TF45,ChIP-Seq, +ZN423_HUMAN.H11MO.0.D,ZNF423,Factors with multiple dispersed zinc fingers,Q2M1K9,Integrative, +ZN436_HUMAN.H11MO.0.C,ZNF436,More than 3 adjacent zinc finger factors,Q9C0F3,ChIP-Seq, +ZN449_HUMAN.H11MO.0.C,ZNF449,More than 3 adjacent zinc finger factors,Q6P9G9,ChIP-Seq, +ZN467_HUMAN.H11MO.0.C,ZNF467,Factors with multiple dispersed zinc fingers,Q7Z7K2,ChIP-Seq, +ZN490_HUMAN.H11MO.0.C,ZNF490,More than 3 adjacent zinc finger factors,Q9ULM2,ChIP-Seq, +ZN502_HUMAN.H11MO.0.C,ZNF502,More than 3 adjacent zinc finger factors,Q8TBZ5,ChIP-Seq, +ZN524_HUMAN.H11MO.0.D,ZNF524,More than 3 adjacent zinc finger factors,Q96C55,HT-SELEX, +ZN528_HUMAN.H11MO.0.C,ZNF528,More than 3 adjacent zinc finger factors,Q3MIS6,ChIP-Seq, +ZN547_HUMAN.H11MO.0.C,ZNF547,More than 3 adjacent zinc finger factors,Q8IVP9,ChIP-Seq, +ZN549_HUMAN.H11MO.0.C,ZNF549,More than 3 adjacent zinc finger factors,Q6P9A3,ChIP-Seq, +ZN554_HUMAN.H11MO.0.C,ZNF554,More than 3 adjacent zinc finger factors,Q86TJ5,ChIP-Seq, +ZN554_HUMAN.H11MO.1.D,ZNF554,More than 3 adjacent zinc finger factors,Q86TJ5,ChIP-Seq, +ZN563_HUMAN.H11MO.0.C,ZNF563,More than 3 adjacent zinc finger factors,Q8TA94,ChIP-Seq, +ZN563_HUMAN.H11MO.1.C,ZNF563,More than 3 adjacent zinc finger factors,Q8TA94,ChIP-Seq, +ZN582_HUMAN.H11MO.0.C,ZNF582,More than 3 adjacent zinc finger factors,Q96NG8,ChIP-Seq, +ZN586_HUMAN.H11MO.0.C,ZNF586,More than 3 adjacent zinc finger factors,Q9NXT0,ChIP-Seq, +ZN589_HUMAN.H11MO.0.D,ZNF589,More than 3 adjacent zinc finger factors,Q86UQ0,Integrative, +ZN652_HUMAN.H11MO.0.D,ZNF652,More than 3 adjacent zinc finger factors,Q9Y2D9,HT-SELEX, +ZN667_HUMAN.H11MO.0.C,ZNF667,More than 3 adjacent zinc finger factors,Q5HYK9,ChIP-Seq, +ZN680_HUMAN.H11MO.0.C,ZNF680,More than 3 adjacent zinc finger factors,Q8NEM1,ChIP-Seq, +ZN708_HUMAN.H11MO.0.C,ZNF708,More than 3 adjacent zinc finger factors,P17019,ChIP-Seq, +ZN708_HUMAN.H11MO.1.D,ZNF708,More than 3 adjacent zinc finger factors,P17019,ChIP-Seq, +ZN713_HUMAN.H11MO.0.D,ZNF713,More than 3 adjacent zinc finger factors,Q8N859,HT-SELEX, +ZN740_HUMAN.H11MO.0.D,ZNF740,Other factors with up to three adjacent zinc fingers,Q8NDX6,HT-SELEX, +ZN768_HUMAN.H11MO.0.C,ZNF768,More than 3 adjacent zinc finger factors,Q9H5H4,ChIP-Seq, +ZN770_HUMAN.H11MO.0.C,ZNF770,Factors with multiple dispersed zinc fingers,Q6IQ21,ChIP-Seq, +ZN770_HUMAN.H11MO.1.C,ZNF770,Factors with multiple dispersed zinc fingers,Q6IQ21,ChIP-Seq, +ZN784_HUMAN.H11MO.0.D,ZNF784,Factors with multiple dispersed zinc fingers,Q8NCA9,HT-SELEX, +ZN816_HUMAN.H11MO.0.C,ZNF816,More than 3 adjacent zinc finger factors,Q0VGE8,ChIP-Seq, +ZN816_HUMAN.H11MO.1.C,ZNF816,More than 3 adjacent zinc finger factors,Q0VGE8,ChIP-Seq, +ZNF8_HUMAN.H11MO.0.C,ZNF8,Factors with multiple dispersed zinc fingers,P17098,ChIP-Seq, +ZNF18_HUMAN.H11MO.0.C,ZNF18,More than 3 adjacent zinc finger factors,P17022,ChIP-Seq, +ZNF41_HUMAN.H11MO.0.C,ZNF41,More than 3 adjacent zinc finger factors,P51814,ChIP-Seq, +ZNF41_HUMAN.H11MO.1.C,ZNF41,More than 3 adjacent zinc finger factors,P51814,ChIP-Seq, +ZNF76_HUMAN.H11MO.0.C,ZNF76,More than 3 adjacent zinc finger factors,P36508,ChIP-Seq, +ZNF85_HUMAN.H11MO.0.C,ZNF85,More than 3 adjacent zinc finger factors,Q03923,ChIP-Seq, +ZNF85_HUMAN.H11MO.1.C,ZNF85,More than 3 adjacent zinc finger factors,Q03923,ChIP-Seq, +ZSC16_HUMAN.H11MO.0.D,ZSCAN16,More than 3 adjacent zinc finger factors,Q9H4T2,HT-SELEX, +ZSC22_HUMAN.H11MO.0.C,ZSCAN22,More than 3 adjacent zinc finger factors,P10073,ChIP-Seq, +ZSC31_HUMAN.H11MO.0.C,ZSCAN31,More than 3 adjacent zinc finger factors,Q96LW9,ChIP-Seq, +ZSCA4_HUMAN.H11MO.0.D,ZSCAN4,More than 3 adjacent zinc finger factors,Q8NAM6,HT-SELEX, diff --git a/data/motifs/jaspar_anno.csv b/data/motifs/jaspar_anno.csv new file mode 100644 index 000000000..7bb7eb440 --- /dev/null +++ b/data/motifs/jaspar_anno.csv @@ -0,0 +1,717 @@ +TfName,GeneName,Family,UniProt,Source +MA0002.1.RUNX1,RUNX1,Runt-related factors,Q01196,SELEX +MA0003.1.TFAP2A,TFAP2A,AP-2,P05549,SELEX +MA0004.1.Arnt,Arnt,PAS domain factors,P53762,SELEX +MA0006.1.Ahr::Arnt,Ahr+Arnt,PAS domain factors,P30561;P53762,SELEX +MA0007.1.Ar,Ar,Steroid hormone receptors (NR3),P15207,SELEX +MA0009.1.T,T,Brachyury-related factors,P20293,SELEX +MA0014.1.Pax5,Pax5,Paired domain only,Q02650,COMPILED +MA0017.1.NR2F1,NR2F1,RXR-related receptors (NR2),P10589,COMPILED +MA0018.1.CREB1,CREB1,CREB-related factors,P16220,SELEX +MA0019.1.Ddit3::Cebpa,Ddit3+Cebpa,C/EBP-related,Q62857;P05554,SELEX +MA0024.1.E2F1,E2F1,E2F-related factors,NP_005216,COMPILED +MA0025.1.NFIL3,NFIL3,C/EBP-related,Q16649,SELEX +MA0027.1.En1,En1,NK-related factors,P09065,SELEX +MA0028.1.ELK1,ELK1,Ets-related factors,P19419,SELEX +MA0029.1.Mecom,Mecom,Factors with multiple dispersed zinc fingers,A4QPC8,SELEX +MA0030.1.FOXF2,FOXF2,Forkhead box (FOX) factors,Q12947,SELEX +MA0031.1.FOXD1,FOXD1,Forkhead box (FOX) factors,Q16676,SELEX +MA0032.1.FOXC1,FOXC1,Forkhead box (FOX) factors,Q12948,SELEX +MA0033.1.FOXL1,FOXL1,Forkhead box (FOX) factors,Q12952,SELEX +MA0035.1.Gata1,Gata1,GATA-type zinc fingers,P17679,SELEX +MA0036.1.GATA2,GATA2,GATA-type zinc fingers,P23769,SELEX +MA0037.1.GATA3,GATA3,GATA-type zinc fingers,P23771,SELEX +MA0038.1.Gfi1,Gfi1,More than 3 adjacent zinc finger factors,Q07120,SELEX +MA0039.1.Klf4,Klf4,Three-zinc finger Kruppel-related factors,Q60793,SELEX +MA0040.1.Foxq1,Foxq1,Forkhead box (FOX) factors,Q63244,SELEX +MA0041.1.Foxd3,Foxd3,Forkhead box (FOX) factors,Q63245,SELEX +MA0042.1.FOXI1,FOXI1,Forkhead box (FOX) factors,Q12951,SELEX +MA0043.1.HLF,HLF,C/EBP-related,Q16534,SELEX +MA0046.1.HNF1A,HNF1A,POU domain factors,ABR09270,COMPILED +MA0047.1.Foxa2,Foxa2,Forkhead box (FOX) factors,P32182,COMPILED +MA0048.1.NHLH1,NHLH1,Tal-related factors,Q02575,SELEX +MA0050.1.IRF1,IRF1,Interferon-regulatory factors,P10914,SELEX +MA0051.1.IRF2,IRF2,Interferon-regulatory factors,P14316,SELEX +MA0052.1.MEF2A,MEF2A,Regulators of differentiation,EAX02249,SELEX +MA0056.1.MZF1,MZF1,More than 3 adjacent zinc finger factors,P28698,SELEX +MA0057.1.MZF1(var.2),MZF1(var.2),More than 3 adjacent zinc finger factors,P28698,SELEX +MA0058.1.MAX,MAX,bHLH-ZIP factors,AAH36092,SELEX +MA0059.1.MAX::MYC,MAX+MYC,bHLH-ZIP factors,P61244;P01106,SELEX +MA0060.1.NFYA,NFYA,NFY,P23511,COMPILED +MA0062.1.GABPA,GABPA,Ets-related factors,Q06546,COMPILED +MA0063.1.Nkx2-5,Nkx2-5,NK-related factors,P42582,SELEX +MA0065.1.PPARG::RXRA,PPARG+RXRA,RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1),P19793;P37231,SELEX +MA0066.1.PPARG,PPARG,Thyroid hormone receptor-related factors (NR1),P37231,SELEX +MA0067.1.Pax2,Pax2,Paired domain only,P32114,SELEX +MA0068.1.Pax4,Pax4,Paired plus homeo domain,P32115,SELEX +MA0069.1.Pax6,Pax6,Paired plus homeo domain,P26367,SELEX +MA0070.1.PBX1,PBX1,TALE-type homeo domain factors,P40424,SELEX +MA0071.1.RORA,RORA,Thyroid hormone receptor-related factors (NR1),P35398,SELEX +MA0072.1.RORA(var.2),RORA(var.2),Thyroid hormone receptor-related factors (NR1),P35398,SELEX +MA0073.1.RREB1,RREB1,Factors with multiple dispersed zinc fingers,Q92766,SELEX +MA0074.1.RXRA::VDR,RXRA+VDR,RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1),P19793;P11473,SELEX +MA0075.1.Prrx2,Prrx2,Paired-related HD factors,Q06348,SELEX +MA0076.1.ELK4,ELK4,Ets-related factors,P28324,SELEX +MA0077.1.SOX9,SOX9,SOX-related factors,P48436,SELEX +MA0078.1.Sox17,Sox17,SOX-related factors,Q61473,SELEX +MA0079.1.SP1,SP1,Three-zinc finger Krüppel-related factors,P08047,SELEX +MA0080.1.SPI1,SPI1,Ets-related factors,P17947,SELEX +MA0081.1.SPIB,SPIB,Ets-related factors,Q01892,SELEX +MA0083.1.SRF,SRF,Responders to external signals (SRF/RLM1),P11831,SELEX +MA0084.1.SRY,SRY,SOX-related factors,Q05066,SELEX +MA0087.1.Sox5,Sox5,SOX-related factors,P35710,SELEX +MA0088.1.znf143,znf143,More than 3 adjacent zinc finger factors,Q91853,COMPILED +MA0089.1.MAFG::NFE2L1,MAFG+NFE2L1,Jun-related factors;Maf-related factors,Q90889;Q5ZL67,SELEX +MA0090.1.TEAD1,TEAD1,TEF-1-related factors,P28347,COMPILED +MA0091.1.TAL1::TCF3,TAL1+TCF3,E2A-related factors;Tal-related factors,P17542;P15923,SELEX +MA0092.1.Hand1::Tcf3,Hand1+Tcf3,E2A-related factors;Tal-related factors,Q64279;P15806,SELEX +MA0093.1.USF1,USF1,bHLH-ZIP factors,P22415,SELEX +MA0095.1.YY1,YY1,More than 3 adjacent zinc finger factors,P25490,COMPILED +MA0098.1.ETS1,ETS1,Ets-related factors,CAG47050,SELEX +MA0099.1.JUN::FOS,JUN+FOS,Fos-related factors;Jun-related factors,P01101,SELEX +MA0100.1.Myb,Myb,Myb/SANT domain factors,P06876,SELEX +MA0101.1.REL,REL,NF-kappaB-related factors,Q04864,SELEX +MA0103.1.ZEB1,ZEB1,HD-ZF factors,P36197,SELEX +MA0104.1.Mycn,Mycn,bHLH-ZIP factors,P03966,SELEX +MA0105.2.NFKB1,NFKB1,NF-kappaB-related factors,P19838,SELEX +MA0106.1.TP53,TP53,p53-related factors,P04637,SELEX +MA0107.1.RELA,RELA,NF-kappaB-related factors,Q04206,SELEX +MA0108.2.TBP,TBP,TBP-related factors,P20226, +MA0109.1.HLTF,HLTF,Myb/SANT domain factors,Q95216,SELEX +MA0111.1.Spz1,Spz1,,Q99MY0,SELEX +MA0112.1.ESR1,ESR1,Steroid hormone receptors (NR3),P03372,COMPILED +MA0113.1.NR3C1,NR3C1,Steroid hormone receptors (NR3),P04150,COMPILED +MA0114.1.HNF4A,HNF4A,RXR-related receptors (NR2),P41235,COMPILED +MA0115.1.NR1H2::RXRA,NR1H2+RXRA,RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1),P55055;P19793,SELEX +MA0116.1.Znf423,Znf423,Factors with multiple dispersed zinc fingers,O08961,SELEX +MA0117.1.Mafb,Mafb,Maf-related factors,P54842,SELEX +MA0119.1.NFIC::TLX1,NFIC+TLX1,NK-related factors;Nuclear factor 1,P08651;P31314,SELEX +MA0122.1.Nkx3-2,Nkx3-2,NK-related factors,P97503,SELEX +MA0124.1.NKX3-1,NKX3-1,NK-related factors,Q99801,SELEX +MA0125.1.Nobox,Nobox,Paired-related HD factors,Q8VIH1,SELEX +MA0130.1.ZNF354C,ZNF354C,More than 3 adjacent zinc finger factors,Q86Y25,SELEX +MA0131.1.HINFP,HINFP,Factors with multiple dispersed zinc fingers,Q9BQA5,SELEX +MA0132.1.Pdx1,Pdx1,HOX-related factors,NP_032840,SELEX +MA0135.1.Lhx3,Lhx3,HD-LIM factors,P50481,SELEX +MA0137.1.STAT1,STAT1,STAT factors,Q53XW4,COMPILED +MA0138.1.REST,REST,Factors with multiple dispersed zinc fingers,NP_005603,COMPILED +MA0139.1.CTCF,CTCF,More than 3 adjacent zinc finger factors,P49711,ChIP-seq +MA0140.1.Tal1::Gata1,Tal1+Gata1,GATA-type zinc fingers;Tal-related factors,P17679;P22091,ChIP-seq +MA0141.1.Esrrb,Esrrb,Steroid hormone receptors (NR3),Q61539,ChIP-seq +MA0142.1.Pou5f1::Sox2,Pou5f1+Sox2,POU domain factors;SOX-related factors,P20263;P48432,ChIP-seq +MA0143.1.Sox2,Sox2,SOX-related factors,P48432,ChIP-seq +MA0144.1.Stat3,Stat3,STAT factors,P42227,ChIP-seq +MA0145.1.Tcfcp2l1,Tcfcp2l1,CP2-related factors,Q3UNW5,ChIP-seq +MA0146.1.Zfx,Zfx,More than 3 adjacent zinc finger factors,P17012,ChIP-seq +MA0147.1.Myc,Myc,bHLH-ZIP factors,P01108,ChIP-seq +MA0148.1.FOXA1,FOXA1,Forkhead box (FOX) factors,P55317,ChIP-seq +MA0149.1.EWSR1-FLI1,EWSR1-FLI1,Ets-related factors,F1JVV7;F1JVV8,ChIP-seq +MA0062.2.Gabpa,Gabpa,Ets-related factors,Q91YY8,ChIP-seq +MA0035.2.Gata1,Gata1,GATA-type zinc fingers,P17679,ChIP-seq +MA0039.2.Klf4,Klf4,Three-zinc finger Kruppel-related factors,Q60793,ChIP-seq +MA0138.2.REST,REST,Factors with multiple dispersed zinc fingers,Q13127,ChIP-seq +MA0002.2.RUNX1,RUNX1,Runt-related factors,Q01196,ChIP-seq +MA0137.2.STAT1,STAT1,STAT factors,Q53XW4,ChIP-seq +MA0104.2.Mycn,Mycn,bHLH-ZIP factors,P03966,ChIP-seq +MA0047.2.Foxa2,Foxa2,Forkhead box (FOX) factors,P35583,ChIP-seq +MA0112.2.ESR1,ESR1,Steroid hormone receptors (NR3),P03372,ChIP-seq +MA0065.2.Pparg::Rxra,Pparg+Rxra,RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1),P37238;P28700,ChIP-seq +MA0150.1.NFE2L2,NFE2L2,Jun-related factors,Q16236,COMPILED +MA0151.1.Arid3a,Arid3a,ARID-related factors,Q62431,SELEX +MA0152.1.NFATC2,NFATC2,NFAT-related factors,Q13469,COMPILED +MA0153.1.HNF1B,HNF1B,POU domain factors,P35680,COMPILED +MA0154.1.EBF1,EBF1,Early B-Cell Factor-related factors,Q07802,COMPILED +MA0155.1.INSM1,INSM1,Factors with multiple dispersed zinc fingers,Q01101,COMPILED +MA0156.1.FEV,FEV,Ets-related factors,Q99581,COMPILED +MA0157.1.FOXO3,FOXO3,Forkhead box (FOX) factors,O43524,COMPILED +MA0158.1.HOXA5,HOXA5,HOX-related factors,P20719,COMPILED +MA0159.1.RARA::RXRA,RARA+RXRA,RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1),P10276;P19793,COMPILED +MA0160.1.NR4A2,NR4A2,NGFI-B-related receptors (NR4),P43354,COMPILED +MA0161.1.NFIC,NFIC,Nuclear factor 1,P08651,High-throughput SELEX SAGE +MA0162.1.Egr1,Egr1,Three-zinc finger Kruppel-related factors,P08046,bacterial 1-hybrid +MA0163.1.PLAG1,PLAG1,More than 3 adjacent zinc finger factors,Q6DJT9,bacterial 1-hybrid +MA0164.1.Nr2e3,Nr2e3,RXR-related receptors (NR2),Q9QXZ7,SELEX +MA0080.2.SPI1,SPI1,Ets-related factors,P17947,COMPILED +MA0018.2.CREB1,CREB1,CREB-related factors,P16220,COMPILED +MA0099.2.FOS::JUN,FOS+JUN,Fos-related factors;Jun-related factors,P01100;P05412,COMPILED +MA0079.2.SP1,SP1,Three-zinc finger Krüppel-related factors,P08047,COMPILED +MA0102.2.CEBPA,CEBPA,C/EBP-related,-,COMPILED +MA0258.1.ESR2,ESR2,Steroid hormone receptors (NR3),Q92731,ChIP-chip +MA0259.1.ARNT::HIF1A,ARNT+HIF1A,PAS domain factors,P27540;Q16665,COMPILED +MA0442.1.SOX10,SOX10,SOX-related factors,P56693,COMPILED +MA0141.2.Esrrb,Esrrb,Steroid hormone receptors (NR3),Q61539,ChIP-seq +MA0143.2.Sox2,Sox2,SOX-related factors,P48432,ChIP-seq +MA0145.2.Tcfcp2l1,Tcfcp2l1,CP2-related factors,Q3UNW5,ChIP-seq +MA0146.2.Zfx,Zfx,More than 3 adjacent zinc finger factors,P17012,ChIP-seq +MA0148.2.FOXA1,FOXA1,Forkhead box (FOX) factors,P55317,ChIP-seq +MA0461.1.Atoh1,Atoh1,Tal-related factors,P48985,ChIP-seq +MA0462.1.BATF::JUN,BATF+JUN,B-ATF-related factors;Jun-related factors,Q16520;P05412,ChIP-seq +MA0463.1.Bcl6,Bcl6,More than 3 adjacent zinc finger factors,P41183,ChIP-seq +MA0464.1.Bhlhe40,Bhlhe40,Hairy-related factors,O35185,ChIP-seq +MA0465.1.CDX2,CDX2,HOX-related factors,Q99626,ChIP-seq +MA0466.1.CEBPB,CEBPB,C/EBP-related,P17676,ChIP-seq +MA0467.1.Crx,Crx,Paired-related HD factors,O54751,ChIP-seq +MA0468.1.DUX4,DUX4,Paired-related HD factors,Q9UBX2,ChIP-seq +MA0469.1.E2F3,E2F3,E2F-related factors,O35261,ChIP-seq +MA0470.1.E2F4,E2F4,E2F-related factors,Q16254,ChIP-seq +MA0471.1.E2F6,E2F6,E2F-related factors,O75461,ChIP-seq +MA0472.1.EGR2,EGR2,Three-zinc finger Krüppel-related factors,P08152,ChIP-seq +MA0473.1.ELF1,ELF1,Ets-related factors,P32519,ChIP-seq +MA0474.1.Erg,Erg,Ets-related factors,P81270,ChIP-seq +MA0475.1.FLI1,FLI1,Ets-related factors,Q01543,ChIP-seq +MA0476.1.FOS,FOS,Fos-related factors,P01100,ChIP-seq +MA0477.1.FOSL1,FOSL1,Fos-related factors,P15407,ChIP-seq +MA0478.1.FOSL2,FOSL2,Fos-related factors,P15408,ChIP-seq +MA0479.1.FOXH1,FOXH1,Forkhead box (FOX) factors,O75593,ChIP-seq +MA0480.1.Foxo1,Foxo1,Forkhead box (FOX) factors,Q9R1E0,ChIP-seq +MA0481.1.FOXP1,FOXP1,Forkhead box (FOX) factors,Q9H334,ChIP-seq +MA0482.1.Gata4,Gata4,GATA-type zinc fingers,Q08369,ChIP-seq +MA0483.1.Gfi1b,Gfi1b,More than 3 adjacent zinc finger factors,O70237,ChIP-seq +MA0484.1.HNF4G,HNF4G,RXR-related receptors (NR2),Q14541,ChIP-seq +MA0485.1.Hoxc9,Hoxc9,HOX-related factors,P09633,ChIP-seq +MA0486.1.HSF1,HSF1,HSF factors,Q00613,ChIP-seq +MA0488.1.JUN,JUN,Jun-related factors,P05412,ChIP-seq +MA0489.1.JUN(var.2),JUN(var.2),Jun-related factors,P05412,ChIP-seq +MA0490.1.JUNB,JUNB,Jun-related factors,P17275,ChIP-seq +MA0491.1.JUND,JUND,Jun-related factors,P17535,ChIP-seq +MA0492.1.JUND(var.2),JUND(var.2),Jun-related factors,P17535,ChIP-seq +MA0493.1.Klf1,Klf1,Three-zinc finger Krüppel-related factors,P46099,ChIP-seq +MA0494.1.Nr1h3::Rxra,Nr1h3+Rxra,RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1),Q9Z0Y9;P28700,ChIP-seq +MA0495.1.MAFF,MAFF,Maf-related factors,Q9ULX9,ChIP-seq +MA0496.1.MAFK,MAFK,Maf-related factors,O60675,ChIP-seq +MA0497.1.MEF2C,MEF2C,Regulators of differentiation,Q06413,ChIP-seq +MA0498.1.Meis1,Meis1,TALE-type homeo domain factors,Q60954,ChIP-seq +MA0499.1.Myod1,Myod1,MyoD / ASC-related factors,P10085,ChIP-seq +MA0500.1.Myog,Myog,MyoD / ASC-related factors,P12979,ChIP-seq +MA0501.1.MAF::NFE2,MAF+NFE2,Jun-related factors;Maf-related factors,O75444;Q16621,ChIP-seq +MA0502.1.NFYB,NFYB,NFY,P25208,ChIP-seq +MA0503.1.Nkx2-5(var.2),Nkx2-5(var.2),NK-related factors,P42582,ChIP-seq +MA0504.1.NR2C2,NR2C2,RXR-related receptors (NR2),P49116,ChIP-seq +MA0505.1.Nr5a2,Nr5a2,FTZ-F1-related receptors (NR5),P45448,ChIP-seq +MA0506.1.NRF1,NRF1,Jun-related factors,Q16656,ChIP-seq +MA0507.1.POU2F2,POU2F2,POU domain factors,P09086,ChIP-seq +MA0508.1.PRDM1,PRDM1,More than 3 adjacent zinc finger factors,O75626,ChIP-seq +MA0509.1.Rfx1,Rfx1,RFX-related factors,P48377,ChIP-seq +MA0510.1.RFX5,RFX5,RFX-related factors,P48382,ChIP-seq +MA0511.1.RUNX2,RUNX2,Runt-related factors,Q13950,ChIP-seq +MA0512.1.Rxra,Rxra,RXR-related receptors (NR2),P28700,ChIP-seq +MA0513.1.SMAD2::SMAD3::SMAD4,SMAD2+SMAD3+SMAD4,SMAD factors,Q15796;P84022;Q13485,ChIP-seq +MA0514.1.Sox3,Sox3,SOX-related factors,P53784,ChIP-seq +MA0515.1.Sox6,Sox6,SOX-related factors,P40645,ChIP-seq +MA0516.1.SP2,SP2,Three-zinc finger Krüppel-related factors,Q02086,ChIP-seq +MA0517.1.STAT1::STAT2,STAT1+STAT2,STAT factors,P42224;P52630,ChIP-seq +MA0518.1.Stat4,Stat4,STAT factors,P42228,ChIP-seq +MA0519.1.Stat5a::Stat5b,Stat5a+Stat5b,STAT factors,P42230;P42232,ChIP-seq +MA0520.1.Stat6,Stat6,STAT factors,P52633,ChIP-seq +MA0521.1.Tcf12,Tcf12,E2A-related factors,Q61286,ChIP-seq +MA0522.1.Tcf3,Tcf3,E2A-related factors,Q9Z1J1,ChIP-seq +MA0523.1.TCF7L2,TCF7L2,TCF-7-related factors,Q9NQB0,ChIP-seq +MA0524.1.TFAP2C,TFAP2C,AP-2,Q92754,ChIP-seq +MA0525.1.TP63,TP63,p53-related factors,Q9H3D4,ChIP-seq +MA0526.1.USF2,USF2,bHLH-ZIP factors,Q15853,ChIP-seq +MA0527.1.ZBTB33,ZBTB33,Other factors with up to three adjacent zinc fingers,Q86T24,ChIP-seq +MA0528.1.ZNF263,ZNF263,More than 3 adjacent zinc finger factors,O14978,ChIP-seq +MA0007.2.AR,AR,Steroid hormone receptors (NR3),P10275,ChIP-seq +MA0102.3.CEBPA,CEBPA,C/EBP-related,P49715,ChIP-seq +MA0024.2.E2F1,E2F1,E2F-related factors,Q01094,ChIP-seq +MA0154.2.EBF1,EBF1,Early B-Cell Factor-related factors,Q9UH73,ChIP-seq +MA0162.2.EGR1,EGR1,Three-zinc finger Kruppel-related factors,P18146,ChIP-seq +MA0076.2.ELK4,ELK4,Ets-related factors,P28324,ChIP-seq +MA0258.2.ESR2,ESR2,Steroid hormone receptors (NR3),Q92731,ChIP-seq +MA0098.2.Ets1,Ets1,Ets-related factors,P27577,ChIP-seq +MA0148.3.FOXA1,FOXA1,Forkhead box (FOX) factors,P55317,ChIP-seq +MA0035.3.Gata1,Gata1,GATA-type zinc fingers,P17679,ChIP-seq +MA0036.2.GATA2,GATA2,GATA-type zinc fingers,P23769,ChIP-seq +MA0037.2.GATA3,GATA3,GATA-type zinc fingers,P23771,ChIP-seq +MA0114.2.HNF4A,HNF4A,RXR-related receptors (NR2),P41235,ChIP-seq +MA0050.2.IRF1,IRF1,Interferon-regulatory factors,P10914,ChIP-seq +MA0058.2.MAX,MAX,bHLH-ZIP factors,P61244,ChIP-seq +MA0052.2.MEF2A,MEF2A,Regulators of differentiation,Q02078,ChIP-seq +MA0100.2.Myb,Myb,Myb/SANT domain factors,P06876,ChIP-seq +MA0147.2.Myc,Myc,bHLH-ZIP factors,P01108,ChIP-seq +MA0104.3.Mycn,Mycn,bHLH-ZIP factors,P03966,ChIP-seq +MA0150.2.Nfe2l2,Nfe2l2,Jun-related factors,Q60795,ChIP-seq +MA0105.3.NFKB1,NFKB1,NF-kappaB-related factors,P19838,ChIP-seq +MA0060.2.NFYA,NFYA,NFY,P23511,ChIP-seq +MA0014.2.PAX5,PAX5,Paired domain only,Q02548,ChIP-seq +MA0080.3.Spi1,Spi1,Ets-related factors,P17433,ChIP-seq +MA0143.3.Sox2,Sox2,SOX-related factors,P48432,ChIP-seq +MA0079.3.SP1,SP1,Three-zinc finger Krüppel-related factors,P08047,ChIP-seq +MA0083.2.SRF,SRF,Responders to external signals (SRF/RLM1),P11831,ChIP-seq +MA0137.3.STAT1,STAT1,STAT factors,P42224,ChIP-seq +MA0144.2.STAT3,STAT3,STAT factors,P40763,ChIP-seq +MA0140.2.GATA1::TAL1,GATA1+TAL1,GATA-type zinc fingers;Tal-related factors,P15976;P17542,ChIP-seq +MA0003.2.TFAP2A,TFAP2A,AP-2,P05549,ChIP-seq +MA0106.2.TP53,TP53,p53-related factors,P04637,ChIP-seq +MA0093.2.USF1,USF1,bHLH-ZIP factors,P22415,ChIP-seq +MA0095.2.YY1,YY1,More than 3 adjacent zinc finger factors,P25490,ChIP-seq +MA0103.2.ZEB1,ZEB1,HD-ZF factors,P37275,ChIP-seq +MA0591.1.Bach1::Mafk,Bach1+Mafk,Jun-related factors;Maf-related factors,P97302;Q61827,ChIP-seq +MA0592.1.ESRRA,ESRRA,Steroid hormone receptors (NR3),P11474,ChIP-seq +MA0593.1.FOXP2,FOXP2,Forkhead box (FOX) factors,O15409,ChIP-seq +MA0594.1.Hoxa9,Hoxa9,HOX-related factors,P09631,ChIP-seq +MA0595.1.SREBF1,SREBF1,bHLH-ZIP factors,P36956,ChIP-seq +MA0596.1.SREBF2,SREBF2,bHLH-ZIP factors,Q12772,ChIP-seq +MA0597.1.THAP1,THAP1,THAP-related factors,Q9NVV9,ChIP-seq +MA0598.1.EHF,EHF,Ets-related factors,Q9NZC4,ChIP-seq +MA0599.1.KLF5,KLF5,Three-zinc finger Krüppel-related factors,Q13887,ChIP-seq +MA0600.1.RFX2,RFX2,RFX-related factors,P48378,ChIP-seq +MA0113.2.NR3C1,NR3C1,Steroid hormone receptors (NR3),P04150,ChIP-seq +MA0601.1.Arid3b,Arid3b,ARID-related factors,Q9Z1N7,PBM +MA0602.1.Arid5a,Arid5a,ARID-related factors,Q3U108,universal protein binding microarray (PBM) +MA0603.1.Arntl,Arntl,PAS domain factors,Q9WTL8,PBM +MA0604.1.Atf1,Atf1,CREB-related factors,P81269,PBM +MA0605.1.Atf3,Atf3,Fos-related factors,Q60765,PBM +MA0606.1.NFAT5,NFAT5,NFAT-related factors,F1NSH4,PBM +MA0607.1.Bhlha15,Bhlha15,Tal-related factors,Q9QYC3,PBM +MA0608.1.Creb3l2,Creb3l2,CREB-related factors,Q8BH52,PBM +MA0609.1.Crem,Crem,CREB-related factors,P27699,PBM +MA0610.1.DMRT3,DMRT3,DMRT,Q9NQL9,PBM +MA0611.1.Dux,Dux,Paired-related HD factors,A1JVI8,PBM +MA0612.1.EMX1,EMX1,NK-related factors,Q04741,PBM +MA0613.1.FOXG1,FOXG1,Forkhead box (FOX) factors,P55316,PBM +MA0614.1.Foxj2,Foxj2,Forkhead box (FOX) factors,Q9ES18,PBM +MA0615.1.Gmeb1,Gmeb1,GMEB,Q9JL60,universal protein binding microarray (PBM) +MA1099.1.Hes1,Hes1,Hairy-related factors,P35428,PBM +MA0616.1.Hes2,Hes2,Hairy-related factors,O54792,PBM +MA0617.1.Id2,Id2,HLH domain only,P41136,PBM +MA0618.1.LBX1,LBX1,NK-related factors,P52954,PBM +MA0619.1.LIN54,LIN54,,F1NCE0,PBM +MA0620.1.Mitf,Mitf,bHLH-ZIP factors,Q08874,PBM +MA0621.1.mix-a,mix-a,Paired-related HD factors,P21711,PBM +MA0622.1.Mlxip,Mlxip,bHLH-ZIP factors,Q2VPU4,PBM +MA0623.1.Neurog1,Neurog1,Tal-related factors,P70660,PBM +MA0624.1.NFATC1,NFATC1,NFAT-related factors,G1MTN6,PBM +MA0625.1.NFATC3,NFATC3,NFAT-related factors,Q12968,PBM +MA0626.1.Npas2,Npas2,PAS domain factors,P97460,PBM +MA0627.1.Pou2f3,Pou2f3,POU domain factors,P31362,universal protein binding microarray (PBM) +MA0628.1.POU6F1,POU6F1,POU domain factors,Q14863,PBM +MA0629.1.Rhox11,Rhox11,Paired-related HD factors,Q810N8,universal protein binding microarray (PBM) +MA0630.1.SHOX,SHOX,Paired-related HD factors,O15266,PBM +MA0631.1.Six3,Six3,HD-SINE factors,Q62233,universal protein binding microarray (PBM) +MA0632.1.Tcfl5,Tcfl5,PAS domain factors,Q32NY8,PBM +MA0633.1.Twist2,Twist2,Tal-related factors,Q9D030,PBM +MA0634.1.ALX3,ALX3,Paired-related HD factors,O95076,HT-SELEX +MA0007.3.Ar,Ar,Steroid hormone receptors (NR3),P19091,HT-SELEX +MA0635.1.BARHL2,BARHL2,NK-related factors,Q9NY43,HT-SELEX +MA0636.1.BHLHE41,BHLHE41,Hairy-related factors,Q9C0J9,HT-SELEX +MA0637.1.CENPB,CENPB,,P07199,HT-SELEX +MA0638.1.CREB3,CREB3,CREB-related factors,O43889,HT-SELEX +MA0639.1.DBP,DBP,C/EBP-related,Q10586,HT-SELEX +MA0154.3.EBF1,EBF1,Early B-Cell Factor-related factors,Q9UH73,HT-SELEX +MA0598.2.EHF,EHF,Ets-related factors,Q9NZC4,HT-SELEX +MA0640.1.ELF3,ELF3,Ets-related factors,P78545,HT-SELEX +MA0641.1.ELF4,ELF4,Ets-related factors,Q99607,HT-SELEX +MA0136.2.ELF5,ELF5,Ets-related factors,Q9UKW6,HT-SELEX +MA0027.2.EN1,EN1,NK-related factors,Q05925,HT-SELEX +MA0642.1.EN2,EN2,NK-related factors,P19622,HT-SELEX +MA0643.1.Esrrg,Esrrg,Steroid hormone receptors (NR3),P62509,HT-SELEX +MA0644.1.ESX1,ESX1,Paired-related HD factors,Q8N693,HT-SELEX +MA0645.1.ETV6,ETV6,Ets-related factors,P41212,HT-SELEX +MA0475.2.FLI1,FLI1,Ets-related factors,Q01543,HT-SELEX +MA0042.2.FOXI1,FOXI1,Forkhead box (FOX) factors,Q12951,HT-SELEX +MA0033.2.FOXL1,FOXL1,Forkhead box (FOX) factors,Q12952,HT-SELEX +MA0157.2.FOXO3,FOXO3,Forkhead box (FOX) factors,O43524,HT-SELEX +MA0646.1.GCM1,GCM1,GCM factors,Q9NP62,HT-SELEX +MA0647.1.GRHL1,GRHL1,Grainyhead-related factors,Q9NZI5,HT-SELEX +MA0648.1.GSC,GSC,Paired-related HD factors,P56915,HT-SELEX +MA0649.1.HEY2,HEY2,Hairy-related factors,Q9UBP5,HT-SELEX +MA0131.2.HINFP,HINFP,Factors with multiple dispersed zinc fingers,Q9BQA5,HT-SELEX +MA0043.2.HLF,HLF,C/EBP-related,Q16534,HT-SELEX +MA0046.2.HNF1A,HNF1A,POU domain factors,P20823,HT-SELEX +MA0153.2.HNF1B,HNF1B,POU domain factors,P35680,HT-SELEX +MA0650.1.HOXA13,HOXA13,HOX-related factors,P31271,HT-SELEX +MA0651.1.HOXC11,HOXC11,HOX-related factors,O43248,HT-SELEX +MA0486.2.HSF1,HSF1,HSF factors,Q00613,HT-SELEX +MA0652.1.IRF8,IRF8,Interferon-regulatory factors,Q02556,HT-SELEX +MA0653.1.IRF9,IRF9,Interferon-regulatory factors,Q00978,HT-SELEX +MA0654.1.ISX,ISX,Paired-related HD factors,Q2M1V0,HT-SELEX +MA0655.1.JDP2,JDP2,Fos-related factors,Q8WYK2,HT-SELEX +MA0656.1.JDP2(var.2),JDP2(var.2),Fos-related factors,Q8WYK2,HT-SELEX +MA0657.1.KLF13,KLF13,Three-zinc finger Krüppel-related factors,Q9Y2Y9,HT-SELEX +MA0658.1.LHX6,LHX6,HD-LIM factors,Q9UPM6,HT-SELEX +MA0659.1.MAFG,MAFG,Maf-related factors,O15525,HT-SELEX +MA0660.1.MEF2B,MEF2B,Regulators of differentiation,Q02080,HT-SELEX +MA0661.1.MEOX1,MEOX1,HOX-related factors,P50221,HT-SELEX +MA0662.1.MIXL1,MIXL1,Paired-related HD factors,Q9H2W2,HT-SELEX +MA0663.1.MLX,MLX,bHLH-ZIP factors,Q9UH92,HT-SELEX +MA0664.1.MLXIPL,MLXIPL,bHLH-ZIP factors,Q9NP71,HT-SELEX +MA0665.1.MSC,MSC,Tal-related factors,O60682,HT-SELEX +MA0666.1.MSX1,MSX1,NK-related factors,P28360,HT-SELEX +MA0667.1.MYF6,MYF6,MyoD / ASC-related factors,P23409,HT-SELEX +MA0668.1.NEUROD2,NEUROD2,Tal-related factors,Q15784,HT-SELEX +MA0669.1.NEUROG2,NEUROG2,Tal-related factors,Q9H2A3,HT-SELEX +MA0670.1.NFIA,NFIA,Nuclear factor 1,Q12857,HT-SELEX +MA0671.1.NFIX,NFIX,Nuclear factor 1,Q14938,HT-SELEX +MA0048.2.NHLH1,NHLH1,Tal-related factors,Q02575,HT-SELEX +MA0672.1.NKX2-3,NKX2-3,NK-related factors,Q8TAU0,HT-SELEX +MA0673.1.NKX2-8,NKX2-8,NK-related factors,O15522,HT-SELEX +MA0674.1.NKX6-1,NKX6-1,NK-related factors,P78426,HT-SELEX +MA0675.1.NKX6-2,NKX6-2,NK-related factors,Q9C056,HT-SELEX +MA0676.1.Nr2e1,Nr2e1,RXR-related receptors (NR2),Q9Y466,HT-SELEX +MA0677.1.Nr2f6,Nr2f6,RXR-related receptors (NR2),P43136,HT-SELEX +MA0678.1.OLIG2,OLIG2,Tal-related factors,Q13516,HT-SELEX +MA0679.1.ONECUT1,ONECUT1,HD-CUT factors,Q9UBC0,HT-SELEX +MA0068.2.PAX4,PAX4,Paired plus homeo domain,O43316,HT-SELEX +MA0680.1.PAX7,PAX7,Paired plus homeo domain,P23759,HT-SELEX +MA0681.1.Phox2b,Phox2b,Paired-related HD factors,Q99453,HT-SELEX +MA0682.1.Pitx1,Pitx1,Paired-related HD factors,P70314,HT-SELEX +MA0683.1.POU4F2,POU4F2,POU domain factors,Q12837,HT-SELEX +MA0075.2.Prrx2,Prrx2,Paired-related HD factors,Q06348,HT-SELEX +MA0684.1.RUNX3,RUNX3,Runt-related factors,Q13761,HT-SELEX +MA0512.2.Rxra,Rxra,RXR-related receptors (NR2),P28700,HT-SELEX +MA0685.1.SP4,SP4,Three-zinc finger Krüppel-related factors,Q02446,HT-SELEX +MA0686.1.SPDEF,SPDEF,Ets-related factors,O95238,HT-SELEX +MA0080.4.SPI1,SPI1,Ets-related factors,P17947,HT-SELEX +MA0687.1.SPIC,SPIC,Ets-related factors,Q8N5J4,HT-SELEX +MA0083.3.SRF,SRF,Responders to external signals (SRF/RLM1),P11831,HT-SELEX +MA0009.2.T,T,Brachyury-related factors,O15178,HT-SELEX +MA0688.1.TBX2,TBX2,TBX2-related factors,Q13207,HT-SELEX +MA0689.1.TBX20,TBX20,TBX1-related factors,Q9UMR3,HT-SELEX +MA0690.1.TBX21,TBX21,TBrain-related factors,Q9UL17,HT-SELEX +MA0090.2.TEAD1,TEAD1,TEF-1-related factors,P28347,HT-SELEX +MA0691.1.TFAP4,TFAP4,bHLH-ZIP factors,Q01664,HT-SELEX +MA0145.3.TFCP2,TFCP2,CP2-related factors,Q12800,HT-SELEX +MA0692.1.TFEB,TFEB,bHLH-ZIP factors,P19484,HT-SELEX +MA0693.1.Vdr,Vdr,Thyroid hormone receptor-related factors (NR1),P48281,HT-SELEX +MA0694.1.ZBTB7B,ZBTB7B,More than 3 adjacent zinc finger factors,O15156,HT-SELEX +MA0695.1.ZBTB7C,ZBTB7C,More than 3 adjacent zinc finger factors,A1YPR0,HT-SELEX +MA0696.1.ZIC1,ZIC1,More than 3 adjacent zinc finger factors,Q15915,HT-SELEX +MA0697.1.ZIC3,ZIC3,More than 3 adjacent zinc finger factors,O60481,HT-SELEX +MA0698.1.ZBTB18,ZBTB18,More than 3 adjacent zinc finger factors,Q99592,HT-SELEX +MA0699.1.LBX2,LBX2,NK-related factors,Q6XYB7,HT-SELEX +MA0700.1.LHX2,LHX2,HD-LIM factors,P50458,HT-SELEX +MA0701.1.LHX9,LHX9,HD-LIM factors,Q9NQ69,HT-SELEX +MA0702.1.LMX1A,LMX1A,HD-LIM factors,Q8TE12,HT-SELEX +MA0703.1.LMX1B,LMX1B,HD-LIM factors,O60663,HT-SELEX +MA0704.1.Lhx4,Lhx4,HD-LIM factors,P53776,HT-SELEX +MA0705.1.Lhx8,Lhx8,HD-LIM factors,O35652,HT-SELEX +MA0706.1.MEOX2,MEOX2,HOX-related factors,Q6FHY5,HT-SELEX +MA0707.1.MNX1,MNX1,HOX-related factors,P50219,HT-SELEX +MA0708.1.MSX2,MSX2,NK-related factors,P35548,HT-SELEX +MA0709.1.Msx3,Msx3,NK-related factors,P70354,HT-SELEX +MA0122.2.NKX3-2,NKX3-2,NK-related factors,P78367,HT-SELEX +MA0710.1.NOTO,NOTO,NK-related factors,A8MTQ0,HT-SELEX +MA0124.2.Nkx3-1,Nkx3-1,NK-related factors,P97436,HT-SELEX +MA0711.1.OTX1,OTX1,Paired-related HD factors,P32242,HT-SELEX +MA0712.1.OTX2,OTX2,Paired-related HD factors,P32243,HT-SELEX +MA0132.2.PDX1,PDX1,HOX-related factors,P52945,HT-SELEX +MA0713.1.PHOX2A,PHOX2A,Paired-related HD factors,O14813,HT-SELEX +MA0714.1.PITX3,PITX3,Paired-related HD factors,O75364,HT-SELEX +MA0715.1.PROP1,PROP1,Paired-related HD factors,O75360,HT-SELEX +MA0716.1.PRRX1,PRRX1,Paired-related HD factors,P54821,HT-SELEX +MA0717.1.RAX2,RAX2,Paired-related HD factors,Q96IS3,HT-SELEX +MA0718.1.RAX,RAX,Paired-related HD factors,Q9Y2V3,HT-SELEX +MA0719.1.RHOXF1,RHOXF1,Paired-related HD factors,Q8NHV9,HT-SELEX +MA0720.1.Shox2,Shox2,Paired-related HD factors,P70390,HT-SELEX +MA0721.1.UNCX,UNCX,Paired-related HD factors,A6NJT0,HT-SELEX +MA0722.1.VAX1,VAX1,NK-related factors,Q5SQQ9,HT-SELEX +MA0723.1.VAX2,VAX2,NK-related factors,Q9UIW0,HT-SELEX +MA0724.1.VENTX,VENTX,NK-related factors,O95231,HT-SELEX +MA0725.1.VSX1,VSX1,Paired-related HD factors,Q9NZR4,HT-SELEX +MA0726.1.VSX2,VSX2,Paired-related HD factors,P58304,HT-SELEX +MA0112.3.ESR1,ESR1,Steroid hormone receptors (NR3),P03372,HT-SELEX +MA0141.3.ESRRB,ESRRB,Steroid hormone receptors (NR3),O95718,HT-SELEX +MA0592.2.Esrra,Esrra,Steroid hormone receptors (NR3),O08580,HT-SELEX +MA0114.3.Hnf4a,Hnf4a,RXR-related receptors (NR2),P49698,HT-SELEX +MA0017.2.NR2F1,NR2F1,RXR-related receptors (NR2),P10589,HT-SELEX +MA0113.3.NR3C1,NR3C1,Steroid hormone receptors (NR3),P04150,HT-SELEX +MA0727.1.NR3C2,NR3C2,Steroid hormone receptors (NR3),P08235,HT-SELEX +MA0728.1.Nr2f6(var.2),Nr2f6(var.2),RXR-related receptors (NR2),P43136,HT-SELEX +MA0729.1.RARA,RARA,Thyroid hormone receptor-related factors (NR1),P10276,HT-SELEX +MA0730.1.RARA(var.2),RARA(var.2),Thyroid hormone receptor-related factors (NR1),P10276,HT-SELEX +MA0731.1.BCL6B,BCL6B,More than 3 adjacent zinc finger factors,A8KA13,HT-SELEX +MA0472.2.EGR2,EGR2,Three-zinc finger Krüppel-related factors,P11161,HT-SELEX +MA0732.1.EGR3,EGR3,Three-zinc finger Krüppel-related factors,Q06889,HT-SELEX +MA0733.1.EGR4,EGR4,Three-zinc finger Krüppel-related factors,Q05215,HT-SELEX +MA0734.1.GLI2,GLI2,More than 3 adjacent zinc finger factors,P10070,HT-SELEX +MA0735.1.GLIS1,GLIS1,More than 3 adjacent zinc finger factors,Q8NBF1,HT-SELEX +MA0736.1.GLIS2,GLIS2,More than 3 adjacent zinc finger factors,Q9BZE0,HT-SELEX +MA0737.1.GLIS3,GLIS3,More than 3 adjacent zinc finger factors,Q8NEA6,HT-SELEX +MA0738.1.HIC2,HIC2,Factors with multiple dispersed zinc fingers,Q96JB3,HT-SELEX +MA0739.1.Hic1,Hic1,Factors with multiple dispersed zinc fingers,Q9R1Y5,HT-SELEX +MA0740.1.KLF14,KLF14,Three-zinc finger Krüppel-related factors,Q8TD94,HT-SELEX +MA0741.1.KLF16,KLF16,Three-zinc finger Krüppel-related factors,Q9BXK1,HT-SELEX +MA0742.1.Klf12,Klf12,Three-zinc finger Krüppel-related factors,O35738,HT-SELEX +MA0743.1.SCRT1,SCRT1,More than 3 adjacent zinc finger factors,Q9BWW7,HT-SELEX +MA0744.1.SCRT2,SCRT2,More than 3 adjacent zinc finger factors,Q9NQ03,HT-SELEX +MA0745.1.SNAI2,SNAI2,More than 3 adjacent zinc finger factors,O43623,HT-SELEX +MA0746.1.SP3,SP3,Three-zinc finger Krüppel-related factors,Q02447,HT-SELEX +MA0747.1.SP8,SP8,Three-zinc finger Krüppel-related factors,Q8IXZ3,HT-SELEX +MA0748.1.YY2,YY2,More than 3 adjacent zinc finger factors,O15391,HT-SELEX +MA0749.1.ZBED1,ZBED1,BED zinc finger factors,O96006,HT-SELEX +MA0750.1.ZBTB7A,ZBTB7A,More than 3 adjacent zinc finger factors,O95365,HT-SELEX +MA0751.1.ZIC4,ZIC4,More than 3 adjacent zinc finger factors,Q8N9L1,HT-SELEX +MA0088.2.ZNF143,ZNF143,More than 3 adjacent zinc finger factors,P52747,HT-SELEX +MA0752.1.ZNF410,ZNF410,More than 3 adjacent zinc finger factors,Q86VK4,HT-SELEX +MA0753.1.ZNF740,ZNF740,Other factors with up to three adjacent zinc fingers,Q8NDX6,HT-SELEX +MA0754.1.CUX1,CUX1,HD-CUT factors,P39880,HT-SELEX +MA0755.1.CUX2,CUX2,HD-CUT factors,O14529,HT-SELEX +MA0756.1.ONECUT2,ONECUT2,HD-CUT factors,O95948,HT-SELEX +MA0757.1.ONECUT3,ONECUT3,HD-CUT factors,O60422,HT-SELEX +MA0469.2.E2F3,E2F3,E2F-related factors,O00716,HT-SELEX +MA0758.1.E2F7,E2F7,E2F-related factors,Q96AV8,HT-SELEX +MA0473.2.ELF1,ELF1,Ets-related factors,P32519,HT-SELEX +MA0759.1.ELK3,ELK3,Ets-related factors,P41970,HT-SELEX +MA0760.1.ERF,ERF,Ets-related factors,P50548,HT-SELEX +MA0474.2.ERG,ERG,Ets-related factors,P11308,HT-SELEX +MA0098.3.ETS1,ETS1,Ets-related factors,P14921,HT-SELEX +MA0761.1.ETV1,ETV1,Ets-related factors,P50549,HT-SELEX +MA0762.1.ETV2,ETV2,Ets-related factors,Q3KNT2,HT-SELEX +MA0763.1.ETV3,ETV3,Ets-related factors,P41162,HT-SELEX +MA0764.1.ETV4,ETV4,Ets-related factors,P43268,HT-SELEX +MA0765.1.ETV5,ETV5,Ets-related factors,P41161,HT-SELEX +MA0156.2.FEV,FEV,Ets-related factors,Q99581,HT-SELEX +MA0766.1.GATA5,GATA5,GATA-type zinc fingers,Q9BWX5,HT-SELEX +MA0767.1.GCM2,GCM2,GCM factors,O75603,HT-SELEX +MA0768.1.LEF1,LEF1,TCF-7-related factors,Q9UJU2,HT-SELEX +MA0769.1.Tcf7,Tcf7,TCF-7-related factors,Q00417,HT-SELEX +MA0770.1.HSF2,HSF2,HSF factors,Q03933,HT-SELEX +MA0771.1.HSF4,HSF4,HSF factors,Q9ULV5,HT-SELEX +MA0772.1.IRF7,IRF7,Interferon-regulatory factors,Q92985,HT-SELEX +MA0052.3.MEF2A,MEF2A,Regulators of differentiation,Q02078,HT-SELEX +MA0773.1.MEF2D,MEF2D,Regulators of differentiation,Q05BX2,HT-SELEX +MA0498.2.MEIS1,MEIS1,TALE-type homeo domain factors,O00470,HT-SELEX +MA0774.1.MEIS2,MEIS2,TALE-type homeo domain factors,O14770,HT-SELEX +MA0775.1.MEIS3,MEIS3,TALE-type homeo domain factors,Q99687,HT-SELEX +MA0776.1.MYBL1,MYBL1,Myb/SANT domain factors,P10243,HT-SELEX +MA0777.1.MYBL2,MYBL2,Myb/SANT domain factors,P10244,HT-SELEX +MA0105.4.NFKB1,NFKB1,NF-kappaB-related factors,P19838,HT-SELEX +MA0778.1.NFKB2,NFKB2,NF-kappaB-related factors,Q00653,HT-SELEX +MA0779.1.PAX1,PAX1,Paired domain only,P15863,HT-SELEX +MA0780.1.PAX3,PAX3,Paired plus homeo domain,P23760,HT-SELEX +MA0781.1.PAX9,PAX9,Paired domain only,P55771,HT-SELEX +MA0782.1.PKNOX1,PKNOX1,TALE-type homeo domain factors,P55347,HT-SELEX +MA0783.1.PKNOX2,PKNOX2,TALE-type homeo domain factors,Q96KN3,HT-SELEX +MA0784.1.POU1F1,POU1F1,POU domain factors,P28069,HT-SELEX +MA0785.1.POU2F1,POU2F1,POU domain factors,P14859,HT-SELEX +MA0786.1.POU3F1,POU3F1,POU domain factors,Q03052,HT-SELEX +MA0787.1.POU3F2,POU3F2,POU domain factors,P20265,HT-SELEX +MA0788.1.POU3F3,POU3F3,POU domain factors,P20264,HT-SELEX +MA0789.1.POU3F4,POU3F4,POU domain factors,P49335,HT-SELEX +MA0790.1.POU4F1,POU4F1,POU domain factors,Q01851,HT-SELEX +MA0791.1.POU4F3,POU4F3,POU domain factors,Q15319,HT-SELEX +MA0792.1.POU5F1B,POU5F1B,POU domain factors,Q06416,HT-SELEX +MA0793.1.POU6F2,POU6F2,POU domain factors,P78424,HT-SELEX +MA0794.1.PROX1,PROX1,HD-PROS factors,Q92786,HT-SELEX +MA0600.2.RFX2,RFX2,RFX-related factors,P48378,HT-SELEX +MA0795.1.SMAD3,SMAD3,SMAD factors,P84022,HT-SELEX +MA0796.1.TGIF1,TGIF1,TALE-type homeo domain factors,Q15583,HT-SELEX +MA0797.1.TGIF2,TGIF2,TALE-type homeo domain factors,Q9GZN2,HT-SELEX +MA0798.1.RFX3,RFX3,RFX-related factors,P48380,HT-SELEX +MA0799.1.RFX4,RFX4,RFX-related factors,Q33E94,HT-SELEX +MA0510.2.RFX5,RFX5,RFX-related factors,P48382,HT-SELEX +MA0511.2.RUNX2,RUNX2,Runt-related factors,Q13950,HT-SELEX +MA0800.1.EOMES,EOMES,TBrain-related factors,O95936,HT-SELEX +MA0801.1.MGA,MGA,TBX6-related factors,Q8IWI9,HT-SELEX +MA0802.1.TBR1,TBR1,TBrain-related factors,Q16650,HT-SELEX +MA0803.1.TBX15,TBX15,TBX1-related factors,Q96SF7,HT-SELEX +MA0804.1.TBX19,TBX19,Brachyury-related factors,O60806,HT-SELEX +MA0805.1.TBX1,TBX1,TBX1-related factors,O43435,HT-SELEX +MA0806.1.TBX4,TBX4,TBX2-related factors,P57082,HT-SELEX +MA0807.1.TBX5,TBX5,TBX2-related factors,Q99593,HT-SELEX +MA0808.1.TEAD3,TEAD3,TEF-1-related factors,Q99594,HT-SELEX +MA0809.1.TEAD4,TEAD4,TEF-1-related factors,Q15561,HT-SELEX +MA0810.1.TFAP2A(var.2),TFAP2A(var.2),AP-2,P05549,HT-SELEX +MA0003.3.TFAP2A,TFAP2A,AP-2,P05549,HT-SELEX +MA0811.1.TFAP2B,TFAP2B,AP-2,Q92481,HT-SELEX +MA0812.1.TFAP2B(var.2),TFAP2B(var.2),AP-2,Q92481,HT-SELEX +MA0813.1.TFAP2B(var.3),TFAP2B(var.3),AP-2,Q92481,HT-SELEX +MA0524.2.TFAP2C,TFAP2C,AP-2,Q92754,HT-SELEX +MA0814.1.TFAP2C(var.2),TFAP2C(var.2),AP-2,Q92754,HT-SELEX +MA0815.1.TFAP2C(var.3),TFAP2C(var.3),AP-2,Q92754,HT-SELEX +MA0816.1.Ascl2,Ascl2,MyoD / ASC-related factors,O35885,HT-SELEX +MA0461.2.Atoh1,Atoh1,Tal-related factors,P48985,HT-SELEX +MA0464.2.BHLHE40,BHLHE40,Hairy-related factors,O14503,HT-SELEX +MA0817.1.BHLHE23,BHLHE23,Tal-related factors,Q8NDY6,HT-SELEX +MA0818.1.BHLHE22,BHLHE22,Tal-related factors,Q8NFJ8,HT-SELEX +MA0819.1.CLOCK,CLOCK,PAS domain factors,O15516,HT-SELEX +MA0820.1.FIGLA,FIGLA,Tal-related factors,Q6QHK4,HT-SELEX +MA0821.1.HES5,HES5,Hairy-related factors,Q5TA89,HT-SELEX +MA0822.1.HES7,HES7,Hairy-related factors,Q9BYE0,HT-SELEX +MA0823.1.HEY1,HEY1,Hairy-related factors,Q9Y5J3,HT-SELEX +MA0824.1.ID4,ID4,HLH domain only,P47928,HT-SELEX +MA0058.3.MAX,MAX,bHLH-ZIP factors,P61244,HT-SELEX +MA0825.1.MNT,MNT,bHLH-ZIP factors,Q99583,HT-SELEX +MA0826.1.OLIG1,OLIG1,Tal-related factors,Q8TAK6,HT-SELEX +MA0827.1.OLIG3,OLIG3,Tal-related factors,Q7RTU3,HT-SELEX +MA0828.1.SREBF2(var.2),SREBF2(var.2),bHLH-ZIP factors,Q12772,HT-SELEX +MA0829.1.Srebf1(var.2),Srebf1(var.2),bHLH-ZIP factors,Q9WTN3,HT-SELEX +MA0522.2.TCF3,TCF3,E2A-related factors,P15923,HT-SELEX +MA0830.1.TCF4,TCF4,E2A-related factors,P15884,HT-SELEX +MA0831.1.TFE3,TFE3,bHLH-ZIP factors,P19532,HT-SELEX +MA0832.1.Tcf21,Tcf21,Tal-related factors,O35437,HT-SELEX +MA0833.1.ATF4,ATF4,ATF-4-related factors,P18848,HT-SELEX +MA0834.1.ATF7,ATF7,Jun-related factors,P17544,HT-SELEX +MA0835.1.BATF3,BATF3,B-ATF-related factors,Q9NR55,HT-SELEX +MA0466.2.CEBPB,CEBPB,C/EBP-related,P17676,HT-SELEX +MA0836.1.CEBPD,CEBPD,C/EBP-related,P49716,HT-SELEX +MA0837.1.CEBPE,CEBPE,C/EBP-related,Q15744,HT-SELEX +MA0838.1.CEBPG,CEBPG,C/EBP-related,P53567,HT-SELEX +MA0839.1.CREB3L1,CREB3L1,CREB-related factors,Q96BA8,HT-SELEX +MA0840.1.Creb5,Creb5,CREB-related factors,Q8K1L0,HT-SELEX +MA0117.2.Mafb,Mafb,Maf-related factors,P54841,HT-SELEX +MA0841.1.NFE2,NFE2,Jun-related factors,Q16621,HT-SELEX +MA0842.1.NRL,NRL,Maf-related factors,P54845,HT-SELEX +MA0843.1.TEF,TEF,TEF-1-related factors,Q10587,HT-SELEX +MA0844.1.XBP1,XBP1,XBP-1-related factors,P17861,HT-SELEX +MA0845.1.FOXB1,FOXB1,Forkhead box (FOX) factors,Q99853,HT-SELEX +MA0032.2.FOXC1,FOXC1,Forkhead box (FOX) factors,Q12948,HT-SELEX +MA0846.1.FOXC2,FOXC2,Forkhead box (FOX) factors,Q99958,HT-SELEX +MA0847.1.FOXD2,FOXD2,Forkhead box (FOX) factors,O60548,HT-SELEX +MA0848.1.FOXO4,FOXO4,Forkhead box (FOX) factors,P98177,HT-SELEX +MA0849.1.FOXO6,FOXO6,Forkhead box (FOX) factors,A8MYZ6,HT-SELEX +MA0850.1.FOXP3,FOXP3,Forkhead box (FOX) factors,B7ZLG1,HT-SELEX +MA0851.1.Foxj3,Foxj3,Forkhead box (FOX) factors,Q8BUR3,universal protein binding microarray (PBM) +MA0852.1.Foxk1,Foxk1,Forkhead box (FOX) factors,P42128,universal protein binding microarray (PBM) +MA0853.1.Alx4,Alx4,Paired-related HD factors,O35137,universal protein binding microarray (PBM) +MA0854.1.Alx1,Alx1,Paired-related HD factors,Q8C8B0,universal protein binding microarray (PBM) +MA0855.1.RXRB,RXRB,RXR-related receptors (NR2),P28702,HT-SELEX +MA0856.1.RXRG,RXRG,RXR-related receptors (NR2),P48443,HT-SELEX +MA0857.1.Rarb,Rarb,Thyroid hormone receptor-related factors (NR1),P22605,HT-SELEX +MA0858.1.Rarb(var.2),Rarb(var.2),Thyroid hormone receptor-related factors (NR1),P22605,HT-SELEX +MA0859.1.Rarg,Rarg,Thyroid hormone receptor-related factors (NR1),P18911,HT-SELEX +MA0860.1.Rarg(var.2),Rarg(var.2),Thyroid hormone receptor-related factors (NR1),P18911,HT-SELEX +MA0525.2.TP63,TP63,p53-related factors,Q9H3D4,HT-SELEX +MA0106.3.TP53,TP53,p53-related factors,P04637,HT-SELEX +MA0861.1.TP73,TP73,p53-related factors,O15350,HT-SELEX +MA0862.1.GMEB2,GMEB2,GMEB,Q9UKD1,HT-SELEX +MA0863.1.MTF1,MTF1,More than 3 adjacent zinc finger factors,Q14872,HT-SELEX +MA0024.3.E2F1,E2F1,E2F-related factors,Q01094,HT-SELEX +MA0864.1.E2F2,E2F2,E2F-related factors,Q14209,HT-SELEX +MA0865.1.E2F8,E2F8,E2F-related factors,A0AVK6,HT-SELEX +MA0866.1.SOX21,SOX21,SOX-related factors,Q9Y651,HT-SELEX +MA0867.1.SOX4,SOX4,SOX-related factors,Q06945,HT-SELEX +MA0868.1.SOX8,SOX8,SOX-related factors,P57073,HT-SELEX +MA0869.1.Sox11,Sox11,SOX-related factors,Q7M6Y2,HT-SELEX +MA0870.1.Sox1,Sox1,SOX-related factors,P53783,HT-SELEX +MA0871.1.TFEC,TFEC,bHLH-ZIP factors,O14948,HT-SELEX +MA0872.1.TFAP2A(var.3),TFAP2A(var.3),AP-2,P05549,HT-SELEX +MA0028.2.ELK1,ELK1,Ets-related factors,P19419,HT-SELEX +MA0873.1.HOXD12,HOXD12,HOX-related factors,P35452,HT-SELEX +MA0874.1.Arx,Arx,Paired-related HD factors,O35085,universal protein binding microarray (PBM) +MA0875.1.BARX1,BARX1,NK-related factors,Q9HBU1,HT-SELEX +MA0876.1.BSX,BSX,NK-related factors,Q3C1V8,HT-SELEX +MA0877.1.Barhl1,Barhl1,NK-related factors,P63157,HT-SELEX +MA0878.1.CDX1,CDX1,HOX-related factors,P47902,HT-SELEX +MA0879.1.Dlx1,Dlx1,NK-related factors,Q64317,PBM +MA0880.1.Dlx3,Dlx3,NK-related factors,Q64205,PBM +MA0881.1.Dlx4,Dlx4,NK-related factors,P70436,PBM +MA0882.1.DLX6,DLX6,NK-related factors,P56179,HT-SELEX +MA0883.1.Dmbx1,Dmbx1,Paired-related HD factors,Q91ZK4,universal protein binding microarray (PBM) +MA0884.1.DUXA,DUXA,Paired-related HD factors,A6NLW8,HT-SELEX +MA0885.1.Dlx2,Dlx2,NK-related factors,P40764,HT-SELEX +MA0886.1.EMX2,EMX2,NK-related factors,Q04743,HT-SELEX +MA0887.1.EVX1,EVX1,HOX-related factors,P49640,HT-SELEX +MA0888.1.EVX2,EVX2,HOX-related factors,Q03828,HT-SELEX +MA0889.1.GBX1,GBX1,HOX-related factors,Q14549,HT-SELEX +MA0890.1.GBX2,GBX2,HOX-related factors,P52951,HT-SELEX +MA0891.1.GSC2,GSC2,Paired-related HD factors,O15499,HT-SELEX +MA0892.1.GSX1,GSX1,HOX-related factors,A4IFQ3,HT-SELEX +MA0893.1.GSX2,GSX2,HOX-related factors,Q9BZM3,HT-SELEX +MA0894.1.HESX1,HESX1,Paired-related HD factors,Q9UBX0,HT-SELEX +MA0895.1.HMBOX1,HMBOX1,POU domain factors,Q6NT76,HT-SELEX +MA0896.1.Hmx1,Hmx1,NK-related factors,O70218,universal protein binding microarray (PBM) +MA0897.1.Hmx2,Hmx2,NK-related factors,P43687,universal protein binding microarray (PBM) +MA0898.1.Hmx3,Hmx3,NK-related factors,P42581,universal protein binding microarray (PBM) +MA0899.1.HOXA10,HOXA10,HOX-related factors,P31260,HT-SELEX +MA0900.1.HOXA2,HOXA2,HOX-related factors,O43364,HT-SELEX +MA0901.1.HOXB13,HOXB13,HOX-related factors,Q92826,HT-SELEX +MA0902.1.HOXB2,HOXB2,HOX-related factors,P14652,HT-SELEX +MA0903.1.HOXB3,HOXB3,HOX-related factors,P14651,HT-SELEX +MA0904.1.Hoxb5,Hoxb5,HOX-related factors,P09079,universal protein binding microarray (PBM) +MA0905.1.HOXC10,HOXC10,HOX-related factors,Q9NYD6,HT-SELEX +MA0906.1.HOXC12,HOXC12,HOX-related factors,P31275,HT-SELEX +MA0907.1.HOXC13,HOXC13,HOX-related factors,P31276,HT-SELEX +MA0908.1.HOXD11,HOXD11,HOX-related factors,P31277,HT-SELEX +MA0909.1.HOXD13,HOXD13,HOX-related factors,P35453,HT-SELEX +MA0910.1.Hoxd8,Hoxd8,HOX-related factors,P23463,universal protein binding microarray (PBM) +MA0911.1.Hoxa11,Hoxa11,HOX-related factors,P31270,HT-SELEX +MA0912.1.Hoxd3,Hoxd3,HOX-related factors,P09027,universal protein binding microarray (PBM) +MA0913.1.Hoxd9,Hoxd9,HOX-related factors,P28357,HT-SELEX +MA0914.1.ISL2,ISL2,HD-LIM factors,Q96A47,HT-SELEX +MA1100.1.ASCL1,ASCL1,MyoD / ASC-related factors,P50553,ChIP-seq +MA1101.1.BACH2,BACH2,Jun-related factors,Q9BYV9,ChIP-seq +MA0018.3.CREB1,CREB1,CREB-related factors,P16220,ChIP-seq +MA1102.1.CTCFL,CTCFL,More than 3 adjacent zinc finger factors,Q8NI51,ChIP-seq +MA0852.2.FOXK1,FOXK1,Forkhead box (FOX) factors,P85037,ChIP-seq +MA1103.1.FOXK2,FOXK2,Forkhead box (FOX) factors,Q01167,ChIP-seq +MA0481.2.FOXP1,FOXP1,Forkhead box (FOX) factors,Q9H334,ChIP-seq +MA0036.3.GATA2,GATA2,GATA-type zinc fingers,P23769,ChIP-seq +MA1104.1.GATA6,GATA6,GATA-type zinc fingers,Q92908,ChIP-seq +MA1105.1.GRHL2,GRHL2,Grainyhead-related factors,Q6ISB3,ChIP-seq +MA1106.1.HIF1A,HIF1A,PAS domain factors,Q16665,ChIP-seq +MA0039.3.KLF4,KLF4,Three-zinc finger Kruppel-related factors,O43474,ChIP-seq +MA1107.1.KLF9,KLF9,Three-zinc finger Kruppel-related factors,Q13886,ChIP-seq +MA0495.2.MAFF,MAFF,Maf-related factors,Q9ULX9,ChIP-seq +MA0496.2.MAFK,MAFK,Maf-related factors,O60675,ChIP-seq +MA0620.2.MITF,MITF,bHLH-ZIP factors,O75030,ChIP-seq +MA1108.1.MXI1,MXI1,bHLH-ZIP factors,P50539,ChIP-seq +MA0147.3.MYC,MYC,bHLH-ZIP factors,P01106,ChIP-seq +MA0100.3.MYB,MYB,Myb/SANT domain factors,P10242,ChIP-seq +MA0104.4.MYCN,MYCN,bHLH-ZIP factors,P04198,ChIP-seq +MA1109.1.NEUROD1,NEUROD1,Tal-related factors,Q13562,ChIP-seq +MA0161.2.NFIC,NFIC,Nuclear factor 1,P08651,ChIP-seq +MA0060.3.NFYA,NFYA,NFY,P23511,ChIP-seq +MA1110.1.NR1H4,NR1H4,Thyroid hormone receptor-related factors (NR1),Q96RI1,ChIP-seq +MA1111.1.NR2F2,NR2F2,RXR-related receptors (NR2),P24468,ChIP-seq +MA1112.1.NR4A1,NR4A1,NGFI-B-related receptors (NR4),P22736,ChIP-seq +MA0014.3.PAX5,PAX5,Paired domain only,Q02548,ChIP-seq +MA1113.1.PBX2,PBX2,TALE-type homeo domain factors,P40425,ChIP-seq +MA1114.1.PBX3,PBX3,TALE-type homeo domain factors,P40426,ChIP-seq +MA1115.1.POU5F1,POU5F1,POU domain factors,Q01860,ChIP-seq +MA0508.2.PRDM1,PRDM1,More than 3 adjacent zinc finger factors,O75626,ChIP-seq +MA1116.1.RBPJ,RBPJ,CSL-related factors,Q06330,ChIP-seq +MA1117.1.RELB,RELB,NF-kappaB-related factors,Q01201,ChIP-seq +MA1118.1.SIX1,SIX1,HD-SINE factors,Q15475,ChIP-seq +MA1119.1.SIX2,SIX2,HD-SINE factors,Q9NPC8,ChIP-seq +MA0442.2.SOX10,SOX10,SOX-related factors,P56693,ChIP-seq +MA1120.1.SOX13,SOX13,SOX-related factors,Q9UN79,ChIP-seq +MA1121.1.TEAD2,TEAD2,TEF-1-related factors,Q15562,ChIP-seq +MA1122.1.TFDP1,TFDP1,E2F-related factors,Q14186,ChIP-seq +MA1123.1.TWIST1,TWIST1,Tal-related factors,Q15672,ChIP-seq +MA0526.2.USF2,USF2,bHLH-ZIP factors,Q15853,ChIP-seq +MA0750.2.ZBTB7A,ZBTB7A,More than 3 adjacent zinc finger factors,O95365,ChIP-seq +MA0103.3.ZEB1,ZEB1,HD-ZF factors,P37275,ChIP-seq +MA1124.1.ZNF24,ZNF24,More than 3 adjacent zinc finger factors,P17028,ChIP-seq +MA1125.1.ZNF384,ZNF384,More than 3 adjacent zinc finger factors,Q8TF68,ChIP-seq +MA1154.1.ZNF282,ZNF282,More than 3 adjacent zinc finger factors,Q9UDV7,HT-SELEX +MA1155.1.ZSCAN4,ZSCAN4,More than 3 adjacent zinc finger factors,Q8NAM6,HT-SELEX +MA0037.3.GATA3,GATA3,GATA-type zinc fingers,P23771,SMiLE-seq +MA0099.3.FOS::JUN,FOS+JUN,Fos-related factors;Jun-related factors,P01100;P05412,SMiLE-seq +MA1126.1.FOS::JUN(var.2),FOS+JUN(var.2),Fos-related factors;Jun-related factors,P01100;P05412,SMiLE-seq +MA1127.1.FOSB::JUN,FOSB+JUN,Fos-related factors;Jun-related factors,P53539;P05412,SMiLE-seq +MA1128.1.FOSL1::JUN,FOSL1+JUN,Fos-related factors;Jun-related factors,P15407;P05412,SMiLE-seq +MA1129.1.FOSL1::JUN(var.2),FOSL1+JUN(var.2),Fos-related factors;Jun-related factors,P15407;P05412,SMiLE-seq +MA1130.1.FOSL2::JUN,FOSL2+JUN,Fos-related factors;Jun-related factors,P15408;P05412,SMiLE-seq +MA1131.1.FOSL2::JUN(var.2),FOSL2+JUN(var.2),Fos-related factors;Jun-related factors,P15408;P05412,SMiLE-seq +MA1132.1.JUN::JUNB,JUN+JUNB,Jun-related factors,P05412;P17275,SMiLE-seq +MA1133.1.JUN::JUNB(var.2),JUN+JUNB(var.2),Jun-related factors,P05412;P17275,SMiLE-seq +MA1134.1.FOS::JUNB,FOS+JUNB,Fos-related factors;Jun-related factors,P01100;P17275,SMiLE-seq +MA1135.1.FOSB::JUNB,FOSB+JUNB,Fos-related factors;Jun-related factors,P53539;P17275,SMiLE-seq +MA1136.1.FOSB::JUNB(var.2),FOSB+JUNB(var.2),Fos-related factors;Jun-related factors,P53539;P17275,SMiLE-seq +MA1137.1.FOSL1::JUNB,FOSL1+JUNB,Fos-related factors;Jun-related factors,P15407;P17275,SMiLE-seq +MA1138.1.FOSL2::JUNB,FOSL2+JUNB,Fos-related factors;Jun-related factors,P15408;P17275,SMiLE-seq +MA1139.1.FOSL2::JUNB(var.2),FOSL2+JUNB(var.2),Fos-related factors;Jun-related factors,P15408;P17275,SMiLE-seq +MA1140.1.JUNB(var.2),JUNB(var.2),Jun-related factors,P17275,SMiLE-seq +MA1141.1.FOS::JUND,FOS+JUND,Fos-related factors;Jun-related factors,P01100;P17535,SMiLE-seq +MA1142.1.FOSL1::JUND,FOSL1+JUND,Fos-related factors;Jun-related factors,P15407;P17535,SMiLE-seq +MA1143.1.FOSL1::JUND(var.2),FOSL1+JUND(var.2),Fos-related factors;Jun-related factors,P15407;P17535,SMiLE-seq +MA1144.1.FOSL2::JUND,FOSL2+JUND,Fos-related factors;Jun-related factors,P15408;P17535,SMiLE-seq +MA1145.1.FOSL2::JUND(var.2),FOSL2+JUND(var.2),Fos-related factors;Jun-related factors,P15408;P17535,SMiLE-seq +MA1146.1.NR1A4::RXRA,NR1A4+RXRA,RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1),Q96RI1;P19793,SMiLE-seq +MA1147.1.NR4A2::RXRA,NR4A2+RXRA,NGFI-B-related receptors (NR4);RXR-related receptors (NR2),P43354;P19793,SMiLE-seq +MA1148.1.PPARA::RXRA,PPARA+RXRA,RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1),Q07869;P19793,SMiLE-seq +MA1149.1.RARA::RXRG,RARA+RXRG,RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1),P10276;P48443,SMiLE-seq +MA1150.1.RORB,RORB,Thyroid hormone receptor-related factors (NR1),Q92753,SMiLE-seq +MA1151.1.RORC,RORC,Thyroid hormone receptor-related factors (NR1),P51449,SMiLE-seq +MA1152.1.SOX15,SOX15,SOX-related factors,O60248,SMiLE-seq +MA0831.2.TFE3,TFE3,bHLH-ZIP factors,P19532,SMiLE-seq +MA0693.2.VDR,VDR,Thyroid hormone receptor-related factors (NR1),P11473,SMiLE-seq +MA1153.1.Smad4,Smad4,SMAD factors,P97471,SMiLE-seq +MA0162.3.EGR1,EGR1,Three-zinc finger Kruppel-related factors,P18146,HT-SELEX +MA1418.1.IRF3,IRF3,Interferon-regulatory factors,Q14653,HT-SELEX +MA1419.1.IRF4,IRF4,Interferon-regulatory factors,Q15306,HT-SELEX +MA1420.1.IRF5,IRF5,Interferon-regulatory factors,Q13568,HT-SELEX +MA1421.1.TCF7L1,TCF7L1,TCF-7-related factors,Q9HCS4,HT-SELEX + diff --git a/data/motifs/jaspar_anno_query.sql b/data/motifs/jaspar_anno_query.sql new file mode 100644 index 000000000..5c4f9e194 --- /dev/null +++ b/data/motifs/jaspar_anno_query.sql @@ -0,0 +1,25 @@ +.header on +.mode csv + +SELECT + trim((m.BASE_ID || '.' || m.VERSION || '.' || m.NAME)) AS TfName, + trim(replace(m.NAME, '::', '+')) as GeneName, + trim(replace(GROUP_CONCAT(DISTINCT CASE WHEN a.TAG = 'family' THEN a.VAL END),',',';')) AS Family, + trim(replace(GROUP_CONCAT(DISTINCT p.ACC),',',';')) AS UniProt, + trim(GROUP_CONCAT(DISTINCT CASE WHEN a.tag = 'type' THEN a.val END)) AS Source +FROM MATRIX m + JOIN (SELECT * + FROM MATRIX_ANNOTATION a2 + WHERE NOT exists + ( + SELECT * + FROM MATRIX_ANNOTATION a3 + WHERE a2.ID = a3.ID AND a3.TAG = 'tax_group' AND a3.VAL <> 'vertebrates' + ) + ORDER BY ID) a + ON m.ID = a.ID + JOIN MATRIX_PROTEIN p + ON m.ID = p.ID +WHERE m.COLLECTION = 'CORE' +GROUP BY m.ID, m.COLLECTION, m.BASE_ID, m.VERSION, m.NAME +ORDER BY m.ID; diff --git a/data/motifs/jaspar_vertebrates.mtf b/data/motifs/jaspar_vertebrates.mtf index f9a77d7ff..05fd23578 100644 --- a/data/motifs/jaspar_vertebrates.mtf +++ b/data/motifs/jaspar_vertebrates.mtf @@ -1,519 +1,579 @@ -MA0002 MA0002.2.RUNX1 jaspar_vertebrates 1 RUNX1 . -MA0003 MA0003.3.TFAP2A jaspar_vertebrates 1 TFAP2A . -MA0004 MA0004.1.Arnt jaspar_vertebrates 1 Arnt . -MA0006 MA0006.1.Ahr::Arnt jaspar_vertebrates 1 Ahr+Arnt . -MA0007 MA0007.3.Ar jaspar_vertebrates 1 Ar . -MA0009 MA0009.2.T jaspar_vertebrates 1 T . -MA0014 MA0014.2.PAX5 jaspar_vertebrates 1 PAX5 . -MA0017 MA0017.2.NR2F1 jaspar_vertebrates 1 NR2F1 . -MA0018 MA0018.2.CREB1 jaspar_vertebrates 1 CREB1 . -MA0019 MA0019.1.Ddit3::Cebpa jaspar_vertebrates 1 Ddit3+Cebpa . -MA0024 MA0024.3.E2F1 jaspar_vertebrates 1 E2F1 . -MA0025 MA0025.1.NFIL3 jaspar_vertebrates 1 NFIL3 . -MA0027 MA0027.2.EN1 jaspar_vertebrates 1 EN1 . -MA0028 MA0028.2.ELK1 jaspar_vertebrates 1 ELK1 . -MA0029 MA0029.1.Mecom jaspar_vertebrates 1 Mecom . -MA0030 MA0030.1.FOXF2 jaspar_vertebrates 1 FOXF2 . -MA0031 MA0031.1.FOXD1 jaspar_vertebrates 1 FOXD1 . -MA0032 MA0032.2.FOXC1 jaspar_vertebrates 1 FOXC1 . -MA0033 MA0033.2.FOXL1 jaspar_vertebrates 1 FOXL1 . -MA0035 MA0035.3.Gata1 jaspar_vertebrates 1 Gata1 . -MA0036 MA0036.2.GATA2 jaspar_vertebrates 1 GATA2 . -MA0037 MA0037.2.GATA3 jaspar_vertebrates 1 GATA3 . -MA0038 MA0038.1.Gfi1 jaspar_vertebrates 1 Gfi1 . -MA0039 MA0039.2.Klf4 jaspar_vertebrates 1 Klf4 . -MA0040 MA0040.1.Foxq1 jaspar_vertebrates 1 Foxq1 . -MA0041 MA0041.1.Foxd3 jaspar_vertebrates 1 Foxd3 . -MA0042 MA0042.2.FOXI1 jaspar_vertebrates 1 FOXI1 . -MA0043 MA0043.2.HLF jaspar_vertebrates 1 HLF . -MA0046 MA0046.2.HNF1A jaspar_vertebrates 1 HNF1A . -MA0047 MA0047.2.Foxa2 jaspar_vertebrates 1 Foxa2 . -MA0048 MA0048.2.NHLH1 jaspar_vertebrates 1 NHLH1 . -MA0050 MA0050.2.IRF1 jaspar_vertebrates 1 IRF1 . -MA0051 MA0051.1.IRF2 jaspar_vertebrates 1 IRF2 . -MA0052 MA0052.3.MEF2A jaspar_vertebrates 1 MEF2A . -MA0056 MA0056.1.MZF1 jaspar_vertebrates 1 MZF1 . -MA0057 MA0057.1.MZF1.var.2 jaspar_vertebrates pwm MZF1.var . -MA0058 MA0058.3.MAX jaspar_vertebrates 1 MAX . -MA0059 MA0059.1.MAX::MYC jaspar_vertebrates 1 MAX+MYC . -MA0060 MA0060.2.NFYA jaspar_vertebrates 1 NFYA . -MA0062 MA0062.2.Gabpa jaspar_vertebrates 1 Gabpa . -MA0063 MA0063.1.Nkx2-5 jaspar_vertebrates 1 Nkx2-5 . -MA0065 MA0065.2.Pparg::Rxra jaspar_vertebrates 1 Pparg+Rxra . -MA0066 MA0066.1.PPARG jaspar_vertebrates 1 PPARG . -MA0067 MA0067.1.Pax2 jaspar_vertebrates 1 Pax2 . -MA0068 MA0068.2.PAX4 jaspar_vertebrates 1 PAX4 . -MA0069 MA0069.1.Pax6 jaspar_vertebrates 1 Pax6 . -MA0070 MA0070.1.PBX1 jaspar_vertebrates 1 PBX1 . -MA0071 MA0071.1.RORA jaspar_vertebrates 1 RORA . -MA0072 MA0072.1.RORA.var.2 jaspar_vertebrates pwm RORA.var . -MA0073 MA0073.1.RREB1 jaspar_vertebrates 1 RREB1 . -MA0074 MA0074.1.RXRA::VDR jaspar_vertebrates 1 RXRA+VDR . -MA0075 MA0075.2.Prrx2 jaspar_vertebrates 1 Prrx2 . -MA0076 MA0076.2.ELK4 jaspar_vertebrates 1 ELK4 . -MA0077 MA0077.1.SOX9 jaspar_vertebrates 1 SOX9 . -MA0078 MA0078.1.Sox17 jaspar_vertebrates 1 Sox17 . -MA0079 MA0079.3.SP1 jaspar_vertebrates 1 SP1 . -MA0080 MA0080.4.SPI1 jaspar_vertebrates 1 SPI1 . -MA0081 MA0081.1.SPIB jaspar_vertebrates 1 SPIB . -MA0083 MA0083.3.SRF jaspar_vertebrates 1 SRF . -MA0084 MA0084.1.SRY jaspar_vertebrates 1 SRY . -MA0087 MA0087.1.Sox5 jaspar_vertebrates 1 Sox5 . -MA0088 MA0088.2.ZNF143 jaspar_vertebrates 1 ZNF143 . -MA0089 MA0089.1.MAFG::NFE2L1 jaspar_vertebrates 1 MAFG+NFE2L1 . -MA0090 MA0090.2.TEAD1 jaspar_vertebrates 1 TEAD1 . -MA0091 MA0091.1.TAL1::TCF3 jaspar_vertebrates 1 TAL1+TCF3 . -MA0092 MA0092.1.Hand1::Tcf3 jaspar_vertebrates 1 Hand1+Tcf3 . -MA0093 MA0093.2.USF1 jaspar_vertebrates 1 USF1 . -MA0095 MA0095.2.YY1 jaspar_vertebrates 1 YY1 . -MA0098 MA0098.3.ETS1 jaspar_vertebrates 1 ETS1 . -MA0099 MA0099.2.FOS::JUN jaspar_vertebrates 1 FOS+JUN . -MA0100 MA0100.2.Myb jaspar_vertebrates 1 Myb . -MA0101 MA0101.1.REL jaspar_vertebrates 1 REL . -MA0102 MA0102.3.CEBPA jaspar_vertebrates 1 CEBPA . -MA0103 MA0103.2.ZEB1 jaspar_vertebrates 1 ZEB1 . -MA0104 MA0104.3.Mycn jaspar_vertebrates 1 Mycn . -MA0105 MA0105.4.NFKB1 jaspar_vertebrates 1 NFKB1 . -MA0106 MA0106.3.TP53 jaspar_vertebrates 1 TP53 . -MA0107 MA0107.1.RELA jaspar_vertebrates 1 RELA . -MA0108 MA0108.2.TBP jaspar_vertebrates 1 TBP . -MA0109 MA0109.1.HLTF jaspar_vertebrates 1 HLTF . -MA0111 MA0111.1.Spz1 jaspar_vertebrates 1 Spz1 . -MA0112 MA0112.3.ESR1 jaspar_vertebrates 1 ESR1 . -MA0113 MA0113.3.NR3C1 jaspar_vertebrates 1 NR3C1 . -MA0114 MA0114.3.Hnf4a jaspar_vertebrates 1 Hnf4a . -MA0115 MA0115.1.NR1H2::RXRA jaspar_vertebrates 1 NR1H2+RXRA . -MA0116 MA0116.1.Znf423 jaspar_vertebrates 1 Znf423 . -MA0117 MA0117.2.Mafb jaspar_vertebrates 1 Mafb . -MA0119 MA0119.1.NFIC::TLX1 jaspar_vertebrates 1 NFIC+TLX1 . -MA0122 MA0122.2.NKX3-2 jaspar_vertebrates 1 NKX3-2 . -MA0124 MA0124.2.Nkx3-1 jaspar_vertebrates 1 Nkx3-1 . -MA0125 MA0125.1.Nobox jaspar_vertebrates 1 Nobox . -MA0130 MA0130.1.ZNF354C jaspar_vertebrates 1 ZNF354C . -MA0131 MA0131.2.HINFP jaspar_vertebrates 1 HINFP . -MA0132 MA0132.2.PDX1 jaspar_vertebrates 1 PDX1 . -MA0135 MA0135.1.Lhx3 jaspar_vertebrates 1 Lhx3 . -MA0136 MA0136.2.ELF5 jaspar_vertebrates 1 ELF5 . -MA0137 MA0137.3.STAT1 jaspar_vertebrates 1 STAT1 . -MA0138 MA0138.2.REST jaspar_vertebrates 1 REST . -MA0139 MA0139.1.CTCF jaspar_vertebrates 1 CTCF . -MA0140 MA0140.2.GATA1::TAL1 jaspar_vertebrates 1 GATA1+TAL1 . -MA0141 MA0141.3.ESRRB jaspar_vertebrates 1 ESRRB . -MA0142 MA0142.1.Pou5f1::Sox2 jaspar_vertebrates 1 Pou5f1+Sox2 . -MA0143 MA0143.3.Sox2 jaspar_vertebrates 1 Sox2 . -MA0144 MA0144.2.STAT3 jaspar_vertebrates 1 STAT3 . -MA0145 MA0145.3.TFCP2 jaspar_vertebrates 1 TFCP2 . -MA0146 MA0146.2.Zfx jaspar_vertebrates 1 Zfx . -MA0147 MA0147.2.Myc jaspar_vertebrates 1 Myc . -MA0148 MA0148.3.FOXA1 jaspar_vertebrates 1 FOXA1 . -MA0149 MA0149.1.EWSR1-FLI1 jaspar_vertebrates 1 EWSR1-FLI1 . -MA0150 MA0150.2.Nfe2l2 jaspar_vertebrates 1 Nfe2l2 . -MA0151 MA0151.1.Arid3a jaspar_vertebrates 1 Arid3a . -MA0152 MA0152.1.NFATC2 jaspar_vertebrates 1 NFATC2 . -MA0153 MA0153.2.HNF1B jaspar_vertebrates 1 HNF1B . -MA0154 MA0154.3.EBF1 jaspar_vertebrates 1 EBF1 . -MA0155 MA0155.1.INSM1 jaspar_vertebrates 1 INSM1 . -MA0156 MA0156.2.FEV jaspar_vertebrates 1 FEV . -MA0157 MA0157.2.FOXO3 jaspar_vertebrates 1 FOXO3 . -MA0158 MA0158.1.HOXA5 jaspar_vertebrates 1 HOXA5 . -MA0159 MA0159.1.RARA::RXRA jaspar_vertebrates 1 RARA+RXRA . -MA0160 MA0160.1.NR4A2 jaspar_vertebrates 1 NR4A2 . -MA0161 MA0161.1.NFIC jaspar_vertebrates 1 NFIC . -MA0162 MA0162.2.EGR1 jaspar_vertebrates 1 EGR1 . -MA0163 MA0163.1.PLAG1 jaspar_vertebrates 1 PLAG1 . -MA0164 MA0164.1.Nr2e3 jaspar_vertebrates 1 Nr2e3 . -MA0258 MA0258.2.ESR2 jaspar_vertebrates 1 ESR2 . -MA0259 MA0259.1.ARNT::HIF1A jaspar_vertebrates 1 ARNT+HIF1A . -MA0442 MA0442.1.SOX10 jaspar_vertebrates 1 SOX10 . -MA0461 MA0461.2.Atoh1 jaspar_vertebrates 1 Atoh1 . -MA0462 MA0462.1.BATF::JUN jaspar_vertebrates 1 BATF+JUN . -MA0463 MA0463.1.Bcl6 jaspar_vertebrates 1 Bcl6 . -MA0464 MA0464.2.BHLHE40 jaspar_vertebrates 1 BHLHE40 . -MA0465 MA0465.1.CDX2 jaspar_vertebrates 1 CDX2 . -MA0466 MA0466.2.CEBPB jaspar_vertebrates 1 CEBPB . -MA0467 MA0467.1.Crx jaspar_vertebrates 1 Crx . -MA0468 MA0468.1.DUX4 jaspar_vertebrates 1 DUX4 . -MA0469 MA0469.2.E2F3 jaspar_vertebrates 1 E2F3 . -MA0470 MA0470.1.E2F4 jaspar_vertebrates 1 E2F4 . -MA0471 MA0471.1.E2F6 jaspar_vertebrates 1 E2F6 . -MA0472 MA0472.2.EGR2 jaspar_vertebrates 1 EGR2 . -MA0473 MA0473.2.ELF1 jaspar_vertebrates 1 ELF1 . -MA0474 MA0474.2.ERG jaspar_vertebrates 1 ERG . -MA0475 MA0475.2.FLI1 jaspar_vertebrates 1 FLI1 . -MA0476 MA0476.1.FOS jaspar_vertebrates 1 FOS . -MA0477 MA0477.1.FOSL1 jaspar_vertebrates 1 FOSL1 . -MA0478 MA0478.1.FOSL2 jaspar_vertebrates 1 FOSL2 . -MA0479 MA0479.1.FOXH1 jaspar_vertebrates 1 FOXH1 . -MA0480 MA0480.1.Foxo1 jaspar_vertebrates 1 Foxo1 . -MA0481 MA0481.1.FOXP1 jaspar_vertebrates 1 FOXP1 . -MA0482 MA0482.1.Gata4 jaspar_vertebrates 1 Gata4 . -MA0483 MA0483.1.Gfi1b jaspar_vertebrates 1 Gfi1b . -MA0484 MA0484.1.HNF4G jaspar_vertebrates 1 HNF4G . -MA0485 MA0485.1.Hoxc9 jaspar_vertebrates 1 Hoxc9 . -MA0486 MA0486.2.HSF1 jaspar_vertebrates 1 HSF1 . -MA0488 MA0488.1.JUN jaspar_vertebrates 1 JUN . -MA0489 MA0489.1.JUN.var.2 jaspar_vertebrates pwm JUN.var . -MA0490 MA0490.1.JUNB jaspar_vertebrates 1 JUNB . -MA0491 MA0491.1.JUND jaspar_vertebrates 1 JUND . -MA0492 MA0492.1.JUND.var.2 jaspar_vertebrates pwm JUND.var . -MA0493 MA0493.1.Klf1 jaspar_vertebrates 1 Klf1 . -MA0494 MA0494.1.Nr1h3::Rxra jaspar_vertebrates 1 Nr1h3+Rxra . -MA0495 MA0495.1.MAFF jaspar_vertebrates 1 MAFF . -MA0496 MA0496.1.MAFK jaspar_vertebrates 1 MAFK . -MA0497 MA0497.1.MEF2C jaspar_vertebrates 1 MEF2C . -MA0498 MA0498.2.MEIS1 jaspar_vertebrates 1 MEIS1 . -MA0499 MA0499.1.Myod1 jaspar_vertebrates 1 Myod1 . -MA0500 MA0500.1.Myog jaspar_vertebrates 1 Myog . -MA0501 MA0501.1.MAF::NFE2 jaspar_vertebrates 1 MAF+NFE2 . -MA0502 MA0502.1.NFYB jaspar_vertebrates 1 NFYB . -MA0503 MA0503.1.Nkx2-5.var.2 jaspar_vertebrates pwm Nkx2-5.var . -MA0504 MA0504.1.NR2C2 jaspar_vertebrates 1 NR2C2 . -MA0505 MA0505.1.Nr5a2 jaspar_vertebrates 1 Nr5a2 . -MA0506 MA0506.1.NRF1 jaspar_vertebrates 1 NRF1 . -MA0507 MA0507.1.POU2F2 jaspar_vertebrates 1 POU2F2 . -MA0508 MA0508.1.PRDM1 jaspar_vertebrates 1 PRDM1 . -MA0509 MA0509.1.Rfx1 jaspar_vertebrates 1 Rfx1 . -MA0510 MA0510.2.RFX5 jaspar_vertebrates 1 RFX5 . -MA0511 MA0511.2.RUNX2 jaspar_vertebrates 1 RUNX2 . -MA0512 MA0512.2.Rxra jaspar_vertebrates 1 Rxra . -MA0513 MA0513.1.SMAD2::SMAD3::SMAD4 jaspar_vertebrates 1 SMAD2+SMAD3+SMAD4 . -MA0514 MA0514.1.Sox3 jaspar_vertebrates 1 Sox3 . -MA0515 MA0515.1.Sox6 jaspar_vertebrates 1 Sox6 . -MA0516 MA0516.1.SP2 jaspar_vertebrates 1 SP2 . -MA0517 MA0517.1.STAT1::STAT2 jaspar_vertebrates 1 STAT1+STAT2 . -MA0518 MA0518.1.Stat4 jaspar_vertebrates 1 Stat4 . -MA0519 MA0519.1.Stat5a::Stat5b jaspar_vertebrates 1 Stat5a+Stat5b . -MA0520 MA0520.1.Stat6 jaspar_vertebrates 1 Stat6 . -MA0521 MA0521.1.Tcf12 jaspar_vertebrates 1 Tcf12 . -MA0522 MA0522.2.TCF3 jaspar_vertebrates 1 TCF3 . -MA0523 MA0523.1.TCF7L2 jaspar_vertebrates 1 TCF7L2 . -MA0524 MA0524.2.TFAP2C jaspar_vertebrates 1 TFAP2C . -MA0525 MA0525.2.TP63 jaspar_vertebrates 1 TP63 . -MA0526 MA0526.1.USF2 jaspar_vertebrates 1 USF2 . -MA0527 MA0527.1.ZBTB33 jaspar_vertebrates 1 ZBTB33 . -MA0528 MA0528.1.ZNF263 jaspar_vertebrates 1 ZNF263 . -MA0591 MA0591.1.Bach1::Mafk jaspar_vertebrates 1 Bach1+Mafk . -MA0592 MA0592.2.Esrra jaspar_vertebrates 1 Esrra . -MA0593 MA0593.1.FOXP2 jaspar_vertebrates 1 FOXP2 . -MA0594 MA0594.1.Hoxa9 jaspar_vertebrates 1 Hoxa9 . -MA0595 MA0595.1.SREBF1 jaspar_vertebrates 1 SREBF1 . -MA0596 MA0596.1.SREBF2 jaspar_vertebrates 1 SREBF2 . -MA0597 MA0597.1.THAP1 jaspar_vertebrates 1 THAP1 . -MA0598 MA0598.2.EHF jaspar_vertebrates 1 EHF . -MA0599 MA0599.1.KLF5 jaspar_vertebrates 1 KLF5 . -MA0600 MA0600.2.RFX2 jaspar_vertebrates 1 RFX2 . -MA0601 MA0601.1.Arid3b jaspar_vertebrates 1 Arid3b . -MA0602 MA0602.1.Arid5a jaspar_vertebrates 1 Arid5a . -MA0603 MA0603.1.Arntl jaspar_vertebrates 1 Arntl . -MA0604 MA0604.1.Atf1 jaspar_vertebrates 1 Atf1 . -MA0605 MA0605.1.Atf3 jaspar_vertebrates 1 Atf3 . -MA0606 MA0606.1.NFAT5 jaspar_vertebrates 1 NFAT5 . -MA0607 MA0607.1.Bhlha15 jaspar_vertebrates 1 Bhlha15 . -MA0608 MA0608.1.Creb3l2 jaspar_vertebrates 1 Creb3l2 . -MA0609 MA0609.1.Crem jaspar_vertebrates 1 Crem . -MA0610 MA0610.1.DMRT3 jaspar_vertebrates 1 DMRT3 . -MA0611 MA0611.1.Dux jaspar_vertebrates 1 Dux . -MA0612 MA0612.1.EMX1 jaspar_vertebrates 1 EMX1 . -MA0613 MA0613.1.FOXG1 jaspar_vertebrates 1 FOXG1 . -MA0614 MA0614.1.Foxj2 jaspar_vertebrates 1 Foxj2 . -MA0615 MA0615.1.Gmeb1 jaspar_vertebrates 1 Gmeb1 . -MA0616 MA0616.1.Hes2 jaspar_vertebrates 1 Hes2 . -MA0617 MA0617.1.Id2 jaspar_vertebrates 1 Id2 . -MA0618 MA0618.1.LBX1 jaspar_vertebrates 1 LBX1 . -MA0619 MA0619.1.LIN54 jaspar_vertebrates 1 LIN54 . -MA0620 MA0620.1.Mitf jaspar_vertebrates 1 Mitf . -MA0621 MA0621.1.mix-a jaspar_vertebrates 1 mix-a . -MA0622 MA0622.1.Mlxip jaspar_vertebrates 1 Mlxip . -MA0623 MA0623.1.Neurog1 jaspar_vertebrates 1 Neurog1 . -MA0624 MA0624.1.NFATC1 jaspar_vertebrates 1 NFATC1 . -MA0625 MA0625.1.NFATC3 jaspar_vertebrates 1 NFATC3 . -MA0626 MA0626.1.Npas2 jaspar_vertebrates 1 Npas2 . -MA0627 MA0627.1.Pou2f3 jaspar_vertebrates 1 Pou2f3 . -MA0628 MA0628.1.POU6F1 jaspar_vertebrates 1 POU6F1 . -MA0629 MA0629.1.Rhox11 jaspar_vertebrates 1 Rhox11 . -MA0630 MA0630.1.SHOX jaspar_vertebrates 1 SHOX . -MA0631 MA0631.1.Six3 jaspar_vertebrates 1 Six3 . -MA0632 MA0632.1.Tcfl5 jaspar_vertebrates 1 Tcfl5 . -MA0633 MA0633.1.Twist2 jaspar_vertebrates 1 Twist2 . -MA0634 MA0634.1.ALX3 jaspar_vertebrates 1 ALX3 . -MA0635 MA0635.1.BARHL2 jaspar_vertebrates 1 BARHL2 . -MA0636 MA0636.1.BHLHE41 jaspar_vertebrates 1 BHLHE41 . -MA0637 MA0637.1.CENPB jaspar_vertebrates 1 CENPB . -MA0638 MA0638.1.CREB3 jaspar_vertebrates 1 CREB3 . -MA0639 MA0639.1.DBP jaspar_vertebrates 1 DBP . -MA0640 MA0640.1.ELF3 jaspar_vertebrates 1 ELF3 . -MA0641 MA0641.1.ELF4 jaspar_vertebrates 1 ELF4 . -MA0642 MA0642.1.EN2 jaspar_vertebrates 1 EN2 . -MA0643 MA0643.1.Esrrg jaspar_vertebrates 1 Esrrg . -MA0644 MA0644.1.ESX1 jaspar_vertebrates 1 ESX1 . -MA0645 MA0645.1.ETV6 jaspar_vertebrates 1 ETV6 . -MA0646 MA0646.1.GCM1 jaspar_vertebrates 1 GCM1 . -MA0647 MA0647.1.GRHL1 jaspar_vertebrates 1 GRHL1 . -MA0648 MA0648.1.GSC jaspar_vertebrates 1 GSC . -MA0649 MA0649.1.HEY2 jaspar_vertebrates 1 HEY2 . -MA0650 MA0650.1.HOXA13 jaspar_vertebrates 1 HOXA13 . -MA0651 MA0651.1.HOXC11 jaspar_vertebrates 1 HOXC11 . -MA0652 MA0652.1.IRF8 jaspar_vertebrates 1 IRF8 . -MA0653 MA0653.1.IRF9 jaspar_vertebrates 1 IRF9 . -MA0654 MA0654.1.ISX jaspar_vertebrates 1 ISX . -MA0655 MA0655.1.JDP2 jaspar_vertebrates 1 JDP2 . -MA0656 MA0656.1.JDP2.var.2 jaspar_vertebrates pwm JDP2.var . -MA0657 MA0657.1.KLF13 jaspar_vertebrates 1 KLF13 . -MA0658 MA0658.1.LHX6 jaspar_vertebrates 1 LHX6 . -MA0659 MA0659.1.MAFG jaspar_vertebrates 1 MAFG . -MA0660 MA0660.1.MEF2B jaspar_vertebrates 1 MEF2B . -MA0661 MA0661.1.MEOX1 jaspar_vertebrates 1 MEOX1 . -MA0662 MA0662.1.MIXL1 jaspar_vertebrates 1 MIXL1 . -MA0663 MA0663.1.MLX jaspar_vertebrates 1 MLX . -MA0664 MA0664.1.MLXIPL jaspar_vertebrates 1 MLXIPL . -MA0665 MA0665.1.MSC jaspar_vertebrates 1 MSC . -MA0666 MA0666.1.MSX1 jaspar_vertebrates 1 MSX1 . -MA0667 MA0667.1.MYF6 jaspar_vertebrates 1 MYF6 . -MA0668 MA0668.1.NEUROD2 jaspar_vertebrates 1 NEUROD2 . -MA0669 MA0669.1.NEUROG2 jaspar_vertebrates 1 NEUROG2 . -MA0670 MA0670.1.NFIA jaspar_vertebrates 1 NFIA . -MA0671 MA0671.1.NFIX jaspar_vertebrates 1 NFIX . -MA0672 MA0672.1.NKX2-3 jaspar_vertebrates 1 NKX2-3 . -MA0673 MA0673.1.NKX2-8 jaspar_vertebrates 1 NKX2-8 . -MA0674 MA0674.1.NKX6-1 jaspar_vertebrates 1 NKX6-1 . -MA0675 MA0675.1.NKX6-2 jaspar_vertebrates 1 NKX6-2 . -MA0676 MA0676.1.Nr2e1 jaspar_vertebrates 1 Nr2e1 . -MA0677 MA0677.1.Nr2f6 jaspar_vertebrates 1 Nr2f6 . -MA0678 MA0678.1.OLIG2 jaspar_vertebrates 1 OLIG2 . -MA0679 MA0679.1.ONECUT1 jaspar_vertebrates 1 ONECUT1 . -MA0680 MA0680.1.PAX7 jaspar_vertebrates 1 PAX7 . -MA0681 MA0681.1.Phox2b jaspar_vertebrates 1 Phox2b . -MA0682 MA0682.1.Pitx1 jaspar_vertebrates 1 Pitx1 . -MA0683 MA0683.1.POU4F2 jaspar_vertebrates 1 POU4F2 . -MA0684 MA0684.1.RUNX3 jaspar_vertebrates 1 RUNX3 . -MA0685 MA0685.1.SP4 jaspar_vertebrates 1 SP4 . -MA0686 MA0686.1.SPDEF jaspar_vertebrates 1 SPDEF . -MA0687 MA0687.1.SPIC jaspar_vertebrates 1 SPIC . -MA0688 MA0688.1.TBX2 jaspar_vertebrates 1 TBX2 . -MA0689 MA0689.1.TBX20 jaspar_vertebrates 1 TBX20 . -MA0690 MA0690.1.TBX21 jaspar_vertebrates 1 TBX21 . -MA0691 MA0691.1.TFAP4 jaspar_vertebrates 1 TFAP4 . -MA0692 MA0692.1.TFEB jaspar_vertebrates 1 TFEB . -MA0693 MA0693.1.Vdr jaspar_vertebrates 1 Vdr . -MA0694 MA0694.1.ZBTB7B jaspar_vertebrates 1 ZBTB7B . -MA0695 MA0695.1.ZBTB7C jaspar_vertebrates 1 ZBTB7C . -MA0696 MA0696.1.ZIC1 jaspar_vertebrates 1 ZIC1 . -MA0697 MA0697.1.ZIC3 jaspar_vertebrates 1 ZIC3 . -MA0698 MA0698.1.ZBTB18 jaspar_vertebrates 1 ZBTB18 . -MA0699 MA0699.1.LBX2 jaspar_vertebrates 1 LBX2 . -MA0700 MA0700.1.LHX2 jaspar_vertebrates 1 LHX2 . -MA0701 MA0701.1.LHX9 jaspar_vertebrates 1 LHX9 . -MA0702 MA0702.1.LMX1A jaspar_vertebrates 1 LMX1A . -MA0703 MA0703.1.LMX1B jaspar_vertebrates 1 LMX1B . -MA0704 MA0704.1.Lhx4 jaspar_vertebrates 1 Lhx4 . -MA0705 MA0705.1.Lhx8 jaspar_vertebrates 1 Lhx8 . -MA0706 MA0706.1.MEOX2 jaspar_vertebrates 1 MEOX2 . -MA0707 MA0707.1.MNX1 jaspar_vertebrates 1 MNX1 . -MA0708 MA0708.1.MSX2 jaspar_vertebrates 1 MSX2 . -MA0709 MA0709.1.Msx3 jaspar_vertebrates 1 Msx3 . -MA0710 MA0710.1.NOTO jaspar_vertebrates 1 NOTO . -MA0711 MA0711.1.OTX1 jaspar_vertebrates 1 OTX1 . -MA0712 MA0712.1.OTX2 jaspar_vertebrates 1 OTX2 . -MA0713 MA0713.1.PHOX2A jaspar_vertebrates 1 PHOX2A . -MA0714 MA0714.1.PITX3 jaspar_vertebrates 1 PITX3 . -MA0715 MA0715.1.PROP1 jaspar_vertebrates 1 PROP1 . -MA0716 MA0716.1.PRRX1 jaspar_vertebrates 1 PRRX1 . -MA0717 MA0717.1.RAX2 jaspar_vertebrates 1 RAX2 . -MA0718 MA0718.1.RAX jaspar_vertebrates 1 RAX . -MA0719 MA0719.1.RHOXF1 jaspar_vertebrates 1 RHOXF1 . -MA0720 MA0720.1.Shox2 jaspar_vertebrates 1 Shox2 . -MA0721 MA0721.1.UNCX jaspar_vertebrates 1 UNCX . -MA0722 MA0722.1.VAX1 jaspar_vertebrates 1 VAX1 . -MA0723 MA0723.1.VAX2 jaspar_vertebrates 1 VAX2 . -MA0724 MA0724.1.VENTX jaspar_vertebrates 1 VENTX . -MA0725 MA0725.1.VSX1 jaspar_vertebrates 1 VSX1 . -MA0726 MA0726.1.VSX2 jaspar_vertebrates 1 VSX2 . -MA0727 MA0727.1.NR3C2 jaspar_vertebrates 1 NR3C2 . -MA0728 MA0728.1.Nr2f6.var.2 jaspar_vertebrates pwm Nr2f6.var . -MA0729 MA0729.1.RARA jaspar_vertebrates 1 RARA . -MA0730 MA0730.1.RARA.var.2 jaspar_vertebrates pwm RARA.var . -MA0731 MA0731.1.BCL6B jaspar_vertebrates 1 BCL6B . -MA0732 MA0732.1.EGR3 jaspar_vertebrates 1 EGR3 . -MA0733 MA0733.1.EGR4 jaspar_vertebrates 1 EGR4 . -MA0734 MA0734.1.GLI2 jaspar_vertebrates 1 GLI2 . -MA0735 MA0735.1.GLIS1 jaspar_vertebrates 1 GLIS1 . -MA0736 MA0736.1.GLIS2 jaspar_vertebrates 1 GLIS2 . -MA0737 MA0737.1.GLIS3 jaspar_vertebrates 1 GLIS3 . -MA0738 MA0738.1.HIC2 jaspar_vertebrates 1 HIC2 . -MA0739 MA0739.1.Hic1 jaspar_vertebrates 1 Hic1 . -MA0740 MA0740.1.KLF14 jaspar_vertebrates 1 KLF14 . -MA0741 MA0741.1.KLF16 jaspar_vertebrates 1 KLF16 . -MA0742 MA0742.1.Klf12 jaspar_vertebrates 1 Klf12 . -MA0743 MA0743.1.SCRT1 jaspar_vertebrates 1 SCRT1 . -MA0744 MA0744.1.SCRT2 jaspar_vertebrates 1 SCRT2 . -MA0745 MA0745.1.SNAI2 jaspar_vertebrates 1 SNAI2 . -MA0746 MA0746.1.SP3 jaspar_vertebrates 1 SP3 . -MA0747 MA0747.1.SP8 jaspar_vertebrates 1 SP8 . -MA0748 MA0748.1.YY2 jaspar_vertebrates 1 YY2 . -MA0749 MA0749.1.ZBED1 jaspar_vertebrates 1 ZBED1 . -MA0750 MA0750.1.ZBTB7A jaspar_vertebrates 1 ZBTB7A . -MA0751 MA0751.1.ZIC4 jaspar_vertebrates 1 ZIC4 . -MA0752 MA0752.1.ZNF410 jaspar_vertebrates 1 ZNF410 . -MA0753 MA0753.1.ZNF740 jaspar_vertebrates 1 ZNF740 . -MA0754 MA0754.1.CUX1 jaspar_vertebrates 1 CUX1 . -MA0755 MA0755.1.CUX2 jaspar_vertebrates 1 CUX2 . -MA0756 MA0756.1.ONECUT2 jaspar_vertebrates 1 ONECUT2 . -MA0757 MA0757.1.ONECUT3 jaspar_vertebrates 1 ONECUT3 . -MA0758 MA0758.1.E2F7 jaspar_vertebrates 1 E2F7 . -MA0759 MA0759.1.ELK3 jaspar_vertebrates 1 ELK3 . -MA0760 MA0760.1.ERF jaspar_vertebrates 1 ERF . -MA0761 MA0761.1.ETV1 jaspar_vertebrates 1 ETV1 . -MA0762 MA0762.1.ETV2 jaspar_vertebrates 1 ETV2 . -MA0763 MA0763.1.ETV3 jaspar_vertebrates 1 ETV3 . -MA0764 MA0764.1.ETV4 jaspar_vertebrates 1 ETV4 . -MA0765 MA0765.1.ETV5 jaspar_vertebrates 1 ETV5 . -MA0766 MA0766.1.GATA5 jaspar_vertebrates 1 GATA5 . -MA0767 MA0767.1.GCM2 jaspar_vertebrates 1 GCM2 . -MA0768 MA0768.1.LEF1 jaspar_vertebrates 1 LEF1 . -MA0769 MA0769.1.Tcf7 jaspar_vertebrates 1 Tcf7 . -MA0770 MA0770.1.HSF2 jaspar_vertebrates 1 HSF2 . -MA0771 MA0771.1.HSF4 jaspar_vertebrates 1 HSF4 . -MA0772 MA0772.1.IRF7 jaspar_vertebrates 1 IRF7 . -MA0773 MA0773.1.MEF2D jaspar_vertebrates 1 MEF2D . -MA0774 MA0774.1.MEIS2 jaspar_vertebrates 1 MEIS2 . -MA0775 MA0775.1.MEIS3 jaspar_vertebrates 1 MEIS3 . -MA0776 MA0776.1.MYBL1 jaspar_vertebrates 1 MYBL1 . -MA0777 MA0777.1.MYBL2 jaspar_vertebrates 1 MYBL2 . -MA0778 MA0778.1.NFKB2 jaspar_vertebrates 1 NFKB2 . -MA0779 MA0779.1.PAX1 jaspar_vertebrates 1 PAX1 . -MA0780 MA0780.1.PAX3 jaspar_vertebrates 1 PAX3 . -MA0781 MA0781.1.PAX9 jaspar_vertebrates 1 PAX9 . -MA0782 MA0782.1.PKNOX1 jaspar_vertebrates 1 PKNOX1 . -MA0783 MA0783.1.PKNOX2 jaspar_vertebrates 1 PKNOX2 . -MA0784 MA0784.1.POU1F1 jaspar_vertebrates 1 POU1F1 . -MA0785 MA0785.1.POU2F1 jaspar_vertebrates 1 POU2F1 . -MA0786 MA0786.1.POU3F1 jaspar_vertebrates 1 POU3F1 . -MA0787 MA0787.1.POU3F2 jaspar_vertebrates 1 POU3F2 . -MA0788 MA0788.1.POU3F3 jaspar_vertebrates 1 POU3F3 . -MA0789 MA0789.1.POU3F4 jaspar_vertebrates 1 POU3F4 . -MA0790 MA0790.1.POU4F1 jaspar_vertebrates 1 POU4F1 . -MA0791 MA0791.1.POU4F3 jaspar_vertebrates 1 POU4F3 . -MA0792 MA0792.1.POU5F1B jaspar_vertebrates 1 POU5F1B . -MA0793 MA0793.1.POU6F2 jaspar_vertebrates 1 POU6F2 . -MA0794 MA0794.1.PROX1 jaspar_vertebrates 1 PROX1 . -MA0795 MA0795.1.SMAD3 jaspar_vertebrates 1 SMAD3 . -MA0796 MA0796.1.TGIF1 jaspar_vertebrates 1 TGIF1 . -MA0797 MA0797.1.TGIF2 jaspar_vertebrates 1 TGIF2 . -MA0798 MA0798.1.RFX3 jaspar_vertebrates 1 RFX3 . -MA0799 MA0799.1.RFX4 jaspar_vertebrates 1 RFX4 . -MA0800 MA0800.1.EOMES jaspar_vertebrates 1 EOMES . -MA0801 MA0801.1.MGA jaspar_vertebrates 1 MGA . -MA0802 MA0802.1.TBR1 jaspar_vertebrates 1 TBR1 . -MA0803 MA0803.1.TBX15 jaspar_vertebrates 1 TBX15 . -MA0804 MA0804.1.TBX19 jaspar_vertebrates 1 TBX19 . -MA0805 MA0805.1.TBX1 jaspar_vertebrates 1 TBX1 . -MA0806 MA0806.1.TBX4 jaspar_vertebrates 1 TBX4 . -MA0807 MA0807.1.TBX5 jaspar_vertebrates 1 TBX5 . -MA0808 MA0808.1.TEAD3 jaspar_vertebrates 1 TEAD3 . -MA0809 MA0809.1.TEAD4 jaspar_vertebrates 1 TEAD4 . -MA0810 MA0810.1.TFAP2A.var.2 jaspar_vertebrates pwm TFAP2A.var . -MA0811 MA0811.1.TFAP2B jaspar_vertebrates 1 TFAP2B . -MA0812 MA0812.1.TFAP2B.var.2 jaspar_vertebrates pwm TFAP2B.var . -MA0813 MA0813.1.TFAP2B.var.3 jaspar_vertebrates pwm TFAP2B.var . -MA0814 MA0814.1.TFAP2C.var.2 jaspar_vertebrates pwm TFAP2C.var . -MA0815 MA0815.1.TFAP2C.var.3 jaspar_vertebrates pwm TFAP2C.var . -MA0816 MA0816.1.Ascl2 jaspar_vertebrates 1 Ascl2 . -MA0817 MA0817.1.BHLHE23 jaspar_vertebrates 1 BHLHE23 . -MA0818 MA0818.1.BHLHE22 jaspar_vertebrates 1 BHLHE22 . -MA0819 MA0819.1.CLOCK jaspar_vertebrates 1 CLOCK . -MA0820 MA0820.1.FIGLA jaspar_vertebrates 1 FIGLA . -MA0821 MA0821.1.HES5 jaspar_vertebrates 1 HES5 . -MA0822 MA0822.1.HES7 jaspar_vertebrates 1 HES7 . -MA0823 MA0823.1.HEY1 jaspar_vertebrates 1 HEY1 . -MA0824 MA0824.1.ID4 jaspar_vertebrates 1 ID4 . -MA0825 MA0825.1.MNT jaspar_vertebrates 1 MNT . -MA0826 MA0826.1.OLIG1 jaspar_vertebrates 1 OLIG1 . -MA0827 MA0827.1.OLIG3 jaspar_vertebrates 1 OLIG3 . -MA0828 MA0828.1.SREBF2.var.2 jaspar_vertebrates pwm SREBF2.var . -MA0829 MA0829.1.Srebf1.var.2 jaspar_vertebrates pwm Srebf1.var . -MA0830 MA0830.1.TCF4 jaspar_vertebrates 1 TCF4 . -MA0831 MA0831.1.TFE3 jaspar_vertebrates 1 TFE3 . -MA0832 MA0832.1.Tcf21 jaspar_vertebrates 1 Tcf21 . -MA0833 MA0833.1.ATF4 jaspar_vertebrates 1 ATF4 . -MA0834 MA0834.1.ATF7 jaspar_vertebrates 1 ATF7 . -MA0835 MA0835.1.BATF3 jaspar_vertebrates 1 BATF3 . -MA0836 MA0836.1.CEBPD jaspar_vertebrates 1 CEBPD . -MA0837 MA0837.1.CEBPE jaspar_vertebrates 1 CEBPE . -MA0838 MA0838.1.CEBPG jaspar_vertebrates 1 CEBPG . -MA0839 MA0839.1.CREB3L1 jaspar_vertebrates 1 CREB3L1 . -MA0840 MA0840.1.Creb5 jaspar_vertebrates 1 Creb5 . -MA0841 MA0841.1.NFE2 jaspar_vertebrates 1 NFE2 . -MA0842 MA0842.1.NRL jaspar_vertebrates 1 NRL . -MA0843 MA0843.1.TEF jaspar_vertebrates 1 TEF . -MA0844 MA0844.1.XBP1 jaspar_vertebrates 1 XBP1 . -MA0845 MA0845.1.FOXB1 jaspar_vertebrates 1 FOXB1 . -MA0846 MA0846.1.FOXC2 jaspar_vertebrates 1 FOXC2 . -MA0847 MA0847.1.FOXD2 jaspar_vertebrates 1 FOXD2 . -MA0848 MA0848.1.FOXO4 jaspar_vertebrates 1 FOXO4 . -MA0849 MA0849.1.FOXO6 jaspar_vertebrates 1 FOXO6 . -MA0850 MA0850.1.FOXP3 jaspar_vertebrates 1 FOXP3 . -MA0851 MA0851.1.Foxj3 jaspar_vertebrates 1 Foxj3 . -MA0852 MA0852.1.Foxk1 jaspar_vertebrates 1 Foxk1 . -MA0853 MA0853.1.Alx4 jaspar_vertebrates 1 Alx4 . -MA0854 MA0854.1.Alx1 jaspar_vertebrates 1 Alx1 . -MA0855 MA0855.1.RXRB jaspar_vertebrates 1 RXRB . -MA0856 MA0856.1.RXRG jaspar_vertebrates 1 RXRG . -MA0857 MA0857.1.Rarb jaspar_vertebrates 1 Rarb . -MA0858 MA0858.1.Rarb.var.2 jaspar_vertebrates pwm Rarb.var . -MA0859 MA0859.1.Rarg jaspar_vertebrates 1 Rarg . -MA0860 MA0860.1.Rarg.var.2 jaspar_vertebrates pwm Rarg.var . -MA0861 MA0861.1.TP73 jaspar_vertebrates 1 TP73 . -MA0862 MA0862.1.GMEB2 jaspar_vertebrates 1 GMEB2 . -MA0863 MA0863.1.MTF1 jaspar_vertebrates 1 MTF1 . -MA0864 MA0864.1.E2F2 jaspar_vertebrates 1 E2F2 . -MA0865 MA0865.1.E2F8 jaspar_vertebrates 1 E2F8 . -MA0866 MA0866.1.SOX21 jaspar_vertebrates 1 SOX21 . -MA0867 MA0867.1.SOX4 jaspar_vertebrates 1 SOX4 . -MA0868 MA0868.1.SOX8 jaspar_vertebrates 1 SOX8 . -MA0869 MA0869.1.Sox11 jaspar_vertebrates 1 Sox11 . -MA0870 MA0870.1.Sox1 jaspar_vertebrates 1 Sox1 . -MA0871 MA0871.1.TFEC jaspar_vertebrates 1 TFEC . -MA0872 MA0872.1.TFAP2A.var.3 jaspar_vertebrates pwm TFAP2A.var . -MA0873 MA0873.1.HOXD12 jaspar_vertebrates 1 HOXD12 . -MA0874 MA0874.1.Arx jaspar_vertebrates 1 Arx . -MA0875 MA0875.1.BARX1 jaspar_vertebrates 1 BARX1 . -MA0876 MA0876.1.BSX jaspar_vertebrates 1 BSX . -MA0877 MA0877.1.Barhl1 jaspar_vertebrates 1 Barhl1 . -MA0878 MA0878.1.CDX1 jaspar_vertebrates 1 CDX1 . -MA0879 MA0879.1.Dlx1 jaspar_vertebrates 1 Dlx1 . -MA0880 MA0880.1.Dlx3 jaspar_vertebrates 1 Dlx3 . -MA0881 MA0881.1.Dlx4 jaspar_vertebrates 1 Dlx4 . -MA0882 MA0882.1.DLX6 jaspar_vertebrates 1 DLX6 . -MA0883 MA0883.1.Dmbx1 jaspar_vertebrates 1 Dmbx1 . -MA0884 MA0884.1.DUXA jaspar_vertebrates 1 DUXA . -MA0885 MA0885.1.Dlx2 jaspar_vertebrates 1 Dlx2 . -MA0886 MA0886.1.EMX2 jaspar_vertebrates 1 EMX2 . -MA0887 MA0887.1.EVX1 jaspar_vertebrates 1 EVX1 . -MA0888 MA0888.1.EVX2 jaspar_vertebrates 1 EVX2 . -MA0889 MA0889.1.GBX1 jaspar_vertebrates 1 GBX1 . -MA0890 MA0890.1.GBX2 jaspar_vertebrates 1 GBX2 . -MA0891 MA0891.1.GSC2 jaspar_vertebrates 1 GSC2 . -MA0892 MA0892.1.GSX1 jaspar_vertebrates 1 GSX1 . -MA0893 MA0893.1.GSX2 jaspar_vertebrates 1 GSX2 . -MA0894 MA0894.1.HESX1 jaspar_vertebrates 1 HESX1 . -MA0895 MA0895.1.HMBOX1 jaspar_vertebrates 1 HMBOX1 . -MA0896 MA0896.1.Hmx1 jaspar_vertebrates 1 Hmx1 . -MA0897 MA0897.1.Hmx2 jaspar_vertebrates 1 Hmx2 . -MA0898 MA0898.1.Hmx3 jaspar_vertebrates 1 Hmx3 . -MA0899 MA0899.1.HOXA10 jaspar_vertebrates 1 HOXA10 . -MA0900 MA0900.1.HOXA2 jaspar_vertebrates 1 HOXA2 . -MA0901 MA0901.1.HOXB13 jaspar_vertebrates 1 HOXB13 . -MA0902 MA0902.1.HOXB2 jaspar_vertebrates 1 HOXB2 . -MA0903 MA0903.1.HOXB3 jaspar_vertebrates 1 HOXB3 . -MA0904 MA0904.1.Hoxb5 jaspar_vertebrates 1 Hoxb5 . -MA0905 MA0905.1.HOXC10 jaspar_vertebrates 1 HOXC10 . -MA0906 MA0906.1.HOXC12 jaspar_vertebrates 1 HOXC12 . -MA0907 MA0907.1.HOXC13 jaspar_vertebrates 1 HOXC13 . -MA0908 MA0908.1.HOXD11 jaspar_vertebrates 1 HOXD11 . -MA0909 MA0909.1.HOXD13 jaspar_vertebrates 1 HOXD13 . -MA0910 MA0910.1.Hoxd8 jaspar_vertebrates 1 Hoxd8 . -MA0911 MA0911.1.Hoxa11 jaspar_vertebrates 1 Hoxa11 . -MA0912 MA0912.1.Hoxd3 jaspar_vertebrates 1 Hoxd3 . -MA0913 MA0913.1.Hoxd9 jaspar_vertebrates 1 Hoxd9 . -MA0914 MA0914.1.ISL2 jaspar_vertebrates 1 ISL2 . -MA1099 MA1099.1.Hes1 jaspar_vertebrates 1 Hes1 . +MA0002 MA0002.2.RUNX1 jaspar_vertebrates 1 RUNX1 Runt-related factors Q01196 ChIP-seq +MA0003 MA0003.3.TFAP2A jaspar_vertebrates 1 TFAP2A AP-2 P05549 HT-SELEX +MA0004 MA0004.1.Arnt jaspar_vertebrates 1 Arnt PAS domain factors P53762 SELEX +MA0006 MA0006.1.Ahr::Arnt jaspar_vertebrates 1 Ahr+Arnt PAS domain factors P30561;P53762 SELEX +MA0007 MA0007.3.Ar jaspar_vertebrates 1 Ar Steroid hormone receptors (NR3) P19091 HT-SELEX +MA0009 MA0009.2.T jaspar_vertebrates 1 T Brachyury-related factors O15178 HT-SELEX +MA0014 MA0014.3.PAX5 jaspar_vertebrates 1 PAX5 Paired domain only Q02548 ChIP-seq +MA0017 MA0017.2.NR2F1 jaspar_vertebrates 1 NR2F1 RXR-related receptors (NR2) P10589 HT-SELEX +MA0018 MA0018.3.CREB1 jaspar_vertebrates 1 CREB1 CREB-related factors P16220 ChIP-seq +MA0019 MA0019.1.Ddit3::Cebpa jaspar_vertebrates 1 Ddit3+Cebpa C/EBP-related Q62857;P05554 SELEX +MA0024 MA0024.3.E2F1 jaspar_vertebrates 1 E2F1 E2F-related factors Q01094 HT-SELEX +MA0025 MA0025.1.NFIL3 jaspar_vertebrates 1 NFIL3 C/EBP-related Q16649 SELEX +MA0027 MA0027.2.EN1 jaspar_vertebrates 1 EN1 NK-related factors Q05925 HT-SELEX +MA0028 MA0028.2.ELK1 jaspar_vertebrates 1 ELK1 Ets-related factors P19419 HT-SELEX +MA0029 MA0029.1.Mecom jaspar_vertebrates 1 Mecom Factors with multiple dispersed zinc fingers A4QPC8 SELEX +MA0030 MA0030.1.FOXF2 jaspar_vertebrates 1 FOXF2 Forkhead box (FOX) factors Q12947 SELEX +MA0031 MA0031.1.FOXD1 jaspar_vertebrates 1 FOXD1 Forkhead box (FOX) factors Q16676 SELEX +MA0032 MA0032.2.FOXC1 jaspar_vertebrates 1 FOXC1 Forkhead box (FOX) factors Q12948 HT-SELEX +MA0033 MA0033.2.FOXL1 jaspar_vertebrates 1 FOXL1 Forkhead box (FOX) factors Q12952 HT-SELEX +MA0035 MA0035.3.Gata1 jaspar_vertebrates 1 Gata1 GATA-type zinc fingers P17679 ChIP-seq +MA0036 MA0036.3.GATA2 jaspar_vertebrates 1 GATA2 GATA-type zinc fingers P23769 ChIP-seq +MA0037 MA0037.3.GATA3 jaspar_vertebrates 1 GATA3 GATA-type zinc fingers P23771 SMiLE-seq +MA0038 MA0038.1.Gfi1 jaspar_vertebrates 1 Gfi1 More than 3 adjacent zinc finger factors Q07120 SELEX +MA0039 MA0039.3.KLF4 jaspar_vertebrates 1 KLF4 Three-zinc finger Kruppel-related factors O43474 ChIP-seq +MA0040 MA0040.1.Foxq1 jaspar_vertebrates 1 Foxq1 Forkhead box (FOX) factors Q63244 SELEX +MA0041 MA0041.1.Foxd3 jaspar_vertebrates 1 Foxd3 Forkhead box (FOX) factors Q63245 SELEX +MA0042 MA0042.2.FOXI1 jaspar_vertebrates 1 FOXI1 Forkhead box (FOX) factors Q12951 HT-SELEX +MA0043 MA0043.2.HLF jaspar_vertebrates 1 HLF C/EBP-related Q16534 HT-SELEX +MA0046 MA0046.2.HNF1A jaspar_vertebrates 1 HNF1A POU domain factors P20823 HT-SELEX +MA0047 MA0047.2.Foxa2 jaspar_vertebrates 1 Foxa2 Forkhead box (FOX) factors P35583 ChIP-seq +MA0048 MA0048.2.NHLH1 jaspar_vertebrates 1 NHLH1 Tal-related factors Q02575 HT-SELEX +MA0050 MA0050.2.IRF1 jaspar_vertebrates 1 IRF1 Interferon-regulatory factors P10914 ChIP-seq +MA0051 MA0051.1.IRF2 jaspar_vertebrates 1 IRF2 Interferon-regulatory factors P14316 SELEX +MA0052 MA0052.3.MEF2A jaspar_vertebrates 1 MEF2A Regulators of differentiation Q02078 HT-SELEX +MA0056 MA0056.1.MZF1 jaspar_vertebrates 1 MZF1 More than 3 adjacent zinc finger factors P28698 SELEX +MA0057 MA0057.1.MZF1(var.2) jaspar_vertebrates pwm MZF1(var.2) More than 3 adjacent zinc finger factors P28698 SELEX +MA0058 MA0058.3.MAX jaspar_vertebrates 1 MAX bHLH-ZIP factors P61244 HT-SELEX +MA0059 MA0059.1.MAX::MYC jaspar_vertebrates 1 MAX+MYC bHLH-ZIP factors P61244;P01106 SELEX +MA0060 MA0060.3.NFYA jaspar_vertebrates 1 NFYA NFY P23511 ChIP-seq +MA0062 MA0062.2.Gabpa jaspar_vertebrates 1 Gabpa Ets-related factors Q91YY8 ChIP-seq +MA0063 MA0063.1.Nkx2-5 jaspar_vertebrates 1 Nkx2-5 NK-related factors P42582 SELEX +MA0065 MA0065.2.Pparg::Rxra jaspar_vertebrates 1 Pparg+Rxra RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1) P37238;P28700 ChIP-seq +MA0066 MA0066.1.PPARG jaspar_vertebrates 1 PPARG Thyroid hormone receptor-related factors (NR1) P37231 SELEX +MA0067 MA0067.1.Pax2 jaspar_vertebrates 1 Pax2 Paired domain only P32114 SELEX +MA0068 MA0068.2.PAX4 jaspar_vertebrates 1 PAX4 Paired plus homeo domain O43316 HT-SELEX +MA0069 MA0069.1.Pax6 jaspar_vertebrates 1 Pax6 Paired plus homeo domain P26367 SELEX +MA0070 MA0070.1.PBX1 jaspar_vertebrates 1 PBX1 TALE-type homeo domain factors P40424 SELEX +MA0071 MA0071.1.RORA jaspar_vertebrates 1 RORA Thyroid hormone receptor-related factors (NR1) P35398 SELEX +MA0072 MA0072.1.RORA(var.2) jaspar_vertebrates pwm RORA(var.2) Thyroid hormone receptor-related factors (NR1) P35398 SELEX +MA0073 MA0073.1.RREB1 jaspar_vertebrates 1 RREB1 Factors with multiple dispersed zinc fingers Q92766 SELEX +MA0074 MA0074.1.RXRA::VDR jaspar_vertebrates 1 RXRA+VDR RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1) P19793;P11473 SELEX +MA0075 MA0075.2.Prrx2 jaspar_vertebrates 1 Prrx2 Paired-related HD factors Q06348 HT-SELEX +MA0076 MA0076.2.ELK4 jaspar_vertebrates 1 ELK4 Ets-related factors P28324 ChIP-seq +MA0077 MA0077.1.SOX9 jaspar_vertebrates 1 SOX9 SOX-related factors P48436 SELEX +MA0078 MA0078.1.Sox17 jaspar_vertebrates 1 Sox17 SOX-related factors Q61473 SELEX +MA0079 MA0079.3.SP1 jaspar_vertebrates 1 SP1 Three-zinc finger Krüppel-related factors P08047 ChIP-seq +MA0080 MA0080.4.SPI1 jaspar_vertebrates 1 SPI1 Ets-related factors P17947 HT-SELEX +MA0081 MA0081.1.SPIB jaspar_vertebrates 1 SPIB Ets-related factors Q01892 SELEX +MA0083 MA0083.3.SRF jaspar_vertebrates 1 SRF Responders to external signals (SRF/RLM1) P11831 HT-SELEX +MA0084 MA0084.1.SRY jaspar_vertebrates 1 SRY SOX-related factors Q05066 SELEX +MA0087 MA0087.1.Sox5 jaspar_vertebrates 1 Sox5 SOX-related factors P35710 SELEX +MA0088 MA0088.2.ZNF143 jaspar_vertebrates 1 ZNF143 More than 3 adjacent zinc finger factors P52747 HT-SELEX +MA0089 MA0089.1.MAFG::NFE2L1 jaspar_vertebrates 1 MAFG+NFE2L1 Jun-related factors;Maf-related factors Q90889;Q5ZL67 SELEX +MA0090 MA0090.2.TEAD1 jaspar_vertebrates 1 TEAD1 TEF-1-related factors P28347 HT-SELEX +MA0091 MA0091.1.TAL1::TCF3 jaspar_vertebrates 1 TAL1+TCF3 E2A-related factors;Tal-related factors P17542;P15923 SELEX +MA0092 MA0092.1.Hand1::Tcf3 jaspar_vertebrates 1 Hand1+Tcf3 E2A-related factors;Tal-related factors Q64279;P15806 SELEX +MA0093 MA0093.2.USF1 jaspar_vertebrates 1 USF1 bHLH-ZIP factors P22415 ChIP-seq +MA0095 MA0095.2.YY1 jaspar_vertebrates 1 YY1 More than 3 adjacent zinc finger factors P25490 ChIP-seq +MA0098 MA0098.3.ETS1 jaspar_vertebrates 1 ETS1 Ets-related factors P14921 HT-SELEX +MA0099 MA0099.3.FOS::JUN jaspar_vertebrates 1 FOS+JUN Fos-related factors;Jun-related factors P01100;P05412 SMiLE-seq +MA0100 MA0100.3.MYB jaspar_vertebrates 1 MYB Myb/SANT domain factors P10242 ChIP-seq +MA0101 MA0101.1.REL jaspar_vertebrates 1 REL NF-kappaB-related factors Q04864 SELEX +MA0102 MA0102.3.CEBPA jaspar_vertebrates 1 CEBPA C/EBP-related P49715 ChIP-seq +MA0103 MA0103.3.ZEB1 jaspar_vertebrates 1 ZEB1 HD-ZF factors P37275 ChIP-seq +MA0104 MA0104.4.MYCN jaspar_vertebrates 1 MYCN bHLH-ZIP factors P04198 ChIP-seq +MA0105 MA0105.4.NFKB1 jaspar_vertebrates 1 NFKB1 NF-kappaB-related factors P19838 HT-SELEX +MA0106 MA0106.3.TP53 jaspar_vertebrates 1 TP53 p53-related factors P04637 HT-SELEX +MA0107 MA0107.1.RELA jaspar_vertebrates 1 RELA NF-kappaB-related factors Q04206 SELEX +MA0108 MA0108.2.TBP jaspar_vertebrates 1 TBP TBP-related factors P20226 +MA0109 MA0109.1.HLTF jaspar_vertebrates 1 HLTF Myb/SANT domain factors Q95216 SELEX +MA0111 MA0111.1.Spz1 jaspar_vertebrates 1 Spz1 . Q99MY0 SELEX +MA0112 MA0112.3.ESR1 jaspar_vertebrates 1 ESR1 Steroid hormone receptors (NR3) P03372 HT-SELEX +MA0113 MA0113.3.NR3C1 jaspar_vertebrates 1 NR3C1 Steroid hormone receptors (NR3) P04150 HT-SELEX +MA0114 MA0114.3.Hnf4a jaspar_vertebrates 1 Hnf4a RXR-related receptors (NR2) P49698 HT-SELEX +MA0115 MA0115.1.NR1H2::RXRA jaspar_vertebrates 1 NR1H2+RXRA RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1) P55055;P19793 SELEX +MA0116 MA0116.1.Znf423 jaspar_vertebrates 1 Znf423 Factors with multiple dispersed zinc fingers O08961 SELEX +MA0117 MA0117.2.Mafb jaspar_vertebrates 1 Mafb Maf-related factors P54841 HT-SELEX +MA0119 MA0119.1.NFIC::TLX1 jaspar_vertebrates 1 NFIC+TLX1 NK-related factors;Nuclear factor 1 P08651;P31314 SELEX +MA0122 MA0122.2.NKX3-2 jaspar_vertebrates 1 NKX3-2 NK-related factors P78367 HT-SELEX +MA0124 MA0124.2.Nkx3-1 jaspar_vertebrates 1 Nkx3-1 NK-related factors P97436 HT-SELEX +MA0125 MA0125.1.Nobox jaspar_vertebrates 1 Nobox Paired-related HD factors Q8VIH1 SELEX +MA0130 MA0130.1.ZNF354C jaspar_vertebrates 1 ZNF354C More than 3 adjacent zinc finger factors Q86Y25 SELEX +MA0131 MA0131.2.HINFP jaspar_vertebrates 1 HINFP Factors with multiple dispersed zinc fingers Q9BQA5 HT-SELEX +MA0132 MA0132.2.PDX1 jaspar_vertebrates 1 PDX1 HOX-related factors P52945 HT-SELEX +MA0135 MA0135.1.Lhx3 jaspar_vertebrates 1 Lhx3 HD-LIM factors P50481 SELEX +MA0136 MA0136.2.ELF5 jaspar_vertebrates 1 ELF5 Ets-related factors Q9UKW6 HT-SELEX +MA0137 MA0137.3.STAT1 jaspar_vertebrates 1 STAT1 STAT factors P42224 ChIP-seq +MA0138 MA0138.2.REST jaspar_vertebrates 1 REST Factors with multiple dispersed zinc fingers Q13127 ChIP-seq +MA0139 MA0139.1.CTCF jaspar_vertebrates 1 CTCF More than 3 adjacent zinc finger factors P49711 ChIP-seq +MA0140 MA0140.2.GATA1::TAL1 jaspar_vertebrates 1 GATA1+TAL1 GATA-type zinc fingers;Tal-related factors P15976;P17542 ChIP-seq +MA0141 MA0141.3.ESRRB jaspar_vertebrates 1 ESRRB Steroid hormone receptors (NR3) O95718 HT-SELEX +MA0142 MA0142.1.Pou5f1::Sox2 jaspar_vertebrates 1 Pou5f1+Sox2 POU domain factors;SOX-related factors P20263;P48432 ChIP-seq +MA0143 MA0143.3.Sox2 jaspar_vertebrates 1 Sox2 SOX-related factors P48432 ChIP-seq +MA0144 MA0144.2.STAT3 jaspar_vertebrates 1 STAT3 STAT factors P40763 ChIP-seq +MA0145 MA0145.3.TFCP2 jaspar_vertebrates 1 TFCP2 CP2-related factors Q12800 HT-SELEX +MA0146 MA0146.2.Zfx jaspar_vertebrates 1 Zfx More than 3 adjacent zinc finger factors P17012 ChIP-seq +MA0147 MA0147.3.MYC jaspar_vertebrates 1 MYC bHLH-ZIP factors P01106 ChIP-seq +MA0148 MA0148.3.FOXA1 jaspar_vertebrates 1 FOXA1 Forkhead box (FOX) factors P55317 ChIP-seq +MA0149 MA0149.1.EWSR1-FLI1 jaspar_vertebrates 1 EWSR1-FLI1 Ets-related factors F1JVV7;F1JVV8 ChIP-seq +MA0150 MA0150.2.Nfe2l2 jaspar_vertebrates 1 Nfe2l2 Jun-related factors Q60795 ChIP-seq +MA0151 MA0151.1.Arid3a jaspar_vertebrates 1 Arid3a ARID-related factors Q62431 SELEX +MA0152 MA0152.1.NFATC2 jaspar_vertebrates 1 NFATC2 NFAT-related factors Q13469 COMPILED +MA0153 MA0153.2.HNF1B jaspar_vertebrates 1 HNF1B POU domain factors P35680 HT-SELEX +MA0154 MA0154.3.EBF1 jaspar_vertebrates 1 EBF1 Early B-Cell Factor-related factors Q9UH73 HT-SELEX +MA0155 MA0155.1.INSM1 jaspar_vertebrates 1 INSM1 Factors with multiple dispersed zinc fingers Q01101 COMPILED +MA0156 MA0156.2.FEV jaspar_vertebrates 1 FEV Ets-related factors Q99581 HT-SELEX +MA0157 MA0157.2.FOXO3 jaspar_vertebrates 1 FOXO3 Forkhead box (FOX) factors O43524 HT-SELEX +MA0158 MA0158.1.HOXA5 jaspar_vertebrates 1 HOXA5 HOX-related factors P20719 COMPILED +MA0159 MA0159.1.RARA::RXRA jaspar_vertebrates 1 RARA+RXRA RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1) P10276;P19793 COMPILED +MA0160 MA0160.1.NR4A2 jaspar_vertebrates 1 NR4A2 NGFI-B-related receptors (NR4) P43354 COMPILED +MA0161 MA0161.2.NFIC jaspar_vertebrates 1 NFIC Nuclear factor 1 P08651 ChIP-seq +MA0162 MA0162.3.EGR1 jaspar_vertebrates 1 EGR1 Three-zinc finger Kruppel-related factors P18146 HT-SELEX +MA0163 MA0163.1.PLAG1 jaspar_vertebrates 1 PLAG1 More than 3 adjacent zinc finger factors Q6DJT9 bacterial 1-hybrid +MA0164 MA0164.1.Nr2e3 jaspar_vertebrates 1 Nr2e3 RXR-related receptors (NR2) Q9QXZ7 SELEX +MA0258 MA0258.2.ESR2 jaspar_vertebrates 1 ESR2 Steroid hormone receptors (NR3) Q92731 ChIP-seq +MA0259 MA0259.1.ARNT::HIF1A jaspar_vertebrates 1 ARNT+HIF1A PAS domain factors P27540;Q16665 COMPILED +MA0442 MA0442.2.SOX10 jaspar_vertebrates 1 SOX10 SOX-related factors P56693 ChIP-seq +MA0461 MA0461.2.Atoh1 jaspar_vertebrates 1 Atoh1 Tal-related factors P48985 HT-SELEX +MA0462 MA0462.1.BATF::JUN jaspar_vertebrates 1 BATF+JUN B-ATF-related factors;Jun-related factors Q16520;P05412 ChIP-seq +MA0463 MA0463.1.Bcl6 jaspar_vertebrates 1 Bcl6 More than 3 adjacent zinc finger factors P41183 ChIP-seq +MA0464 MA0464.2.BHLHE40 jaspar_vertebrates 1 BHLHE40 Hairy-related factors O14503 HT-SELEX +MA0465 MA0465.1.CDX2 jaspar_vertebrates 1 CDX2 HOX-related factors Q99626 ChIP-seq +MA0466 MA0466.2.CEBPB jaspar_vertebrates 1 CEBPB C/EBP-related P17676 HT-SELEX +MA0467 MA0467.1.Crx jaspar_vertebrates 1 Crx Paired-related HD factors O54751 ChIP-seq +MA0468 MA0468.1.DUX4 jaspar_vertebrates 1 DUX4 Paired-related HD factors Q9UBX2 ChIP-seq +MA0469 MA0469.2.E2F3 jaspar_vertebrates 1 E2F3 E2F-related factors O00716 HT-SELEX +MA0470 MA0470.1.E2F4 jaspar_vertebrates 1 E2F4 E2F-related factors Q16254 ChIP-seq +MA0471 MA0471.1.E2F6 jaspar_vertebrates 1 E2F6 E2F-related factors O75461 ChIP-seq +MA0472 MA0472.2.EGR2 jaspar_vertebrates 1 EGR2 Three-zinc finger Krüppel-related factors P11161 HT-SELEX +MA0473 MA0473.2.ELF1 jaspar_vertebrates 1 ELF1 Ets-related factors P32519 HT-SELEX +MA0474 MA0474.2.ERG jaspar_vertebrates 1 ERG Ets-related factors P11308 HT-SELEX +MA0475 MA0475.2.FLI1 jaspar_vertebrates 1 FLI1 Ets-related factors Q01543 HT-SELEX +MA0476 MA0476.1.FOS jaspar_vertebrates 1 FOS Fos-related factors P01100 ChIP-seq +MA0477 MA0477.1.FOSL1 jaspar_vertebrates 1 FOSL1 Fos-related factors P15407 ChIP-seq +MA0478 MA0478.1.FOSL2 jaspar_vertebrates 1 FOSL2 Fos-related factors P15408 ChIP-seq +MA0479 MA0479.1.FOXH1 jaspar_vertebrates 1 FOXH1 Forkhead box (FOX) factors O75593 ChIP-seq +MA0480 MA0480.1.Foxo1 jaspar_vertebrates 1 Foxo1 Forkhead box (FOX) factors Q9R1E0 ChIP-seq +MA0481 MA0481.2.FOXP1 jaspar_vertebrates 1 FOXP1 Forkhead box (FOX) factors Q9H334 ChIP-seq +MA0482 MA0482.1.Gata4 jaspar_vertebrates 1 Gata4 GATA-type zinc fingers Q08369 ChIP-seq +MA0483 MA0483.1.Gfi1b jaspar_vertebrates 1 Gfi1b More than 3 adjacent zinc finger factors O70237 ChIP-seq +MA0484 MA0484.1.HNF4G jaspar_vertebrates 1 HNF4G RXR-related receptors (NR2) Q14541 ChIP-seq +MA0485 MA0485.1.Hoxc9 jaspar_vertebrates 1 Hoxc9 HOX-related factors P09633 ChIP-seq +MA0486 MA0486.2.HSF1 jaspar_vertebrates 1 HSF1 HSF factors Q00613 HT-SELEX +MA0488 MA0488.1.JUN jaspar_vertebrates 1 JUN Jun-related factors P05412 ChIP-seq +MA0489 MA0489.1.JUN(var.2) jaspar_vertebrates pwm JUN(var.2) Jun-related factors P05412 ChIP-seq +MA0490 MA0490.1.JUNB jaspar_vertebrates 1 JUNB Jun-related factors P17275 ChIP-seq +MA0491 MA0491.1.JUND jaspar_vertebrates 1 JUND Jun-related factors P17535 ChIP-seq +MA0492 MA0492.1.JUND(var.2) jaspar_vertebrates pwm JUND(var.2) Jun-related factors P17535 ChIP-seq +MA0493 MA0493.1.Klf1 jaspar_vertebrates 1 Klf1 Three-zinc finger Krüppel-related factors P46099 ChIP-seq +MA0494 MA0494.1.Nr1h3::Rxra jaspar_vertebrates 1 Nr1h3+Rxra RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1) Q9Z0Y9;P28700 ChIP-seq +MA0495 MA0495.2.MAFF jaspar_vertebrates 1 MAFF Maf-related factors Q9ULX9 ChIP-seq +MA0496 MA0496.2.MAFK jaspar_vertebrates 1 MAFK Maf-related factors O60675 ChIP-seq +MA0497 MA0497.1.MEF2C jaspar_vertebrates 1 MEF2C Regulators of differentiation Q06413 ChIP-seq +MA0498 MA0498.2.MEIS1 jaspar_vertebrates 1 MEIS1 TALE-type homeo domain factors O00470 HT-SELEX +MA0499 MA0499.1.Myod1 jaspar_vertebrates 1 Myod1 MyoD / ASC-related factors P10085 ChIP-seq +MA0500 MA0500.1.Myog jaspar_vertebrates 1 Myog MyoD / ASC-related factors P12979 ChIP-seq +MA0501 MA0501.1.MAF::NFE2 jaspar_vertebrates 1 MAF+NFE2 Jun-related factors;Maf-related factors O75444;Q16621 ChIP-seq +MA0502 MA0502.1.NFYB jaspar_vertebrates 1 NFYB NFY P25208 ChIP-seq +MA0503 MA0503.1.Nkx2-5(var.2) jaspar_vertebrates pwm Nkx2-5(var.2) NK-related factors P42582 ChIP-seq +MA0504 MA0504.1.NR2C2 jaspar_vertebrates 1 NR2C2 RXR-related receptors (NR2) P49116 ChIP-seq +MA0505 MA0505.1.Nr5a2 jaspar_vertebrates 1 Nr5a2 FTZ-F1-related receptors (NR5) P45448 ChIP-seq +MA0506 MA0506.1.NRF1 jaspar_vertebrates 1 NRF1 Jun-related factors Q16656 ChIP-seq +MA0507 MA0507.1.POU2F2 jaspar_vertebrates 1 POU2F2 POU domain factors P09086 ChIP-seq +MA0508 MA0508.2.PRDM1 jaspar_vertebrates 1 PRDM1 More than 3 adjacent zinc finger factors O75626 ChIP-seq +MA0509 MA0509.1.Rfx1 jaspar_vertebrates 1 Rfx1 RFX-related factors P48377 ChIP-seq +MA0510 MA0510.2.RFX5 jaspar_vertebrates 1 RFX5 RFX-related factors P48382 HT-SELEX +MA0511 MA0511.2.RUNX2 jaspar_vertebrates 1 RUNX2 Runt-related factors Q13950 HT-SELEX +MA0512 MA0512.2.Rxra jaspar_vertebrates 1 Rxra RXR-related receptors (NR2) P28700 HT-SELEX +MA0513 MA0513.1.SMAD2::SMAD3::SMAD4 jaspar_vertebrates 1 SMAD2+SMAD3+SMAD4 SMAD factors Q15796;P84022;Q13485 ChIP-seq +MA0514 MA0514.1.Sox3 jaspar_vertebrates 1 Sox3 SOX-related factors P53784 ChIP-seq +MA0515 MA0515.1.Sox6 jaspar_vertebrates 1 Sox6 SOX-related factors P40645 ChIP-seq +MA0516 MA0516.1.SP2 jaspar_vertebrates 1 SP2 Three-zinc finger Krüppel-related factors Q02086 ChIP-seq +MA0517 MA0517.1.STAT1::STAT2 jaspar_vertebrates 1 STAT1+STAT2 STAT factors P42224;P52630 ChIP-seq +MA0518 MA0518.1.Stat4 jaspar_vertebrates 1 Stat4 STAT factors P42228 ChIP-seq +MA0519 MA0519.1.Stat5a::Stat5b jaspar_vertebrates 1 Stat5a+Stat5b STAT factors P42230;P42232 ChIP-seq +MA0520 MA0520.1.Stat6 jaspar_vertebrates 1 Stat6 STAT factors P52633 ChIP-seq +MA0521 MA0521.1.Tcf12 jaspar_vertebrates 1 Tcf12 E2A-related factors Q61286 ChIP-seq +MA0522 MA0522.2.TCF3 jaspar_vertebrates 1 TCF3 E2A-related factors P15923 HT-SELEX +MA0523 MA0523.1.TCF7L2 jaspar_vertebrates 1 TCF7L2 TCF-7-related factors Q9NQB0 ChIP-seq +MA0524 MA0524.2.TFAP2C jaspar_vertebrates 1 TFAP2C AP-2 Q92754 HT-SELEX +MA0525 MA0525.2.TP63 jaspar_vertebrates 1 TP63 p53-related factors Q9H3D4 HT-SELEX +MA0526 MA0526.2.USF2 jaspar_vertebrates 1 USF2 bHLH-ZIP factors Q15853 ChIP-seq +MA0527 MA0527.1.ZBTB33 jaspar_vertebrates 1 ZBTB33 Other factors with up to three adjacent zinc fingers Q86T24 ChIP-seq +MA0528 MA0528.1.ZNF263 jaspar_vertebrates 1 ZNF263 More than 3 adjacent zinc finger factors O14978 ChIP-seq +MA0591 MA0591.1.Bach1::Mafk jaspar_vertebrates 1 Bach1+Mafk Jun-related factors;Maf-related factors P97302;Q61827 ChIP-seq +MA0592 MA0592.2.Esrra jaspar_vertebrates 1 Esrra Steroid hormone receptors (NR3) O08580 HT-SELEX +MA0593 MA0593.1.FOXP2 jaspar_vertebrates 1 FOXP2 Forkhead box (FOX) factors O15409 ChIP-seq +MA0594 MA0594.1.Hoxa9 jaspar_vertebrates 1 Hoxa9 HOX-related factors P09631 ChIP-seq +MA0595 MA0595.1.SREBF1 jaspar_vertebrates 1 SREBF1 bHLH-ZIP factors P36956 ChIP-seq +MA0596 MA0596.1.SREBF2 jaspar_vertebrates 1 SREBF2 bHLH-ZIP factors Q12772 ChIP-seq +MA0597 MA0597.1.THAP1 jaspar_vertebrates 1 THAP1 THAP-related factors Q9NVV9 ChIP-seq +MA0598 MA0598.2.EHF jaspar_vertebrates 1 EHF Ets-related factors Q9NZC4 HT-SELEX +MA0599 MA0599.1.KLF5 jaspar_vertebrates 1 KLF5 Three-zinc finger Krüppel-related factors Q13887 ChIP-seq +MA0600 MA0600.2.RFX2 jaspar_vertebrates 1 RFX2 RFX-related factors P48378 HT-SELEX +MA0601 MA0601.1.Arid3b jaspar_vertebrates 1 Arid3b ARID-related factors Q9Z1N7 PBM +MA0602 MA0602.1.Arid5a jaspar_vertebrates 1 Arid5a ARID-related factors Q3U108 universal protein binding microarray (PBM) +MA0603 MA0603.1.Arntl jaspar_vertebrates 1 Arntl PAS domain factors Q9WTL8 PBM +MA0604 MA0604.1.Atf1 jaspar_vertebrates 1 Atf1 CREB-related factors P81269 PBM +MA0605 MA0605.1.Atf3 jaspar_vertebrates 1 Atf3 Fos-related factors Q60765 PBM +MA0606 MA0606.1.NFAT5 jaspar_vertebrates 1 NFAT5 NFAT-related factors F1NSH4 PBM +MA0607 MA0607.1.Bhlha15 jaspar_vertebrates 1 Bhlha15 Tal-related factors Q9QYC3 PBM +MA0608 MA0608.1.Creb3l2 jaspar_vertebrates 1 Creb3l2 CREB-related factors Q8BH52 PBM +MA0609 MA0609.1.Crem jaspar_vertebrates 1 Crem CREB-related factors P27699 PBM +MA0610 MA0610.1.DMRT3 jaspar_vertebrates 1 DMRT3 DMRT Q9NQL9 PBM +MA0611 MA0611.1.Dux jaspar_vertebrates 1 Dux Paired-related HD factors A1JVI8 PBM +MA0612 MA0612.1.EMX1 jaspar_vertebrates 1 EMX1 NK-related factors Q04741 PBM +MA0613 MA0613.1.FOXG1 jaspar_vertebrates 1 FOXG1 Forkhead box (FOX) factors P55316 PBM +MA0614 MA0614.1.Foxj2 jaspar_vertebrates 1 Foxj2 Forkhead box (FOX) factors Q9ES18 PBM +MA0615 MA0615.1.Gmeb1 jaspar_vertebrates 1 Gmeb1 GMEB Q9JL60 universal protein binding microarray (PBM) +MA0616 MA0616.1.Hes2 jaspar_vertebrates 1 Hes2 Hairy-related factors O54792 PBM +MA0617 MA0617.1.Id2 jaspar_vertebrates 1 Id2 HLH domain only P41136 PBM +MA0618 MA0618.1.LBX1 jaspar_vertebrates 1 LBX1 NK-related factors P52954 PBM +MA0619 MA0619.1.LIN54 jaspar_vertebrates 1 LIN54 . F1NCE0 PBM +MA0620 MA0620.2.MITF jaspar_vertebrates 1 MITF bHLH-ZIP factors O75030 ChIP-seq +MA0621 MA0621.1.mix-a jaspar_vertebrates 1 mix-a Paired-related HD factors P21711 PBM +MA0622 MA0622.1.Mlxip jaspar_vertebrates 1 Mlxip bHLH-ZIP factors Q2VPU4 PBM +MA0623 MA0623.1.Neurog1 jaspar_vertebrates 1 Neurog1 Tal-related factors P70660 PBM +MA0624 MA0624.1.NFATC1 jaspar_vertebrates 1 NFATC1 NFAT-related factors G1MTN6 PBM +MA0625 MA0625.1.NFATC3 jaspar_vertebrates 1 NFATC3 NFAT-related factors Q12968 PBM +MA0626 MA0626.1.Npas2 jaspar_vertebrates 1 Npas2 PAS domain factors P97460 PBM +MA0627 MA0627.1.Pou2f3 jaspar_vertebrates 1 Pou2f3 POU domain factors P31362 universal protein binding microarray (PBM) +MA0628 MA0628.1.POU6F1 jaspar_vertebrates 1 POU6F1 POU domain factors Q14863 PBM +MA0629 MA0629.1.Rhox11 jaspar_vertebrates 1 Rhox11 Paired-related HD factors Q810N8 universal protein binding microarray (PBM) +MA0630 MA0630.1.SHOX jaspar_vertebrates 1 SHOX Paired-related HD factors O15266 PBM +MA0631 MA0631.1.Six3 jaspar_vertebrates 1 Six3 HD-SINE factors Q62233 universal protein binding microarray (PBM) +MA0632 MA0632.1.Tcfl5 jaspar_vertebrates 1 Tcfl5 PAS domain factors Q32NY8 PBM +MA0633 MA0633.1.Twist2 jaspar_vertebrates 1 Twist2 Tal-related factors Q9D030 PBM +MA0634 MA0634.1.ALX3 jaspar_vertebrates 1 ALX3 Paired-related HD factors O95076 HT-SELEX +MA0635 MA0635.1.BARHL2 jaspar_vertebrates 1 BARHL2 NK-related factors Q9NY43 HT-SELEX +MA0636 MA0636.1.BHLHE41 jaspar_vertebrates 1 BHLHE41 Hairy-related factors Q9C0J9 HT-SELEX +MA0637 MA0637.1.CENPB jaspar_vertebrates 1 CENPB . P07199 HT-SELEX +MA0638 MA0638.1.CREB3 jaspar_vertebrates 1 CREB3 CREB-related factors O43889 HT-SELEX +MA0639 MA0639.1.DBP jaspar_vertebrates 1 DBP C/EBP-related Q10586 HT-SELEX +MA0640 MA0640.1.ELF3 jaspar_vertebrates 1 ELF3 Ets-related factors P78545 HT-SELEX +MA0641 MA0641.1.ELF4 jaspar_vertebrates 1 ELF4 Ets-related factors Q99607 HT-SELEX +MA0642 MA0642.1.EN2 jaspar_vertebrates 1 EN2 NK-related factors P19622 HT-SELEX +MA0643 MA0643.1.Esrrg jaspar_vertebrates 1 Esrrg Steroid hormone receptors (NR3) P62509 HT-SELEX +MA0644 MA0644.1.ESX1 jaspar_vertebrates 1 ESX1 Paired-related HD factors Q8N693 HT-SELEX +MA0645 MA0645.1.ETV6 jaspar_vertebrates 1 ETV6 Ets-related factors P41212 HT-SELEX +MA0646 MA0646.1.GCM1 jaspar_vertebrates 1 GCM1 GCM factors Q9NP62 HT-SELEX +MA0647 MA0647.1.GRHL1 jaspar_vertebrates 1 GRHL1 Grainyhead-related factors Q9NZI5 HT-SELEX +MA0648 MA0648.1.GSC jaspar_vertebrates 1 GSC Paired-related HD factors P56915 HT-SELEX +MA0649 MA0649.1.HEY2 jaspar_vertebrates 1 HEY2 Hairy-related factors Q9UBP5 HT-SELEX +MA0650 MA0650.1.HOXA13 jaspar_vertebrates 1 HOXA13 HOX-related factors P31271 HT-SELEX +MA0651 MA0651.1.HOXC11 jaspar_vertebrates 1 HOXC11 HOX-related factors O43248 HT-SELEX +MA0652 MA0652.1.IRF8 jaspar_vertebrates 1 IRF8 Interferon-regulatory factors Q02556 HT-SELEX +MA0653 MA0653.1.IRF9 jaspar_vertebrates 1 IRF9 Interferon-regulatory factors Q00978 HT-SELEX +MA0654 MA0654.1.ISX jaspar_vertebrates 1 ISX Paired-related HD factors Q2M1V0 HT-SELEX +MA0655 MA0655.1.JDP2 jaspar_vertebrates 1 JDP2 Fos-related factors Q8WYK2 HT-SELEX +MA0656 MA0656.1.JDP2(var.2) jaspar_vertebrates pwm JDP2(var.2) Fos-related factors Q8WYK2 HT-SELEX +MA0657 MA0657.1.KLF13 jaspar_vertebrates 1 KLF13 Three-zinc finger Krüppel-related factors Q9Y2Y9 HT-SELEX +MA0658 MA0658.1.LHX6 jaspar_vertebrates 1 LHX6 HD-LIM factors Q9UPM6 HT-SELEX +MA0659 MA0659.1.MAFG jaspar_vertebrates 1 MAFG Maf-related factors O15525 HT-SELEX +MA0660 MA0660.1.MEF2B jaspar_vertebrates 1 MEF2B Regulators of differentiation Q02080 HT-SELEX +MA0661 MA0661.1.MEOX1 jaspar_vertebrates 1 MEOX1 HOX-related factors P50221 HT-SELEX +MA0662 MA0662.1.MIXL1 jaspar_vertebrates 1 MIXL1 Paired-related HD factors Q9H2W2 HT-SELEX +MA0663 MA0663.1.MLX jaspar_vertebrates 1 MLX bHLH-ZIP factors Q9UH92 HT-SELEX +MA0664 MA0664.1.MLXIPL jaspar_vertebrates 1 MLXIPL bHLH-ZIP factors Q9NP71 HT-SELEX +MA0665 MA0665.1.MSC jaspar_vertebrates 1 MSC Tal-related factors O60682 HT-SELEX +MA0666 MA0666.1.MSX1 jaspar_vertebrates 1 MSX1 NK-related factors P28360 HT-SELEX +MA0667 MA0667.1.MYF6 jaspar_vertebrates 1 MYF6 MyoD / ASC-related factors P23409 HT-SELEX +MA0668 MA0668.1.NEUROD2 jaspar_vertebrates 1 NEUROD2 Tal-related factors Q15784 HT-SELEX +MA0669 MA0669.1.NEUROG2 jaspar_vertebrates 1 NEUROG2 Tal-related factors Q9H2A3 HT-SELEX +MA0670 MA0670.1.NFIA jaspar_vertebrates 1 NFIA Nuclear factor 1 Q12857 HT-SELEX +MA0671 MA0671.1.NFIX jaspar_vertebrates 1 NFIX Nuclear factor 1 Q14938 HT-SELEX +MA0672 MA0672.1.NKX2-3 jaspar_vertebrates 1 NKX2-3 NK-related factors Q8TAU0 HT-SELEX +MA0673 MA0673.1.NKX2-8 jaspar_vertebrates 1 NKX2-8 NK-related factors O15522 HT-SELEX +MA0674 MA0674.1.NKX6-1 jaspar_vertebrates 1 NKX6-1 NK-related factors P78426 HT-SELEX +MA0675 MA0675.1.NKX6-2 jaspar_vertebrates 1 NKX6-2 NK-related factors Q9C056 HT-SELEX +MA0676 MA0676.1.Nr2e1 jaspar_vertebrates 1 Nr2e1 RXR-related receptors (NR2) Q9Y466 HT-SELEX +MA0677 MA0677.1.Nr2f6 jaspar_vertebrates 1 Nr2f6 RXR-related receptors (NR2) P43136 HT-SELEX +MA0678 MA0678.1.OLIG2 jaspar_vertebrates 1 OLIG2 Tal-related factors Q13516 HT-SELEX +MA0679 MA0679.1.ONECUT1 jaspar_vertebrates 1 ONECUT1 HD-CUT factors Q9UBC0 HT-SELEX +MA0680 MA0680.1.PAX7 jaspar_vertebrates 1 PAX7 Paired plus homeo domain P23759 HT-SELEX +MA0681 MA0681.1.Phox2b jaspar_vertebrates 1 Phox2b Paired-related HD factors Q99453 HT-SELEX +MA0682 MA0682.1.Pitx1 jaspar_vertebrates 1 Pitx1 Paired-related HD factors P70314 HT-SELEX +MA0683 MA0683.1.POU4F2 jaspar_vertebrates 1 POU4F2 POU domain factors Q12837 HT-SELEX +MA0684 MA0684.1.RUNX3 jaspar_vertebrates 1 RUNX3 Runt-related factors Q13761 HT-SELEX +MA0685 MA0685.1.SP4 jaspar_vertebrates 1 SP4 Three-zinc finger Krüppel-related factors Q02446 HT-SELEX +MA0686 MA0686.1.SPDEF jaspar_vertebrates 1 SPDEF Ets-related factors O95238 HT-SELEX +MA0687 MA0687.1.SPIC jaspar_vertebrates 1 SPIC Ets-related factors Q8N5J4 HT-SELEX +MA0688 MA0688.1.TBX2 jaspar_vertebrates 1 TBX2 TBX2-related factors Q13207 HT-SELEX +MA0689 MA0689.1.TBX20 jaspar_vertebrates 1 TBX20 TBX1-related factors Q9UMR3 HT-SELEX +MA0690 MA0690.1.TBX21 jaspar_vertebrates 1 TBX21 TBrain-related factors Q9UL17 HT-SELEX +MA0691 MA0691.1.TFAP4 jaspar_vertebrates 1 TFAP4 bHLH-ZIP factors Q01664 HT-SELEX +MA0692 MA0692.1.TFEB jaspar_vertebrates 1 TFEB bHLH-ZIP factors P19484 HT-SELEX +MA0693 MA0693.2.VDR jaspar_vertebrates 1 VDR Thyroid hormone receptor-related factors (NR1) P11473 SMiLE-seq +MA0694 MA0694.1.ZBTB7B jaspar_vertebrates 1 ZBTB7B More than 3 adjacent zinc finger factors O15156 HT-SELEX +MA0695 MA0695.1.ZBTB7C jaspar_vertebrates 1 ZBTB7C More than 3 adjacent zinc finger factors A1YPR0 HT-SELEX +MA0696 MA0696.1.ZIC1 jaspar_vertebrates 1 ZIC1 More than 3 adjacent zinc finger factors Q15915 HT-SELEX +MA0697 MA0697.1.ZIC3 jaspar_vertebrates 1 ZIC3 More than 3 adjacent zinc finger factors O60481 HT-SELEX +MA0698 MA0698.1.ZBTB18 jaspar_vertebrates 1 ZBTB18 More than 3 adjacent zinc finger factors Q99592 HT-SELEX +MA0699 MA0699.1.LBX2 jaspar_vertebrates 1 LBX2 NK-related factors Q6XYB7 HT-SELEX +MA0700 MA0700.1.LHX2 jaspar_vertebrates 1 LHX2 HD-LIM factors P50458 HT-SELEX +MA0701 MA0701.1.LHX9 jaspar_vertebrates 1 LHX9 HD-LIM factors Q9NQ69 HT-SELEX +MA0702 MA0702.1.LMX1A jaspar_vertebrates 1 LMX1A HD-LIM factors Q8TE12 HT-SELEX +MA0703 MA0703.1.LMX1B jaspar_vertebrates 1 LMX1B HD-LIM factors O60663 HT-SELEX +MA0704 MA0704.1.Lhx4 jaspar_vertebrates 1 Lhx4 HD-LIM factors P53776 HT-SELEX +MA0705 MA0705.1.Lhx8 jaspar_vertebrates 1 Lhx8 HD-LIM factors O35652 HT-SELEX +MA0706 MA0706.1.MEOX2 jaspar_vertebrates 1 MEOX2 HOX-related factors Q6FHY5 HT-SELEX +MA0707 MA0707.1.MNX1 jaspar_vertebrates 1 MNX1 HOX-related factors P50219 HT-SELEX +MA0708 MA0708.1.MSX2 jaspar_vertebrates 1 MSX2 NK-related factors P35548 HT-SELEX +MA0709 MA0709.1.Msx3 jaspar_vertebrates 1 Msx3 NK-related factors P70354 HT-SELEX +MA0710 MA0710.1.NOTO jaspar_vertebrates 1 NOTO NK-related factors A8MTQ0 HT-SELEX +MA0711 MA0711.1.OTX1 jaspar_vertebrates 1 OTX1 Paired-related HD factors P32242 HT-SELEX +MA0712 MA0712.1.OTX2 jaspar_vertebrates 1 OTX2 Paired-related HD factors P32243 HT-SELEX +MA0713 MA0713.1.PHOX2A jaspar_vertebrates 1 PHOX2A Paired-related HD factors O14813 HT-SELEX +MA0714 MA0714.1.PITX3 jaspar_vertebrates 1 PITX3 Paired-related HD factors O75364 HT-SELEX +MA0715 MA0715.1.PROP1 jaspar_vertebrates 1 PROP1 Paired-related HD factors O75360 HT-SELEX +MA0716 MA0716.1.PRRX1 jaspar_vertebrates 1 PRRX1 Paired-related HD factors P54821 HT-SELEX +MA0717 MA0717.1.RAX2 jaspar_vertebrates 1 RAX2 Paired-related HD factors Q96IS3 HT-SELEX +MA0718 MA0718.1.RAX jaspar_vertebrates 1 RAX Paired-related HD factors Q9Y2V3 HT-SELEX +MA0719 MA0719.1.RHOXF1 jaspar_vertebrates 1 RHOXF1 Paired-related HD factors Q8NHV9 HT-SELEX +MA0720 MA0720.1.Shox2 jaspar_vertebrates 1 Shox2 Paired-related HD factors P70390 HT-SELEX +MA0721 MA0721.1.UNCX jaspar_vertebrates 1 UNCX Paired-related HD factors A6NJT0 HT-SELEX +MA0722 MA0722.1.VAX1 jaspar_vertebrates 1 VAX1 NK-related factors Q5SQQ9 HT-SELEX +MA0723 MA0723.1.VAX2 jaspar_vertebrates 1 VAX2 NK-related factors Q9UIW0 HT-SELEX +MA0724 MA0724.1.VENTX jaspar_vertebrates 1 VENTX NK-related factors O95231 HT-SELEX +MA0725 MA0725.1.VSX1 jaspar_vertebrates 1 VSX1 Paired-related HD factors Q9NZR4 HT-SELEX +MA0726 MA0726.1.VSX2 jaspar_vertebrates 1 VSX2 Paired-related HD factors P58304 HT-SELEX +MA0727 MA0727.1.NR3C2 jaspar_vertebrates 1 NR3C2 Steroid hormone receptors (NR3) P08235 HT-SELEX +MA0728 MA0728.1.Nr2f6(var.2) jaspar_vertebrates pwm Nr2f6(var.2) RXR-related receptors (NR2) P43136 HT-SELEX +MA0729 MA0729.1.RARA jaspar_vertebrates 1 RARA Thyroid hormone receptor-related factors (NR1) P10276 HT-SELEX +MA0730 MA0730.1.RARA(var.2) jaspar_vertebrates pwm RARA(var.2) Thyroid hormone receptor-related factors (NR1) P10276 HT-SELEX +MA0731 MA0731.1.BCL6B jaspar_vertebrates 1 BCL6B More than 3 adjacent zinc finger factors A8KA13 HT-SELEX +MA0732 MA0732.1.EGR3 jaspar_vertebrates 1 EGR3 Three-zinc finger Krüppel-related factors Q06889 HT-SELEX +MA0733 MA0733.1.EGR4 jaspar_vertebrates 1 EGR4 Three-zinc finger Krüppel-related factors Q05215 HT-SELEX +MA0734 MA0734.1.GLI2 jaspar_vertebrates 1 GLI2 More than 3 adjacent zinc finger factors P10070 HT-SELEX +MA0735 MA0735.1.GLIS1 jaspar_vertebrates 1 GLIS1 More than 3 adjacent zinc finger factors Q8NBF1 HT-SELEX +MA0736 MA0736.1.GLIS2 jaspar_vertebrates 1 GLIS2 More than 3 adjacent zinc finger factors Q9BZE0 HT-SELEX +MA0737 MA0737.1.GLIS3 jaspar_vertebrates 1 GLIS3 More than 3 adjacent zinc finger factors Q8NEA6 HT-SELEX +MA0738 MA0738.1.HIC2 jaspar_vertebrates 1 HIC2 Factors with multiple dispersed zinc fingers Q96JB3 HT-SELEX +MA0739 MA0739.1.Hic1 jaspar_vertebrates 1 Hic1 Factors with multiple dispersed zinc fingers Q9R1Y5 HT-SELEX +MA0740 MA0740.1.KLF14 jaspar_vertebrates 1 KLF14 Three-zinc finger Krüppel-related factors Q8TD94 HT-SELEX +MA0741 MA0741.1.KLF16 jaspar_vertebrates 1 KLF16 Three-zinc finger Krüppel-related factors Q9BXK1 HT-SELEX +MA0742 MA0742.1.Klf12 jaspar_vertebrates 1 Klf12 Three-zinc finger Krüppel-related factors O35738 HT-SELEX +MA0743 MA0743.1.SCRT1 jaspar_vertebrates 1 SCRT1 More than 3 adjacent zinc finger factors Q9BWW7 HT-SELEX +MA0744 MA0744.1.SCRT2 jaspar_vertebrates 1 SCRT2 More than 3 adjacent zinc finger factors Q9NQ03 HT-SELEX +MA0745 MA0745.1.SNAI2 jaspar_vertebrates 1 SNAI2 More than 3 adjacent zinc finger factors O43623 HT-SELEX +MA0746 MA0746.1.SP3 jaspar_vertebrates 1 SP3 Three-zinc finger Krüppel-related factors Q02447 HT-SELEX +MA0747 MA0747.1.SP8 jaspar_vertebrates 1 SP8 Three-zinc finger Krüppel-related factors Q8IXZ3 HT-SELEX +MA0748 MA0748.1.YY2 jaspar_vertebrates 1 YY2 More than 3 adjacent zinc finger factors O15391 HT-SELEX +MA0749 MA0749.1.ZBED1 jaspar_vertebrates 1 ZBED1 BED zinc finger factors O96006 HT-SELEX +MA0750 MA0750.2.ZBTB7A jaspar_vertebrates 1 ZBTB7A More than 3 adjacent zinc finger factors O95365 ChIP-seq +MA0751 MA0751.1.ZIC4 jaspar_vertebrates 1 ZIC4 More than 3 adjacent zinc finger factors Q8N9L1 HT-SELEX +MA0752 MA0752.1.ZNF410 jaspar_vertebrates 1 ZNF410 More than 3 adjacent zinc finger factors Q86VK4 HT-SELEX +MA0753 MA0753.1.ZNF740 jaspar_vertebrates 1 ZNF740 Other factors with up to three adjacent zinc fingers Q8NDX6 HT-SELEX +MA0754 MA0754.1.CUX1 jaspar_vertebrates 1 CUX1 HD-CUT factors P39880 HT-SELEX +MA0755 MA0755.1.CUX2 jaspar_vertebrates 1 CUX2 HD-CUT factors O14529 HT-SELEX +MA0756 MA0756.1.ONECUT2 jaspar_vertebrates 1 ONECUT2 HD-CUT factors O95948 HT-SELEX +MA0757 MA0757.1.ONECUT3 jaspar_vertebrates 1 ONECUT3 HD-CUT factors O60422 HT-SELEX +MA0758 MA0758.1.E2F7 jaspar_vertebrates 1 E2F7 E2F-related factors Q96AV8 HT-SELEX +MA0759 MA0759.1.ELK3 jaspar_vertebrates 1 ELK3 Ets-related factors P41970 HT-SELEX +MA0760 MA0760.1.ERF jaspar_vertebrates 1 ERF Ets-related factors P50548 HT-SELEX +MA0761 MA0761.1.ETV1 jaspar_vertebrates 1 ETV1 Ets-related factors P50549 HT-SELEX +MA0762 MA0762.1.ETV2 jaspar_vertebrates 1 ETV2 Ets-related factors Q3KNT2 HT-SELEX +MA0763 MA0763.1.ETV3 jaspar_vertebrates 1 ETV3 Ets-related factors P41162 HT-SELEX +MA0764 MA0764.1.ETV4 jaspar_vertebrates 1 ETV4 Ets-related factors P43268 HT-SELEX +MA0765 MA0765.1.ETV5 jaspar_vertebrates 1 ETV5 Ets-related factors P41161 HT-SELEX +MA0766 MA0766.1.GATA5 jaspar_vertebrates 1 GATA5 GATA-type zinc fingers Q9BWX5 HT-SELEX +MA0767 MA0767.1.GCM2 jaspar_vertebrates 1 GCM2 GCM factors O75603 HT-SELEX +MA0768 MA0768.1.LEF1 jaspar_vertebrates 1 LEF1 TCF-7-related factors Q9UJU2 HT-SELEX +MA0769 MA0769.1.Tcf7 jaspar_vertebrates 1 Tcf7 TCF-7-related factors Q00417 HT-SELEX +MA0770 MA0770.1.HSF2 jaspar_vertebrates 1 HSF2 HSF factors Q03933 HT-SELEX +MA0771 MA0771.1.HSF4 jaspar_vertebrates 1 HSF4 HSF factors Q9ULV5 HT-SELEX +MA0772 MA0772.1.IRF7 jaspar_vertebrates 1 IRF7 Interferon-regulatory factors Q92985 HT-SELEX +MA0773 MA0773.1.MEF2D jaspar_vertebrates 1 MEF2D Regulators of differentiation Q05BX2 HT-SELEX +MA0774 MA0774.1.MEIS2 jaspar_vertebrates 1 MEIS2 TALE-type homeo domain factors O14770 HT-SELEX +MA0775 MA0775.1.MEIS3 jaspar_vertebrates 1 MEIS3 TALE-type homeo domain factors Q99687 HT-SELEX +MA0776 MA0776.1.MYBL1 jaspar_vertebrates 1 MYBL1 Myb/SANT domain factors P10243 HT-SELEX +MA0777 MA0777.1.MYBL2 jaspar_vertebrates 1 MYBL2 Myb/SANT domain factors P10244 HT-SELEX +MA0778 MA0778.1.NFKB2 jaspar_vertebrates 1 NFKB2 NF-kappaB-related factors Q00653 HT-SELEX +MA0779 MA0779.1.PAX1 jaspar_vertebrates 1 PAX1 Paired domain only P15863 HT-SELEX +MA0780 MA0780.1.PAX3 jaspar_vertebrates 1 PAX3 Paired plus homeo domain P23760 HT-SELEX +MA0781 MA0781.1.PAX9 jaspar_vertebrates 1 PAX9 Paired domain only P55771 HT-SELEX +MA0782 MA0782.1.PKNOX1 jaspar_vertebrates 1 PKNOX1 TALE-type homeo domain factors P55347 HT-SELEX +MA0783 MA0783.1.PKNOX2 jaspar_vertebrates 1 PKNOX2 TALE-type homeo domain factors Q96KN3 HT-SELEX +MA0784 MA0784.1.POU1F1 jaspar_vertebrates 1 POU1F1 POU domain factors P28069 HT-SELEX +MA0785 MA0785.1.POU2F1 jaspar_vertebrates 1 POU2F1 POU domain factors P14859 HT-SELEX +MA0786 MA0786.1.POU3F1 jaspar_vertebrates 1 POU3F1 POU domain factors Q03052 HT-SELEX +MA0787 MA0787.1.POU3F2 jaspar_vertebrates 1 POU3F2 POU domain factors P20265 HT-SELEX +MA0788 MA0788.1.POU3F3 jaspar_vertebrates 1 POU3F3 POU domain factors P20264 HT-SELEX +MA0789 MA0789.1.POU3F4 jaspar_vertebrates 1 POU3F4 POU domain factors P49335 HT-SELEX +MA0790 MA0790.1.POU4F1 jaspar_vertebrates 1 POU4F1 POU domain factors Q01851 HT-SELEX +MA0791 MA0791.1.POU4F3 jaspar_vertebrates 1 POU4F3 POU domain factors Q15319 HT-SELEX +MA0792 MA0792.1.POU5F1B jaspar_vertebrates 1 POU5F1B POU domain factors Q06416 HT-SELEX +MA0793 MA0793.1.POU6F2 jaspar_vertebrates 1 POU6F2 POU domain factors P78424 HT-SELEX +MA0794 MA0794.1.PROX1 jaspar_vertebrates 1 PROX1 HD-PROS factors Q92786 HT-SELEX +MA0795 MA0795.1.SMAD3 jaspar_vertebrates 1 SMAD3 SMAD factors P84022 HT-SELEX +MA0796 MA0796.1.TGIF1 jaspar_vertebrates 1 TGIF1 TALE-type homeo domain factors Q15583 HT-SELEX +MA0797 MA0797.1.TGIF2 jaspar_vertebrates 1 TGIF2 TALE-type homeo domain factors Q9GZN2 HT-SELEX +MA0798 MA0798.1.RFX3 jaspar_vertebrates 1 RFX3 RFX-related factors P48380 HT-SELEX +MA0799 MA0799.1.RFX4 jaspar_vertebrates 1 RFX4 RFX-related factors Q33E94 HT-SELEX +MA0800 MA0800.1.EOMES jaspar_vertebrates 1 EOMES TBrain-related factors O95936 HT-SELEX +MA0801 MA0801.1.MGA jaspar_vertebrates 1 MGA TBX6-related factors Q8IWI9 HT-SELEX +MA0802 MA0802.1.TBR1 jaspar_vertebrates 1 TBR1 TBrain-related factors Q16650 HT-SELEX +MA0803 MA0803.1.TBX15 jaspar_vertebrates 1 TBX15 TBX1-related factors Q96SF7 HT-SELEX +MA0804 MA0804.1.TBX19 jaspar_vertebrates 1 TBX19 Brachyury-related factors O60806 HT-SELEX +MA0805 MA0805.1.TBX1 jaspar_vertebrates 1 TBX1 TBX1-related factors O43435 HT-SELEX +MA0806 MA0806.1.TBX4 jaspar_vertebrates 1 TBX4 TBX2-related factors P57082 HT-SELEX +MA0807 MA0807.1.TBX5 jaspar_vertebrates 1 TBX5 TBX2-related factors Q99593 HT-SELEX +MA0808 MA0808.1.TEAD3 jaspar_vertebrates 1 TEAD3 TEF-1-related factors Q99594 HT-SELEX +MA0809 MA0809.1.TEAD4 jaspar_vertebrates 1 TEAD4 TEF-1-related factors Q15561 HT-SELEX +MA0810 MA0810.1.TFAP2A(var.2) jaspar_vertebrates pwm TFAP2A(var.2) AP-2 P05549 HT-SELEX +MA0811 MA0811.1.TFAP2B jaspar_vertebrates 1 TFAP2B AP-2 Q92481 HT-SELEX +MA0812 MA0812.1.TFAP2B(var.2) jaspar_vertebrates pwm TFAP2B(var.2) AP-2 Q92481 HT-SELEX +MA0813 MA0813.1.TFAP2B(var.3) jaspar_vertebrates pwm TFAP2B(var.3) AP-2 Q92481 HT-SELEX +MA0814 MA0814.1.TFAP2C(var.2) jaspar_vertebrates pwm TFAP2C(var.2) AP-2 Q92754 HT-SELEX +MA0815 MA0815.1.TFAP2C(var.3) jaspar_vertebrates pwm TFAP2C(var.3) AP-2 Q92754 HT-SELEX +MA0816 MA0816.1.Ascl2 jaspar_vertebrates 1 Ascl2 MyoD / ASC-related factors O35885 HT-SELEX +MA0817 MA0817.1.BHLHE23 jaspar_vertebrates 1 BHLHE23 Tal-related factors Q8NDY6 HT-SELEX +MA0818 MA0818.1.BHLHE22 jaspar_vertebrates 1 BHLHE22 Tal-related factors Q8NFJ8 HT-SELEX +MA0819 MA0819.1.CLOCK jaspar_vertebrates 1 CLOCK PAS domain factors O15516 HT-SELEX +MA0820 MA0820.1.FIGLA jaspar_vertebrates 1 FIGLA Tal-related factors Q6QHK4 HT-SELEX +MA0821 MA0821.1.HES5 jaspar_vertebrates 1 HES5 Hairy-related factors Q5TA89 HT-SELEX +MA0822 MA0822.1.HES7 jaspar_vertebrates 1 HES7 Hairy-related factors Q9BYE0 HT-SELEX +MA0823 MA0823.1.HEY1 jaspar_vertebrates 1 HEY1 Hairy-related factors Q9Y5J3 HT-SELEX +MA0824 MA0824.1.ID4 jaspar_vertebrates 1 ID4 HLH domain only P47928 HT-SELEX +MA0825 MA0825.1.MNT jaspar_vertebrates 1 MNT bHLH-ZIP factors Q99583 HT-SELEX +MA0826 MA0826.1.OLIG1 jaspar_vertebrates 1 OLIG1 Tal-related factors Q8TAK6 HT-SELEX +MA0827 MA0827.1.OLIG3 jaspar_vertebrates 1 OLIG3 Tal-related factors Q7RTU3 HT-SELEX +MA0828 MA0828.1.SREBF2(var.2) jaspar_vertebrates pwm SREBF2(var.2) bHLH-ZIP factors Q12772 HT-SELEX +MA0829 MA0829.1.Srebf1(var.2) jaspar_vertebrates pwm Srebf1(var.2) bHLH-ZIP factors Q9WTN3 HT-SELEX +MA0830 MA0830.1.TCF4 jaspar_vertebrates 1 TCF4 E2A-related factors P15884 HT-SELEX +MA0831 MA0831.2.TFE3 jaspar_vertebrates 1 TFE3 bHLH-ZIP factors P19532 SMiLE-seq +MA0832 MA0832.1.Tcf21 jaspar_vertebrates 1 Tcf21 Tal-related factors O35437 HT-SELEX +MA0833 MA0833.1.ATF4 jaspar_vertebrates 1 ATF4 ATF-4-related factors P18848 HT-SELEX +MA0834 MA0834.1.ATF7 jaspar_vertebrates 1 ATF7 Jun-related factors P17544 HT-SELEX +MA0835 MA0835.1.BATF3 jaspar_vertebrates 1 BATF3 B-ATF-related factors Q9NR55 HT-SELEX +MA0836 MA0836.1.CEBPD jaspar_vertebrates 1 CEBPD C/EBP-related P49716 HT-SELEX +MA0837 MA0837.1.CEBPE jaspar_vertebrates 1 CEBPE C/EBP-related Q15744 HT-SELEX +MA0838 MA0838.1.CEBPG jaspar_vertebrates 1 CEBPG C/EBP-related P53567 HT-SELEX +MA0839 MA0839.1.CREB3L1 jaspar_vertebrates 1 CREB3L1 CREB-related factors Q96BA8 HT-SELEX +MA0840 MA0840.1.Creb5 jaspar_vertebrates 1 Creb5 CREB-related factors Q8K1L0 HT-SELEX +MA0841 MA0841.1.NFE2 jaspar_vertebrates 1 NFE2 Jun-related factors Q16621 HT-SELEX +MA0842 MA0842.1.NRL jaspar_vertebrates 1 NRL Maf-related factors P54845 HT-SELEX +MA0843 MA0843.1.TEF jaspar_vertebrates 1 TEF TEF-1-related factors Q10587 HT-SELEX +MA0844 MA0844.1.XBP1 jaspar_vertebrates 1 XBP1 XBP-1-related factors P17861 HT-SELEX +MA0845 MA0845.1.FOXB1 jaspar_vertebrates 1 FOXB1 Forkhead box (FOX) factors Q99853 HT-SELEX +MA0846 MA0846.1.FOXC2 jaspar_vertebrates 1 FOXC2 Forkhead box (FOX) factors Q99958 HT-SELEX +MA0847 MA0847.1.FOXD2 jaspar_vertebrates 1 FOXD2 Forkhead box (FOX) factors O60548 HT-SELEX +MA0848 MA0848.1.FOXO4 jaspar_vertebrates 1 FOXO4 Forkhead box (FOX) factors P98177 HT-SELEX +MA0849 MA0849.1.FOXO6 jaspar_vertebrates 1 FOXO6 Forkhead box (FOX) factors A8MYZ6 HT-SELEX +MA0850 MA0850.1.FOXP3 jaspar_vertebrates 1 FOXP3 Forkhead box (FOX) factors B7ZLG1 HT-SELEX +MA0851 MA0851.1.Foxj3 jaspar_vertebrates 1 Foxj3 Forkhead box (FOX) factors Q8BUR3 universal protein binding microarray (PBM) +MA0852 MA0852.2.FOXK1 jaspar_vertebrates 1 FOXK1 Forkhead box (FOX) factors P85037 ChIP-seq +MA0853 MA0853.1.Alx4 jaspar_vertebrates 1 Alx4 Paired-related HD factors O35137 universal protein binding microarray (PBM) +MA0854 MA0854.1.Alx1 jaspar_vertebrates 1 Alx1 Paired-related HD factors Q8C8B0 universal protein binding microarray (PBM) +MA0855 MA0855.1.RXRB jaspar_vertebrates 1 RXRB RXR-related receptors (NR2) P28702 HT-SELEX +MA0856 MA0856.1.RXRG jaspar_vertebrates 1 RXRG RXR-related receptors (NR2) P48443 HT-SELEX +MA0857 MA0857.1.Rarb jaspar_vertebrates 1 Rarb Thyroid hormone receptor-related factors (NR1) P22605 HT-SELEX +MA0858 MA0858.1.Rarb(var.2) jaspar_vertebrates pwm Rarb(var.2) Thyroid hormone receptor-related factors (NR1) P22605 HT-SELEX +MA0859 MA0859.1.Rarg jaspar_vertebrates 1 Rarg Thyroid hormone receptor-related factors (NR1) P18911 HT-SELEX +MA0860 MA0860.1.Rarg(var.2) jaspar_vertebrates pwm Rarg(var.2) Thyroid hormone receptor-related factors (NR1) P18911 HT-SELEX +MA0861 MA0861.1.TP73 jaspar_vertebrates 1 TP73 p53-related factors O15350 HT-SELEX +MA0862 MA0862.1.GMEB2 jaspar_vertebrates 1 GMEB2 GMEB Q9UKD1 HT-SELEX +MA0863 MA0863.1.MTF1 jaspar_vertebrates 1 MTF1 More than 3 adjacent zinc finger factors Q14872 HT-SELEX +MA0864 MA0864.1.E2F2 jaspar_vertebrates 1 E2F2 E2F-related factors Q14209 HT-SELEX +MA0865 MA0865.1.E2F8 jaspar_vertebrates 1 E2F8 E2F-related factors A0AVK6 HT-SELEX +MA0866 MA0866.1.SOX21 jaspar_vertebrates 1 SOX21 SOX-related factors Q9Y651 HT-SELEX +MA0867 MA0867.1.SOX4 jaspar_vertebrates 1 SOX4 SOX-related factors Q06945 HT-SELEX +MA0868 MA0868.1.SOX8 jaspar_vertebrates 1 SOX8 SOX-related factors P57073 HT-SELEX +MA0869 MA0869.1.Sox11 jaspar_vertebrates 1 Sox11 SOX-related factors Q7M6Y2 HT-SELEX +MA0870 MA0870.1.Sox1 jaspar_vertebrates 1 Sox1 SOX-related factors P53783 HT-SELEX +MA0871 MA0871.1.TFEC jaspar_vertebrates 1 TFEC bHLH-ZIP factors O14948 HT-SELEX +MA0872 MA0872.1.TFAP2A(var.3) jaspar_vertebrates pwm TFAP2A(var.3) AP-2 P05549 HT-SELEX +MA0873 MA0873.1.HOXD12 jaspar_vertebrates 1 HOXD12 HOX-related factors P35452 HT-SELEX +MA0874 MA0874.1.Arx jaspar_vertebrates 1 Arx Paired-related HD factors O35085 universal protein binding microarray (PBM) +MA0875 MA0875.1.BARX1 jaspar_vertebrates 1 BARX1 NK-related factors Q9HBU1 HT-SELEX +MA0876 MA0876.1.BSX jaspar_vertebrates 1 BSX NK-related factors Q3C1V8 HT-SELEX +MA0877 MA0877.1.Barhl1 jaspar_vertebrates 1 Barhl1 NK-related factors P63157 HT-SELEX +MA0878 MA0878.1.CDX1 jaspar_vertebrates 1 CDX1 HOX-related factors P47902 HT-SELEX +MA0879 MA0879.1.Dlx1 jaspar_vertebrates 1 Dlx1 NK-related factors Q64317 PBM +MA0880 MA0880.1.Dlx3 jaspar_vertebrates 1 Dlx3 NK-related factors Q64205 PBM +MA0881 MA0881.1.Dlx4 jaspar_vertebrates 1 Dlx4 NK-related factors P70436 PBM +MA0882 MA0882.1.DLX6 jaspar_vertebrates 1 DLX6 NK-related factors P56179 HT-SELEX +MA0883 MA0883.1.Dmbx1 jaspar_vertebrates 1 Dmbx1 Paired-related HD factors Q91ZK4 universal protein binding microarray (PBM) +MA0884 MA0884.1.DUXA jaspar_vertebrates 1 DUXA Paired-related HD factors A6NLW8 HT-SELEX +MA0885 MA0885.1.Dlx2 jaspar_vertebrates 1 Dlx2 NK-related factors P40764 HT-SELEX +MA0886 MA0886.1.EMX2 jaspar_vertebrates 1 EMX2 NK-related factors Q04743 HT-SELEX +MA0887 MA0887.1.EVX1 jaspar_vertebrates 1 EVX1 HOX-related factors P49640 HT-SELEX +MA0888 MA0888.1.EVX2 jaspar_vertebrates 1 EVX2 HOX-related factors Q03828 HT-SELEX +MA0889 MA0889.1.GBX1 jaspar_vertebrates 1 GBX1 HOX-related factors Q14549 HT-SELEX +MA0890 MA0890.1.GBX2 jaspar_vertebrates 1 GBX2 HOX-related factors P52951 HT-SELEX +MA0891 MA0891.1.GSC2 jaspar_vertebrates 1 GSC2 Paired-related HD factors O15499 HT-SELEX +MA0892 MA0892.1.GSX1 jaspar_vertebrates 1 GSX1 HOX-related factors A4IFQ3 HT-SELEX +MA0893 MA0893.1.GSX2 jaspar_vertebrates 1 GSX2 HOX-related factors Q9BZM3 HT-SELEX +MA0894 MA0894.1.HESX1 jaspar_vertebrates 1 HESX1 Paired-related HD factors Q9UBX0 HT-SELEX +MA0895 MA0895.1.HMBOX1 jaspar_vertebrates 1 HMBOX1 POU domain factors Q6NT76 HT-SELEX +MA0896 MA0896.1.Hmx1 jaspar_vertebrates 1 Hmx1 NK-related factors O70218 universal protein binding microarray (PBM) +MA0897 MA0897.1.Hmx2 jaspar_vertebrates 1 Hmx2 NK-related factors P43687 universal protein binding microarray (PBM) +MA0898 MA0898.1.Hmx3 jaspar_vertebrates 1 Hmx3 NK-related factors P42581 universal protein binding microarray (PBM) +MA0899 MA0899.1.HOXA10 jaspar_vertebrates 1 HOXA10 HOX-related factors P31260 HT-SELEX +MA0900 MA0900.1.HOXA2 jaspar_vertebrates 1 HOXA2 HOX-related factors O43364 HT-SELEX +MA0901 MA0901.1.HOXB13 jaspar_vertebrates 1 HOXB13 HOX-related factors Q92826 HT-SELEX +MA0902 MA0902.1.HOXB2 jaspar_vertebrates 1 HOXB2 HOX-related factors P14652 HT-SELEX +MA0903 MA0903.1.HOXB3 jaspar_vertebrates 1 HOXB3 HOX-related factors P14651 HT-SELEX +MA0904 MA0904.1.Hoxb5 jaspar_vertebrates 1 Hoxb5 HOX-related factors P09079 universal protein binding microarray (PBM) +MA0905 MA0905.1.HOXC10 jaspar_vertebrates 1 HOXC10 HOX-related factors Q9NYD6 HT-SELEX +MA0906 MA0906.1.HOXC12 jaspar_vertebrates 1 HOXC12 HOX-related factors P31275 HT-SELEX +MA0907 MA0907.1.HOXC13 jaspar_vertebrates 1 HOXC13 HOX-related factors P31276 HT-SELEX +MA0908 MA0908.1.HOXD11 jaspar_vertebrates 1 HOXD11 HOX-related factors P31277 HT-SELEX +MA0909 MA0909.1.HOXD13 jaspar_vertebrates 1 HOXD13 HOX-related factors P35453 HT-SELEX +MA0910 MA0910.1.Hoxd8 jaspar_vertebrates 1 Hoxd8 HOX-related factors P23463 universal protein binding microarray (PBM) +MA0911 MA0911.1.Hoxa11 jaspar_vertebrates 1 Hoxa11 HOX-related factors P31270 HT-SELEX +MA0912 MA0912.1.Hoxd3 jaspar_vertebrates 1 Hoxd3 HOX-related factors P09027 universal protein binding microarray (PBM) +MA0913 MA0913.1.Hoxd9 jaspar_vertebrates 1 Hoxd9 HOX-related factors P28357 HT-SELEX +MA0914 MA0914.1.ISL2 jaspar_vertebrates 1 ISL2 HD-LIM factors Q96A47 HT-SELEX +MA1099 MA1099.1.Hes1 jaspar_vertebrates 1 Hes1 Hairy-related factors P35428 PBM +MA1100 MA1100.1.ASCL1 jaspar_vertebrates 1 ASCL1 MyoD / ASC-related factors P50553 ChIP-seq +MA1101 MA1101.1.BACH2 jaspar_vertebrates 1 BACH2 Jun-related factors Q9BYV9 ChIP-seq +MA1102 MA1102.1.CTCFL jaspar_vertebrates 1 CTCFL More than 3 adjacent zinc finger factors Q8NI51 ChIP-seq +MA1103 MA1103.1.FOXK2 jaspar_vertebrates 1 FOXK2 Forkhead box (FOX) factors Q01167 ChIP-seq +MA1104 MA1104.1.GATA6 jaspar_vertebrates 1 GATA6 GATA-type zinc fingers Q92908 ChIP-seq +MA1105 MA1105.1.GRHL2 jaspar_vertebrates 1 GRHL2 Grainyhead-related factors Q6ISB3 ChIP-seq +MA1106 MA1106.1.HIF1A jaspar_vertebrates 1 HIF1A PAS domain factors Q16665 ChIP-seq +MA1107 MA1107.1.KLF9 jaspar_vertebrates 1 KLF9 Three-zinc finger Kruppel-related factors Q13886 ChIP-seq +MA1108 MA1108.1.MXI1 jaspar_vertebrates 1 MXI1 bHLH-ZIP factors P50539 ChIP-seq +MA1109 MA1109.1.NEUROD1 jaspar_vertebrates 1 NEUROD1 Tal-related factors Q13562 ChIP-seq +MA1110 MA1110.1.NR1H4 jaspar_vertebrates 1 NR1H4 Thyroid hormone receptor-related factors (NR1) Q96RI1 ChIP-seq +MA1111 MA1111.1.NR2F2 jaspar_vertebrates 1 NR2F2 RXR-related receptors (NR2) P24468 ChIP-seq +MA1112 MA1112.1.NR4A1 jaspar_vertebrates 1 NR4A1 NGFI-B-related receptors (NR4) P22736 ChIP-seq +MA1113 MA1113.1.PBX2 jaspar_vertebrates 1 PBX2 TALE-type homeo domain factors P40425 ChIP-seq +MA1114 MA1114.1.PBX3 jaspar_vertebrates 1 PBX3 TALE-type homeo domain factors P40426 ChIP-seq +MA1115 MA1115.1.POU5F1 jaspar_vertebrates 1 POU5F1 POU domain factors Q01860 ChIP-seq +MA1116 MA1116.1.RBPJ jaspar_vertebrates 1 RBPJ CSL-related factors Q06330 ChIP-seq +MA1117 MA1117.1.RELB jaspar_vertebrates 1 RELB NF-kappaB-related factors Q01201 ChIP-seq +MA1118 MA1118.1.SIX1 jaspar_vertebrates 1 SIX1 HD-SINE factors Q15475 ChIP-seq +MA1119 MA1119.1.SIX2 jaspar_vertebrates 1 SIX2 HD-SINE factors Q9NPC8 ChIP-seq +MA1120 MA1120.1.SOX13 jaspar_vertebrates 1 SOX13 SOX-related factors Q9UN79 ChIP-seq +MA1121 MA1121.1.TEAD2 jaspar_vertebrates 1 TEAD2 TEF-1-related factors Q15562 ChIP-seq +MA1122 MA1122.1.TFDP1 jaspar_vertebrates 1 TFDP1 E2F-related factors Q14186 ChIP-seq +MA1123 MA1123.1.TWIST1 jaspar_vertebrates 1 TWIST1 Tal-related factors Q15672 ChIP-seq +MA1124 MA1124.1.ZNF24 jaspar_vertebrates 1 ZNF24 More than 3 adjacent zinc finger factors P17028 ChIP-seq +MA1125 MA1125.1.ZNF384 jaspar_vertebrates 1 ZNF384 More than 3 adjacent zinc finger factors Q8TF68 ChIP-seq +MA1126 MA1126.1.FOS::JUN(var.2) jaspar_vertebrates pwm FOS+JUN(var.2) Fos-related factors;Jun-related factors P01100;P05412 SMiLE-seq +MA1127 MA1127.1.FOSB::JUN jaspar_vertebrates 1 FOSB+JUN Fos-related factors;Jun-related factors P53539;P05412 SMiLE-seq +MA1128 MA1128.1.FOSL1::JUN jaspar_vertebrates 1 FOSL1+JUN Fos-related factors;Jun-related factors P15407;P05412 SMiLE-seq +MA1129 MA1129.1.FOSL1::JUN(var.2) jaspar_vertebrates pwm FOSL1+JUN(var.2) Fos-related factors;Jun-related factors P15407;P05412 SMiLE-seq +MA1130 MA1130.1.FOSL2::JUN jaspar_vertebrates 1 FOSL2+JUN Fos-related factors;Jun-related factors P15408;P05412 SMiLE-seq +MA1131 MA1131.1.FOSL2::JUN(var.2) jaspar_vertebrates pwm FOSL2+JUN(var.2) Fos-related factors;Jun-related factors P15408;P05412 SMiLE-seq +MA1132 MA1132.1.JUN::JUNB jaspar_vertebrates 1 JUN+JUNB Jun-related factors P05412;P17275 SMiLE-seq +MA1133 MA1133.1.JUN::JUNB(var.2) jaspar_vertebrates pwm JUN+JUNB(var.2) Jun-related factors P05412;P17275 SMiLE-seq +MA1134 MA1134.1.FOS::JUNB jaspar_vertebrates 1 FOS+JUNB Fos-related factors;Jun-related factors P01100;P17275 SMiLE-seq +MA1135 MA1135.1.FOSB::JUNB jaspar_vertebrates 1 FOSB+JUNB Fos-related factors;Jun-related factors P53539;P17275 SMiLE-seq +MA1136 MA1136.1.FOSB::JUNB(var.2) jaspar_vertebrates pwm FOSB+JUNB(var.2) Fos-related factors;Jun-related factors P53539;P17275 SMiLE-seq +MA1137 MA1137.1.FOSL1::JUNB jaspar_vertebrates 1 FOSL1+JUNB Fos-related factors;Jun-related factors P15407;P17275 SMiLE-seq +MA1138 MA1138.1.FOSL2::JUNB jaspar_vertebrates 1 FOSL2+JUNB Fos-related factors;Jun-related factors P15408;P17275 SMiLE-seq +MA1139 MA1139.1.FOSL2::JUNB(var.2) jaspar_vertebrates pwm FOSL2+JUNB(var.2) Fos-related factors;Jun-related factors P15408;P17275 SMiLE-seq +MA1140 MA1140.1.JUNB(var.2) jaspar_vertebrates pwm JUNB(var.2) Jun-related factors P17275 SMiLE-seq +MA1141 MA1141.1.FOS::JUND jaspar_vertebrates 1 FOS+JUND Fos-related factors;Jun-related factors P01100;P17535 SMiLE-seq +MA1142 MA1142.1.FOSL1::JUND jaspar_vertebrates 1 FOSL1+JUND Fos-related factors;Jun-related factors P15407;P17535 SMiLE-seq +MA1143 MA1143.1.FOSL1::JUND(var.2) jaspar_vertebrates pwm FOSL1+JUND(var.2) Fos-related factors;Jun-related factors P15407;P17535 SMiLE-seq +MA1144 MA1144.1.FOSL2::JUND jaspar_vertebrates 1 FOSL2+JUND Fos-related factors;Jun-related factors P15408;P17535 SMiLE-seq +MA1145 MA1145.1.FOSL2::JUND(var.2) jaspar_vertebrates pwm FOSL2+JUND(var.2) Fos-related factors;Jun-related factors P15408;P17535 SMiLE-seq +MA1146 MA1146.1.NR1A4::RXRA jaspar_vertebrates 1 NR1A4+RXRA RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1) Q96RI1;P19793 SMiLE-seq +MA1147 MA1147.1.NR4A2::RXRA jaspar_vertebrates 1 NR4A2+RXRA NGFI-B-related receptors (NR4);RXR-related receptors (NR2) P43354;P19793 SMiLE-seq +MA1148 MA1148.1.PPARA::RXRA jaspar_vertebrates 1 PPARA+RXRA RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1) Q07869;P19793 SMiLE-seq +MA1149 MA1149.1.RARA::RXRG jaspar_vertebrates 1 RARA+RXRG RXR-related receptors (NR2);Thyroid hormone receptor-related factors (NR1) P10276;P48443 SMiLE-seq +MA1150 MA1150.1.RORB jaspar_vertebrates 1 RORB Thyroid hormone receptor-related factors (NR1) Q92753 SMiLE-seq +MA1151 MA1151.1.RORC jaspar_vertebrates 1 RORC Thyroid hormone receptor-related factors (NR1) P51449 SMiLE-seq +MA1152 MA1152.1.SOX15 jaspar_vertebrates 1 SOX15 SOX-related factors O60248 SMiLE-seq +MA1153 MA1153.1.Smad4 jaspar_vertebrates 1 Smad4 SMAD factors P97471 SMiLE-seq +MA1154 MA1154.1.ZNF282 jaspar_vertebrates 1 ZNF282 More than 3 adjacent zinc finger factors Q9UDV7 HT-SELEX +MA1155 MA1155.1.ZSCAN4 jaspar_vertebrates 1 ZSCAN4 More than 3 adjacent zinc finger factors Q8NAM6 HT-SELEX +MA1418 MA1418.1.IRF3 jaspar_vertebrates 1 IRF3 Interferon-regulatory factors Q14653 HT-SELEX +MA1419 MA1419.1.IRF4 jaspar_vertebrates 1 IRF4 Interferon-regulatory factors Q15306 HT-SELEX +MA1420 MA1420.1.IRF5 jaspar_vertebrates 1 IRF5 Interferon-regulatory factors Q13568 HT-SELEX +MA1421 MA1421.1.TCF7L1 jaspar_vertebrates 1 TCF7L1 TCF-7-related factors Q9HCS4 HT-SELEX diff --git a/data/motifs/make_annotation.sh b/data/motifs/make_annotation.sh new file mode 100755 index 000000000..b6c6dd543 --- /dev/null +++ b/data/motifs/make_annotation.sh @@ -0,0 +1,45 @@ +#!/bin/bash + +echo "### Jaspar Vertebrates 7 (2018) Annotation ###" + +echo "Downloading SQL dump.." + +wget -c http://jaspar.genereg.net/download/database/JASPAR2018.sql.tar.gz --user-agent="Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8.1.6) Gecko/20070725 Firefox/2.0.0.6" + +echo "Un-tarring it (it isn't actually gzipped).." +tar xvf JASPAR2018.sql.tar.gz || { echo "failed extracting archive"; exit 1; } + +echo "Converting to SQLite3.." + +chmod 755 mysql2sqlite +./mysql2sqlite JASPAR2018.sql | sqlite3 jaspar.db || { echo "failed converting DB to SQLite3"; exit 1; } + +echo "Exporting annotation.." + +sqlite3 jaspar.db '.read jaspar_anno_query.sql' | sed -e 's/"//g' > jaspar_anno.csv || { echo "failed exporting sql table"; exit 1; } + +echo "Cleaning up.." +rm ._JASPAR2018.sql JASPAR2018.sql jaspar.db JASPAR2018.sql.tar.gz + +echo "Done" + +echo + +echo "### Hocomoco Full v11 Annotation ###" + +echo "Downloading.." + +wget -c http://hocomoco11.autosome.ru/final_bundle/hocomoco11/full/HUMAN/mono/HOCOMOCOv11_full_annotation_HUMAN_mono.tsv + +echo "Converting to CSV.." + +echo "TfName,GeneName,Family,UniProt,Source" > hocomoco_anno.csv +tail -n+2 HOCOMOCOv11_full_annotation_HUMAN_mono.tsv | awk -F"\t" '{print $1","$2","$14","$19","$8","}' | sed -e 's/{.*}//g' >> hocomoco_anno.csv + +echo "Cleaning up.." +rm HOCOMOCOv11_full_annotation_HUMAN_mono.tsv + +echo "Done" + +echo "Re-creating Mtf file.." +python createMtf.py diff --git a/data/motifs/mysql2sqlite b/data/motifs/mysql2sqlite new file mode 100755 index 000000000..4e1c9f851 --- /dev/null +++ b/data/motifs/mysql2sqlite @@ -0,0 +1,184 @@ +#!/usr/bin/awk -f + +# Authors: @esperlu, @artemyk, @gkuenning, @dumblob + +BEGIN { + if (ARGC != 2) { + printf "%s\n%s\n", + "USAGE: mysql2sqlite dump_mysql.sql > dump_sqlite3.sql", + " file name - (dash) is not supported, because - means stdin" > "/dev/stderr" + err=1 # do not execute the END rule + exit 1 + } + FS=",$" + print "PRAGMA synchronous = OFF;" + print "PRAGMA journal_mode = MEMORY;" + print "BEGIN TRANSACTION;" +} + +# CREATE TRIGGER statements have funny commenting. Remember we are in trigger. +/^\/\*.*(CREATE.*TRIGGER|create.*trigger)/ { + gsub( /^.*(TRIGGER|trigger)/, "CREATE TRIGGER" ) + print + inTrigger = 1 + next +} +# The end of CREATE TRIGGER has a stray comment terminator +/(END|end) \*\/;;/ { gsub( /\*\//, "" ); print; inTrigger = 0; next } +# The rest of triggers just get passed through +inTrigger != 0 { print; next } + +# CREATE VIEW looks like a TABLE in comments +/^\/\*.*(CREATE.*TABLE|create.*table)/ { + inView = 1 + next +} +# The end of CREATE VIEW +/^(\).*(ENGINE|engine).*\*\/;)/ { + inView = 0; + next +} +# The rest of view just get passed through +inView != 0 { next } + +# Skip other comments +/^\/\*/ { next } + +# Print all `INSERT` lines. The single quotes are protected by another single quote. +( /^ *\(/ && /\) *[,;] *$/ ) || /^(INSERT|insert)/ { + prev = ""; + gsub( /\\\047/, "\047\047" ) # single quote + gsub( /\\\047\047,/, "\\\047," ) + gsub( /\\n/, "\n" ) + gsub( /\\r/, "\r" ) + gsub( /\\"/, "\"" ) + gsub( /\\\\/, "\\" ) + gsub( /\\\032/, "\032" ) # substitute + # sqlite3 is limited to 16 significant digits of precision + while ( match( $0, /0x[0-9a-fA-F]{17}/ ) ) { + hexIssue = 1 + sub( /0x[0-9a-fA-F]+/, substr( $0, RSTART, RLENGTH-1 ), $0 ) + } + print + next +} + +# CREATE DATABASE is not supported +/^(CREATE.*DATABASE|create.*database)/ { next } + +# Print the `CREATE` line as is and capture the table name. +/^(CREATE|create)/ { + if ( $0 ~ /IF NOT EXISTS|if not exists/ || $0 ~ /TEMPORARY|temporary/ ){ + caseIssue = 1 + } + if ( match( $0, /`[^`]+/ ) ) { + tableName = substr( $0, RSTART+1, RLENGTH-1 ) + } + aInc = 0 + prev = "" + firstInTable = 1 + print + next +} + +# Replace `FULLTEXT KEY` (probably other `XXXXX KEY`) +/^ (FULLTEXT KEY|fulltext key)/ { gsub( /.+(KEY|key)/, " KEY" ) } + +# Get rid of field lengths in KEY lines +/ (PRIMARY |primary )?(KEY|key)/ { gsub( /\([0-9]+\)/, "" ) } + +aInc == 1 && /PRIMARY KEY|primary key/ { next } + +# Replace COLLATE xxx_xxxx_xx statements with COLLATE BINARY +/ (COLLATE|collate) [a-z0-9_]*/ { gsub( /(COLLATE|collate) [a-z0-9_]*/, "COLLATE BINARY" ) } + +# Print all fields definition lines except the `KEY` lines. +/^ / && !/^( (KEY|key)|\);)/ { + if ( match( $0, /[^"`]AUTO_INCREMENT|auto_increment[^"`]/)) { + aInc = 1; + gsub( /AUTO_INCREMENT|auto_increment/, "PRIMARY KEY AUTOINCREMENT" ) + } + gsub( /(UNIQUE KEY|unique key) `.*` /, "UNIQUE " ) + gsub( /(CHARACTER SET|character set) [^ ]+[ ,]/, "" ) + gsub( /DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP|default current_timestamp on update current_timestamp/, "" ) + gsub( /(COLLATE|collate) [^ ]+ /, "" ) + gsub( /(ENUM|enum)[^)]+\)/, "text " ) + gsub( /(SET|set)\([^)]+\)/, "text " ) + gsub( /UNSIGNED|unsigned/, "" ) + gsub( /` [^ ]*(INT|int)[^ ]*/, "` integer" ) + # field comments are not supported + gsub( / (COMMENT|comment).+$/, "" ) + # Get commas off end of line + gsub( /,.?$/, "") + if ( prev ){ + if ( firstInTable ){ + print prev + firstInTable = 0 + } + else print "," prev + } + else { + # FIXME check if this is correct in all cases + if ( match( $1, + /(CONSTRAINT|constraint) \".*\" (FOREIGN KEY|foreign key)/ ) ) + print "," + } + prev = $1 +} + +/ ENGINE| engine/ { + if (prev) { + if (firstInTable) { + print prev + firstInTable = 0 + } + else print "," prev + # else print prev + } + prev="" + print ");" + next +} +# `KEY` lines are extracted from the `CREATE` block and stored in array for later print +# in a separate `CREATE KEY` command. The index name is prefixed by the table name to +# avoid a sqlite error for duplicate index name. +/^( (KEY|key)|\);)/ { + if (prev) { + if (firstInTable) { + print prev + firstInTable = 0 + } + else print "," prev + # else print prev + } + prev = "" + if ($0 == ");"){ + print + } else { + if ( match( $0, /`[^`]+/ ) ) { + indexName = substr( $0, RSTART+1, RLENGTH-1 ) + } + if ( match( $0, /\([^()]+/ ) ) { + indexKey = substr( $0, RSTART+1, RLENGTH-1 ) + } + # idx_ prefix to avoid name clashes (they really happen!) + key[tableName]=key[tableName] "CREATE INDEX \"idx_" tableName "_" indexName "\" ON \"" tableName "\" (" indexKey ");\n" + } +} + +END { + if (err) { exit 1}; + # print all `KEY` creation lines. + for (table in key) printf key[table] + + print "END TRANSACTION;" + + if ( hexIssue ){ + print "WARN Hexadecimal numbers longer than 16 characters has been trimmed." | "cat >&2" + } + if ( caseIssue ){ + print "WARN Pure sqlite identifiers are case insensitive (even if quoted\n" \ + " or if ASCII) and doesnt cross-check TABLE and TEMPORARY TABLE\n" \ + " identifiers. Thus expect errors like \"table T has no column named F\"." | "cat >&2" + } +} diff --git a/data/motifs/transfac_public.mtf b/data/motifs/transfac_public.mtf index d3b7d307b..9a2742d93 100644 --- a/data/motifs/transfac_public.mtf +++ b/data/motifs/transfac_public.mtf @@ -1,2 +1,2 @@ -M00244 M00244_EGR4 transfac_public 1 EGR4 . -M00245 M00245_EGR3 transfac_public 1 EGR3 . +M00244 M00244_EGR4 transfac_public 1 EGR4 . . . . +M00245 M00245_EGR3 transfac_public 1 EGR3 . . . . diff --git a/data/motifs/uniprobe_primary.mtf b/data/motifs/uniprobe_primary.mtf index 773ffb4b7..58a5167b2 100644 --- a/data/motifs/uniprobe_primary.mtf +++ b/data/motifs/uniprobe_primary.mtf @@ -1,268 +1,268 @@ -UP00000 UP00000_1_Smad3_primary uniprobe_primary 1 Smad3 . -UP00001 UP00001_1_E2F2_primary uniprobe_primary 1 E2F2 . -UP00002 UP00002_1_Sp4_primary uniprobe_primary 1 Sp4 . -UP00003 UP00003_1_E2F3_primary uniprobe_primary 1 E2F3 . -UP00004 UP00004_1_Sox14_primary uniprobe_primary 1 Sox14 . -UP00005 UP00005_1_Tcfap2a_primary uniprobe_primary 1 Tcfap2a . -UP00006 UP00006_1_Zic3_primary uniprobe_primary 1 Zic3 . -UP00007 UP00007_1_Egr1_primary uniprobe_primary 1 Egr1 . -UP00008 UP00008_1_Six6_primary uniprobe_primary 1 Six6 . -UP00009 UP00009_1_Nr2f2_primary uniprobe_primary 1 Nr2f2 . -UP00010 UP00010_1_Tcfap2b_primary uniprobe_primary 1 Tcfap2b . -UP00011 UP00011_1_Irf6_primary uniprobe_primary 1 Irf6 . -UP00012 UP00012_1_Bbx_primary uniprobe_primary 1 Bbx . -UP00014 UP00014_1_Sox17_primary uniprobe_primary 1 Sox17 . -UP00015 UP00015_1_Ehf_primary uniprobe_primary 1 Ehf . -UP00016 UP00016_1_Sry_primary uniprobe_primary 1 Sry . -UP00017 UP00017_1_Nkx3-1_primary uniprobe_primary 1 Nkx3-1 . -UP00018 UP00018_1_Irf4_primary uniprobe_primary 1 Irf4 . -UP00019 UP00019_1_Zbtb12_primary uniprobe_primary 1 Zbtb12 . -UP00020 UP00020_1_Atf1_primary uniprobe_primary 1 Atf1 . -UP00021 UP00021_1_Zfp281_primary uniprobe_primary 1 Zfp281 . -UP00022 UP00022_1_Zfp740_primary uniprobe_primary 1 Zfp740 . -UP00023 UP00023_1_Sox30_primary uniprobe_primary 1 Sox30 . -UP00024 UP00024_1_Glis2_primary uniprobe_primary 1 Glis2 . -UP00025 UP00025_1_Foxk1_primary uniprobe_primary 1 Foxk1 . -UP00026 UP00026_1_Zscan4_primary uniprobe_primary 1 Zscan4 . -UP00027 UP00027_1_Osr1_primary uniprobe_primary 1 Osr1 . -UP00028 UP00028_1_Tcfap2e_primary uniprobe_primary 1 Tcfap2e . -UP00029 UP00029_1_Tbp_primary uniprobe_primary 1 Tbp . -UP00030 UP00030_1_Sox11_primary uniprobe_primary 1 Sox11 . -UP00031 UP00031_1_Zbtb3_primary uniprobe_primary 1 Zbtb3 . -UP00032 UP00032_1_Gata3_primary uniprobe_primary 1 Gata3 . -UP00033 UP00033_1_Zfp410_primary uniprobe_primary 1 Zfp410 . -UP00034 UP00034_1_Sox7_primary uniprobe_primary 1 Sox7 . -UP00035 UP00035_1_Hic1_primary uniprobe_primary 1 Hic1 . -UP00036 UP00036_1_Myf6_primary uniprobe_primary 1 Myf6 . -UP00037 UP00037_1_Zfp105_primary uniprobe_primary 1 Zfp105 . -UP00039 UP00039_1_Foxj3_primary uniprobe_primary 1 Foxj3 . -UP00040 UP00040_1_Irf5_primary uniprobe_primary 1 Irf5 . -UP00041 UP00041_1_Foxj1_primary uniprobe_primary 1 Foxj1 . -UP00042 UP00042_1_Gm397_primary uniprobe_primary 1 Gm397 . -UP00043 UP00043_1_Bcl6b_primary uniprobe_primary 1 Bcl6b . -UP00044 UP00044_1_Mafk_primary uniprobe_primary 1 Mafk . -UP00045 UP00045_1_Mafb_primary uniprobe_primary 1 Mafb . -UP00046 UP00046_1_Tcfe2a_primary uniprobe_primary 1 Tcfe2a . -UP00047 UP00047_1_Zbtb7b_primary uniprobe_primary 1 Zbtb7b . -UP00048 UP00048_1_Rara_primary uniprobe_primary 1 Rara . -UP00049 UP00049_1_Sp100_primary uniprobe_primary 1 Sp100 . -UP00050 UP00050_1_Bhlhb2_primary uniprobe_primary 1 Bhlhb2 . -UP00051 UP00051_1_Sox8_primary uniprobe_primary 1 Sox8 . -UP00052 UP00052_1_Osr2_primary uniprobe_primary 1 Osr2 . -UP00053 UP00053_1_Rxra_primary uniprobe_primary 1 Rxra . -UP00054 UP00054_1_Tcf7_primary uniprobe_primary 1 Tcf7 . -UP00055 UP00055_1_Hbp1_primary uniprobe_primary 1 Hbp1 . -UP00056 UP00056_1_Rfx4_primary uniprobe_primary 1 Rfx4 . -UP00057 UP00057_1_Zic2_primary uniprobe_primary 1 Zic2 . -UP00058 UP00058_1_Tcf3_primary uniprobe_primary 1 Tcf3 . -UP00059 UP00059_1_Arid5a_primary uniprobe_primary 1 Arid5a . -UP00060 UP00060_1_Max_primary uniprobe_primary 1 Max . -UP00061 UP00061_1_Foxl1_primary uniprobe_primary 1 Foxl1 . -UP00062 UP00062_1_Sox4_primary uniprobe_primary 1 Sox4 . -UP00064 UP00064_1_Sox18_primary uniprobe_primary 1 Sox18 . -UP00065 UP00065_1_Zfp161_primary uniprobe_primary 1 Zfp161 . -UP00066 UP00066_1_Hnf4a_primary uniprobe_primary 1 Hnf4a . -UP00067 UP00067_1_Lef1_primary uniprobe_primary 1 Lef1 . -UP00068 UP00068_1_Eomes_primary uniprobe_primary 1 Eomes . -UP00069 UP00069_1_Sox1_primary uniprobe_primary 1 Sox1 . -UP00070 UP00070_1_Gcm1_primary uniprobe_primary 1 Gcm1 . -UP00071 UP00071_1_Sox21_primary uniprobe_primary 1 Sox21 . -UP00072 UP00072_1_IRC900814_primary uniprobe_primary 1 IRC900814 . -UP00073 UP00073_1_Foxa2_primary uniprobe_primary 1 Foxa2 . -UP00074 UP00074_1_Isgf3g_primary uniprobe_primary 1 Isgf3g . -UP00075 UP00075_1_Sox15_primary uniprobe_primary 1 Sox15 . -UP00076 UP00076_1_Rfxdc2_primary uniprobe_primary 1 Rfxdc2 . -UP00077 UP00077_1_Srf_primary uniprobe_primary 1 Srf . -UP00078 UP00078_1_Arid3a_primary uniprobe_primary 1 Arid3a . -UP00079 UP00079_1_Esrra_primary uniprobe_primary 1 Esrra . -UP00080 UP00080_1_Gata5_primary uniprobe_primary 1 Gata5 . -UP00081 UP00081_1_Mybl1_primary uniprobe_primary 1 Mybl1 . -UP00082 UP00082_1_Zfp187_primary uniprobe_primary 1 Zfp187 . -UP00083 UP00083_1_Tcf7l2_primary uniprobe_primary 1 Tcf7l2 . -UP00084 UP00084_1_Gmeb1_primary uniprobe_primary 1 Gmeb1 . -UP00085 UP00085_1_Sfpi1_primary uniprobe_primary 1 Sfpi1 . -UP00086 UP00086_1_Irf3_primary uniprobe_primary 1 Irf3 . -UP00087 UP00087_1_Tcfap2c_primary uniprobe_primary 1 Tcfap2c . -UP00088 UP00088_1_Plagl1_primary uniprobe_primary 1 Plagl1 . -UP00089 UP00089_1_Tcf1_primary uniprobe_primary 1 Tcf1 . -UP00091 UP00091_1_Sox5_primary uniprobe_primary 1 Sox5 . -UP00092 UP00092_1_Myb_primary uniprobe_primary 1 Myb . -UP00093 UP00093_1_Klf7_primary uniprobe_primary 1 Klf7 . -UP00094 UP00094_1_Zfp128_primary uniprobe_primary 1 Zfp128 . -UP00095 UP00095_1_Zfp691_primary uniprobe_primary 1 Zfp691 . -UP00096 UP00096_1_Sox13_primary uniprobe_primary 1 Sox13 . -UP00097 UP00097_1_Mtf1_primary uniprobe_primary 1 Mtf1 . -UP00098 UP00098_1_Rfx3_primary uniprobe_primary 1 Rfx3 . -UP00099 UP00099_1_Ascl2_primary uniprobe_primary 1 Ascl2 . -UP00100 UP00100_1_Gata6_primary uniprobe_primary 1 Gata6 . -UP00101 UP00101_1_Sox12_primary uniprobe_primary 1 Sox12 . -UP00102 UP00102_1_Zic1_primary uniprobe_primary 1 Zic1 . -UP00103 UP00103_1_Jundm2_primary uniprobe_primary 1 Jundm2 . -UP00104 UP00104_1_Hmx1_3423.1 uniprobe_primary 1 Hmx1 . -UP00105 UP00105_1_Pou3f4_3773.1 uniprobe_primary 1 Pou3f4 . -UP00106 UP00106_1_Vax2_3500.1 uniprobe_primary 1 Vax2 . -UP00107 UP00107_1_Nkx2-4_3074.1 uniprobe_primary 1 Nkx2-4 . -UP00108 UP00108_1_Alx3_3418.2 uniprobe_primary 1 Alx3 . -UP00109 UP00109_1_Obox6_3440.2 uniprobe_primary 1 Obox6 . -UP00110 UP00110_1_Dlx4_3488.2 uniprobe_primary 1 Dlx4 . -UP00111 UP00111_1_Dmbx1_2277.1 uniprobe_primary 1 Dmbx1 . -UP00112 UP00112_1_Gsc_2327.3 uniprobe_primary 1 Gsc . -UP00113 UP00113_1_Hoxc4_3491.1 uniprobe_primary 1 Hoxc4 . -UP00114 UP00114_1_Homez_1063.2 uniprobe_primary 1 Homez . -UP00115 UP00115_1_Lhx2_0953.2 uniprobe_primary 1 Lhx2 . -UP00116 UP00116_1_Rhox6_4251.1 uniprobe_primary 1 Rhox6 . -UP00117 UP00117_1_Hoxd11_3873.1 uniprobe_primary 1 Hoxd11 . -UP00118 UP00118_1_Pou4f3_2791.1 uniprobe_primary 1 Pou4f3 . -UP00119 UP00119_1_Nkx2-9_3082.1 uniprobe_primary 1 Nkx2-9 . -UP00120 UP00120_1_Lbx2_3869.2 uniprobe_primary 1 Lbx2 . -UP00121 UP00121_1_Hoxd10_2368.2 uniprobe_primary 1 Hoxd10 . -UP00122 UP00122_1_Tgif1_2342.2 uniprobe_primary 1 Tgif1 . -UP00123 UP00123_1_Hlxb9_3422.1 uniprobe_primary 1 Hlxb9 . -UP00124 UP00124_1_Ipf1_3815.1 uniprobe_primary 1 Ipf1 . -UP00125 UP00125_1_Pitx2_2274.3 uniprobe_primary 1 Pitx2 . -UP00126 UP00126_1_Dlx2_2273.2 uniprobe_primary 1 Dlx2 . -UP00127 UP00127_1_Gsh2_3990.2 uniprobe_primary 1 Gsh2 . -UP00128 UP00128_1_Pou3f2_2824.1 uniprobe_primary 1 Pou3f2 . -UP00129 UP00129_1_Pou3f1_3819.1 uniprobe_primary 1 Pou3f1 . -UP00130 UP00130_1_Lhx3_3431.1 uniprobe_primary 1 Lhx3 . -UP00131 UP00131_1_Gbx2_3110.1 uniprobe_primary 1 Gbx2 . -UP00132 UP00132_1_Evx2_2645.3 uniprobe_primary 1 Evx2 . -UP00133 UP00133_1_Cdx2_4272.1 uniprobe_primary 1 Cdx2 . -UP00134 UP00134_1_Hoxb13_3479.1 uniprobe_primary 1 Hoxb13 . -UP00135 UP00135_1_Hoxc12_3480.1 uniprobe_primary 1 Hoxc12 . -UP00136 UP00136_1_Prrx2_3072.1 uniprobe_primary 1 Prrx2 . -UP00137 UP00137_1_Hoxb3_1720.2 uniprobe_primary 1 Hoxb3 . -UP00138 UP00138_1_Bsx_3483.2 uniprobe_primary 1 Bsx . -UP00139 UP00139_1_Nkx1-2_3214.1 uniprobe_primary 1 Nkx1-2 . -UP00140 UP00140_1_Hoxd1_3448.1 uniprobe_primary 1 Hoxd1 . -UP00141 UP00141_1_Vsx1_1728.1 uniprobe_primary 1 Vsx1 . -UP00142 UP00142_1_Uncx4.1_2281.2 uniprobe_primary 1 Uncx4 . -UP00143 UP00143_1_Dobox5_3493.1 uniprobe_primary 1 Dobox5 . -UP00144 UP00144_1_Hoxb4_2627.1 uniprobe_primary 1 Hoxb4 . -UP00145 UP00145_1_Barhl2_3868.1 uniprobe_primary 1 Barhl2 . -UP00146 UP00146_1_Pou6f1_1731.2 uniprobe_primary 1 Pou6f1 . -UP00147 UP00147_1_Nkx2-6_3437.1 uniprobe_primary 1 Nkx2-6 . -UP00148 UP00148_1_Hdx_3845.3 uniprobe_primary 1 Hdx . -UP00149 UP00149_1_Phox2b_3948.1 uniprobe_primary 1 Phox2b . -UP00150 UP00150_1_Irx6_2623.2 uniprobe_primary 1 Irx6 . -UP00151 UP00151_1_Barx2_3447.2 uniprobe_primary 1 Barx2 . -UP00152 UP00152_1_Arx_1738.2 uniprobe_primary 1 Arx . -UP00153 UP00153_1_Pitx1_2312.1 uniprobe_primary 1 Pitx1 . -UP00154 UP00154_1_Dlx3_1030.1 uniprobe_primary 1 Dlx3 . -UP00155 UP00155_1_Hmx2_3424.3 uniprobe_primary 1 Hmx2 . -UP00156 UP00156_1_Msx2_3449.1 uniprobe_primary 1 Msx2 . -UP00157 UP00157_1_Hmx3_3490.2 uniprobe_primary 1 Hmx3 . -UP00158 UP00158_1_Pou1f1_3818.1 uniprobe_primary 1 Pou1f1 . -UP00159 UP00159_1_Six2_2307.2 uniprobe_primary 1 Six2 . -UP00160 UP00160_1_Obox3_3439.1 uniprobe_primary 1 Obox3 . -UP00161 UP00161_1_Hmbox1_2674.1 uniprobe_primary 1 Hmbox1 . -UP00162 UP00162_1_Evx1_3952.2 uniprobe_primary 1 Evx1 . -UP00163 UP00163_1_En2_0952.1 uniprobe_primary 1 En2 . -UP00164 UP00164_1_Hoxa7_2668.2 uniprobe_primary 1 Hoxa7 . -UP00165 UP00165_1_Titf1_1722.2 uniprobe_primary 1 Titf1 . -UP00166 UP00166_1_Barhl1_2590.2 uniprobe_primary 1 Barhl1 . -UP00167 UP00167_1_En1_3123.2 uniprobe_primary 1 En1 . -UP00168 UP00168_1_Hoxd8_2644.1 uniprobe_primary 1 Hoxd8 . -UP00169 UP00169_1_Lmx1b_3433.2 uniprobe_primary 1 Lmx1b . -UP00170 UP00170_1_Isl2_3430.1 uniprobe_primary 1 Isl2 . -UP00171 UP00171_1_Msx3_3206.1 uniprobe_primary 1 Msx3 . -UP00172 UP00172_1_Prop1_3949.1 uniprobe_primary 1 Prop1 . -UP00173 UP00173_1_Hoxc13_3127.1 uniprobe_primary 1 Hoxc13 . -UP00174 UP00174_1_Hoxa2_3079.1 uniprobe_primary 1 Hoxa2 . -UP00175 UP00175_1_Lhx9_3492.1 uniprobe_primary 1 Lhx9 . -UP00176 UP00176_1_Crx_3485.1 uniprobe_primary 1 Crx . -UP00177 UP00177_1_Hoxd12_3481.1 uniprobe_primary 1 Hoxd12 . -UP00178 UP00178_1_Og2x_3719.1 uniprobe_primary 1 Og2x . -UP00179 UP00179_1_Pou2f3_3986.2 uniprobe_primary 1 Pou2f3 . -UP00180 UP00180_1_Hoxd13_2356.1 uniprobe_primary 1 Hoxd13 . -UP00181 UP00181_1_Barx1_2877.1 uniprobe_primary 1 Barx1 . -UP00182 UP00182_1_Hoxa6_1040.1 uniprobe_primary 1 Hoxa6 . -UP00183 UP00183_1_Hoxa13_3126.1 uniprobe_primary 1 Hoxa13 . -UP00184 UP00184_1_Lhx8_2247.2 uniprobe_primary 1 Lhx8 . -UP00185 UP00185_1_Pbx1_3203.1 uniprobe_primary 1 Pbx1 . -UP00186 UP00186_1_Meis1_2335.1 uniprobe_primary 1 Meis1 . -UP00187 UP00187_1_Alx4_1744.1 uniprobe_primary 1 Alx4 . -UP00188 UP00188_1_Lmx1a_2238.2 uniprobe_primary 1 Lmx1a . -UP00189 UP00189_1_Hoxa5_3415.1 uniprobe_primary 1 Hoxa5 . -UP00190 UP00190_1_Nkx2-3_3435.1 uniprobe_primary 1 Nkx2-3 . -UP00191 UP00191_1_Pou2f2_3748.1 uniprobe_primary 1 Pou2f2 . -UP00192 UP00192_1_Six1_0935.2 uniprobe_primary 1 Six1 . -UP00193 UP00193_1_Rhox11_1765.2 uniprobe_primary 1 Rhox11 . -UP00194 UP00194_1_Irx4_2242.3 uniprobe_primary 1 Irx4 . -UP00195 UP00195_1_Six3_1732.2 uniprobe_primary 1 Six3 . -UP00196 UP00196_1_Hoxa4_3426.1 uniprobe_primary 1 Hoxa4 . -UP00197 UP00197_1_Hoxc9_2367.2 uniprobe_primary 1 Hoxc9 . -UP00198 UP00198_1_Cphx_3484.1 uniprobe_primary 1 Cphx . -UP00199 UP00199_1_Six4_2860.1 uniprobe_primary 1 Six4 . -UP00200 UP00200_1_Nkx6-1_2825.1 uniprobe_primary 1 Nkx6-1 . -UP00201 UP00201_1_Emx2_3420.1 uniprobe_primary 1 Emx2 . -UP00202 UP00202_1_Dlx1_1741.2 uniprobe_primary 1 Dlx1 . -UP00203 UP00203_1_Pknox1_2364.2 uniprobe_primary 1 Pknox1 . -UP00204 UP00204_1_Gbx1_2883.2 uniprobe_primary 1 Gbx1 . -UP00205 UP00205_1_Pknox2_3077.2 uniprobe_primary 1 Pknox2 . -UP00206 UP00206_1_Hoxb7_3953.1 uniprobe_primary 1 Hoxb7 . -UP00207 UP00207_1_Hoxb9_3413.1 uniprobe_primary 1 Hoxb9 . -UP00208 UP00208_1_Obox5_2284.1 uniprobe_primary 1 Obox5 . -UP00209 UP00209_1_Cart1_0997.1 uniprobe_primary 1 Cart1 . -UP00210 UP00210_1_Mrg2_2302.1 uniprobe_primary 1 Mrg2 . -UP00211 UP00211_1_Pou3f3_3235.2 uniprobe_primary 1 Pou3f3 . -UP00212 UP00212_1_Lhx5_2279.1 uniprobe_primary 1 Lhx5 . -UP00213 UP00213_1_Hoxa9_2622.2 uniprobe_primary 1 Hoxa9 . -UP00214 UP00214_1_Hoxb5_3122.2 uniprobe_primary 1 Hoxb5 . -UP00215 UP00215_1_Vax1_3499.1 uniprobe_primary 1 Vax1 . -UP00216 UP00216_1_Obox1_3970.2 uniprobe_primary 1 Obox1 . -UP00217 UP00217_1_Hoxa10_2318.1 uniprobe_primary 1 Hoxa10 . -UP00218 UP00218_1_Dbx2_3487.1 uniprobe_primary 1 Dbx2 . -UP00219 UP00219_1_Cutl1_3494.1 uniprobe_primary 1 Cutl1 . -UP00220 UP00220_1_Nkx1-1_3856.3 uniprobe_primary 1 Nkx1-1 . -UP00221 UP00221_1_Phox2a_3947.1 uniprobe_primary 1 Phox2a . -UP00222 UP00222_1_Tcf2_0913.2 uniprobe_primary 1 Tcf2 . -UP00223 UP00223_1_Irx3_0920.1 uniprobe_primary 1 Irx3 . -UP00224 UP00224_1_Pax6_3838.3 uniprobe_primary 1 Pax6 . -UP00225 UP00225_1_Hlx1_2350.1 uniprobe_primary 1 Hlx1 . -UP00226 UP00226_1_Mrg1_2246.2 uniprobe_primary 1 Mrg1 . -UP00227 UP00227_1_Duxl_1286.2 uniprobe_primary 1 Duxl . -UP00228 UP00228_1_Bapx1_2343.1 uniprobe_primary 1 Bapx1 . -UP00229 UP00229_1_Otx1_2325.1 uniprobe_primary 1 Otx1 . -UP00230 UP00230_1_Dlx5_3419.2 uniprobe_primary 1 Dlx5 . -UP00231 UP00231_1_Nkx2-2_2823.1 uniprobe_primary 1 Nkx2-2 . -UP00232 UP00232_1_Dobox4_3956.2 uniprobe_primary 1 Dobox4 . -UP00233 UP00233_1_Meox1_2310.2 uniprobe_primary 1 Meox1 . -UP00234 UP00234_1_Msx1_3031.2 uniprobe_primary 1 Msx1 . -UP00235 UP00235_1_Hoxc11_3718.2 uniprobe_primary 1 Hoxc11 . -UP00236 UP00236_1_Irx2_0900.3 uniprobe_primary 1 Irx2 . -UP00237 UP00237_1_Otp_3496.1 uniprobe_primary 1 Otp . -UP00238 UP00238_1_Nkx6-3_3446.1 uniprobe_primary 1 Nkx6-3 . -UP00239 UP00239_1_Obox2_3438.2 uniprobe_primary 1 Obox2 . -UP00240 UP00240_1_Cdx1_2245.1 uniprobe_primary 1 Cdx1 . -UP00241 UP00241_1_Hoxd3_1742.2 uniprobe_primary 1 Hoxd3 . -UP00242 UP00242_1_Hoxc8_3429.2 uniprobe_primary 1 Hoxc8 . -UP00243 UP00243_1_Isx_3445.1 uniprobe_primary 1 Isx . -UP00244 UP00244_1_Tlx2_3498.2 uniprobe_primary 1 Tlx2 . -UP00245 UP00245_1_Hoxc10_2779.2 uniprobe_primary 1 Hoxc10 . -UP00246 UP00246_1_Hoxa11_2218.1 uniprobe_primary 1 Hoxa11 . -UP00247 UP00247_1_Pax4_3989.2 uniprobe_primary 1 Pax4 . -UP00248 UP00248_1_Pax7_3783.1 uniprobe_primary 1 Pax7 . -UP00249 UP00249_1_Nkx2-5_3436.1 uniprobe_primary 1 Nkx2-5 . -UP00250 UP00250_1_Irx5_2385.1 uniprobe_primary 1 Irx5 . -UP00251 UP00251_1_Esx1_3124.2 uniprobe_primary 1 Esx1 . -UP00252 UP00252_1_Hoxc5_2630.2 uniprobe_primary 1 Hoxc5 . -UP00253 UP00253_1_Rax_3443.1 uniprobe_primary 1 Rax . -UP00254 UP00254_1_Pou2f1_3081.2 uniprobe_primary 1 Pou2f1 . -UP00255 UP00255_1_Dbx1_3486.1 uniprobe_primary 1 Dbx1 . -UP00256 UP00256_1_Lhx6_2272.1 uniprobe_primary 1 Lhx6 . -UP00257 UP00257_1_Shox2_2641.2 uniprobe_primary 1 Shox2 . -UP00258 UP00258_1_Tgif2_3451.1 uniprobe_primary 1 Tgif2 . -UP00259 UP00259_1_Hoxb6_3428.2 uniprobe_primary 1 Hoxb6 . -UP00260 UP00260_1_Hoxc6_3954.2 uniprobe_primary 1 Hoxc6 . -UP00261 UP00261_1_Lhx4_1719.2 uniprobe_primary 1 Lhx4 . -UP00262 UP00262_1_Lhx1_2240.2 uniprobe_primary 1 Lhx1 . -UP00263 UP00263_1_Hoxb8_3780.2 uniprobe_primary 1 Hoxb8 . -UP00264 UP00264_1_Hoxa1_3425.1 uniprobe_primary 1 Hoxa1 . -UP00265 UP00265_1_Pitx3_3497.2 uniprobe_primary 1 Pitx3 . -UP00266 UP00266_1_Prrx1_3442.1 uniprobe_primary 1 Prrx1 . -UP00267 UP00267_1_Otx2_3441.1 uniprobe_primary 1 Otx2 . -UP00391 UP00391_1_Hoxa3_primary uniprobe_primary 1 Hoxa3 . -UP00406 UP00406_1_Spdef_primary uniprobe_primary 1 Spdef . -UP00407 UP00407_1_Elf3_primary uniprobe_primary 1 Elf3 . -UP00408 UP00408_1_Gabpa_primary uniprobe_primary 1 Gabpa . +UP00000 UP00000_1_Smad3_primary uniprobe_primary 1 Smad3 . . . . +UP00001 UP00001_1_E2F2_primary uniprobe_primary 1 E2F2 . . . . +UP00002 UP00002_1_Sp4_primary uniprobe_primary 1 Sp4 . . . . +UP00003 UP00003_1_E2F3_primary uniprobe_primary 1 E2F3 . . . . +UP00004 UP00004_1_Sox14_primary uniprobe_primary 1 Sox14 . . . . +UP00005 UP00005_1_Tcfap2a_primary uniprobe_primary 1 Tcfap2a . . . . +UP00006 UP00006_1_Zic3_primary uniprobe_primary 1 Zic3 . . . . +UP00007 UP00007_1_Egr1_primary uniprobe_primary 1 Egr1 . . . . +UP00008 UP00008_1_Six6_primary uniprobe_primary 1 Six6 . . . . +UP00009 UP00009_1_Nr2f2_primary uniprobe_primary 1 Nr2f2 . . . . +UP00010 UP00010_1_Tcfap2b_primary uniprobe_primary 1 Tcfap2b . . . . +UP00011 UP00011_1_Irf6_primary uniprobe_primary 1 Irf6 . . . . +UP00012 UP00012_1_Bbx_primary uniprobe_primary 1 Bbx . . . . +UP00014 UP00014_1_Sox17_primary uniprobe_primary 1 Sox17 . . . . +UP00015 UP00015_1_Ehf_primary uniprobe_primary 1 Ehf . . . . +UP00016 UP00016_1_Sry_primary uniprobe_primary 1 Sry . . . . +UP00017 UP00017_1_Nkx3-1_primary uniprobe_primary 1 Nkx3-1 . . . . +UP00018 UP00018_1_Irf4_primary uniprobe_primary 1 Irf4 . . . . +UP00019 UP00019_1_Zbtb12_primary uniprobe_primary 1 Zbtb12 . . . . +UP00020 UP00020_1_Atf1_primary uniprobe_primary 1 Atf1 . . . . +UP00021 UP00021_1_Zfp281_primary uniprobe_primary 1 Zfp281 . . . . +UP00022 UP00022_1_Zfp740_primary uniprobe_primary 1 Zfp740 . . . . +UP00023 UP00023_1_Sox30_primary uniprobe_primary 1 Sox30 . . . . +UP00024 UP00024_1_Glis2_primary uniprobe_primary 1 Glis2 . . . . +UP00025 UP00025_1_Foxk1_primary uniprobe_primary 1 Foxk1 . . . . +UP00026 UP00026_1_Zscan4_primary uniprobe_primary 1 Zscan4 . . . . +UP00027 UP00027_1_Osr1_primary uniprobe_primary 1 Osr1 . . . . +UP00028 UP00028_1_Tcfap2e_primary uniprobe_primary 1 Tcfap2e . . . . +UP00029 UP00029_1_Tbp_primary uniprobe_primary 1 Tbp . . . . +UP00030 UP00030_1_Sox11_primary uniprobe_primary 1 Sox11 . . . . +UP00031 UP00031_1_Zbtb3_primary uniprobe_primary 1 Zbtb3 . . . . +UP00032 UP00032_1_Gata3_primary uniprobe_primary 1 Gata3 . . . . +UP00033 UP00033_1_Zfp410_primary uniprobe_primary 1 Zfp410 . . . . +UP00034 UP00034_1_Sox7_primary uniprobe_primary 1 Sox7 . . . . +UP00035 UP00035_1_Hic1_primary uniprobe_primary 1 Hic1 . . . . +UP00036 UP00036_1_Myf6_primary uniprobe_primary 1 Myf6 . . . . +UP00037 UP00037_1_Zfp105_primary uniprobe_primary 1 Zfp105 . . . . +UP00039 UP00039_1_Foxj3_primary uniprobe_primary 1 Foxj3 . . . . +UP00040 UP00040_1_Irf5_primary uniprobe_primary 1 Irf5 . . . . +UP00041 UP00041_1_Foxj1_primary uniprobe_primary 1 Foxj1 . . . . +UP00042 UP00042_1_Gm397_primary uniprobe_primary 1 Gm397 . . . . +UP00043 UP00043_1_Bcl6b_primary uniprobe_primary 1 Bcl6b . . . . +UP00044 UP00044_1_Mafk_primary uniprobe_primary 1 Mafk . . . . +UP00045 UP00045_1_Mafb_primary uniprobe_primary 1 Mafb . . . . +UP00046 UP00046_1_Tcfe2a_primary uniprobe_primary 1 Tcfe2a . . . . +UP00047 UP00047_1_Zbtb7b_primary uniprobe_primary 1 Zbtb7b . . . . +UP00048 UP00048_1_Rara_primary uniprobe_primary 1 Rara . . . . +UP00049 UP00049_1_Sp100_primary uniprobe_primary 1 Sp100 . . . . +UP00050 UP00050_1_Bhlhb2_primary uniprobe_primary 1 Bhlhb2 . . . . +UP00051 UP00051_1_Sox8_primary uniprobe_primary 1 Sox8 . . . . +UP00052 UP00052_1_Osr2_primary uniprobe_primary 1 Osr2 . . . . +UP00053 UP00053_1_Rxra_primary uniprobe_primary 1 Rxra . . . . +UP00054 UP00054_1_Tcf7_primary uniprobe_primary 1 Tcf7 . . . . +UP00055 UP00055_1_Hbp1_primary uniprobe_primary 1 Hbp1 . . . . +UP00056 UP00056_1_Rfx4_primary uniprobe_primary 1 Rfx4 . . . . +UP00057 UP00057_1_Zic2_primary uniprobe_primary 1 Zic2 . . . . +UP00058 UP00058_1_Tcf3_primary uniprobe_primary 1 Tcf3 . . . . +UP00059 UP00059_1_Arid5a_primary uniprobe_primary 1 Arid5a . . . . +UP00060 UP00060_1_Max_primary uniprobe_primary 1 Max . . . . +UP00061 UP00061_1_Foxl1_primary uniprobe_primary 1 Foxl1 . . . . +UP00062 UP00062_1_Sox4_primary uniprobe_primary 1 Sox4 . . . . +UP00064 UP00064_1_Sox18_primary uniprobe_primary 1 Sox18 . . . . +UP00065 UP00065_1_Zfp161_primary uniprobe_primary 1 Zfp161 . . . . +UP00066 UP00066_1_Hnf4a_primary uniprobe_primary 1 Hnf4a . . . . +UP00067 UP00067_1_Lef1_primary uniprobe_primary 1 Lef1 . . . . +UP00068 UP00068_1_Eomes_primary uniprobe_primary 1 Eomes . . . . +UP00069 UP00069_1_Sox1_primary uniprobe_primary 1 Sox1 . . . . +UP00070 UP00070_1_Gcm1_primary uniprobe_primary 1 Gcm1 . . . . +UP00071 UP00071_1_Sox21_primary uniprobe_primary 1 Sox21 . . . . +UP00072 UP00072_1_IRC900814_primary uniprobe_primary 1 IRC900814 . . . . +UP00073 UP00073_1_Foxa2_primary uniprobe_primary 1 Foxa2 . . . . +UP00074 UP00074_1_Isgf3g_primary uniprobe_primary 1 Isgf3g . . . . +UP00075 UP00075_1_Sox15_primary uniprobe_primary 1 Sox15 . . . . +UP00076 UP00076_1_Rfxdc2_primary uniprobe_primary 1 Rfxdc2 . . . . +UP00077 UP00077_1_Srf_primary uniprobe_primary 1 Srf . . . . +UP00078 UP00078_1_Arid3a_primary uniprobe_primary 1 Arid3a . . . . +UP00079 UP00079_1_Esrra_primary uniprobe_primary 1 Esrra . . . . +UP00080 UP00080_1_Gata5_primary uniprobe_primary 1 Gata5 . . . . +UP00081 UP00081_1_Mybl1_primary uniprobe_primary 1 Mybl1 . . . . +UP00082 UP00082_1_Zfp187_primary uniprobe_primary 1 Zfp187 . . . . +UP00083 UP00083_1_Tcf7l2_primary uniprobe_primary 1 Tcf7l2 . . . . +UP00084 UP00084_1_Gmeb1_primary uniprobe_primary 1 Gmeb1 . . . . +UP00085 UP00085_1_Sfpi1_primary uniprobe_primary 1 Sfpi1 . . . . +UP00086 UP00086_1_Irf3_primary uniprobe_primary 1 Irf3 . . . . +UP00087 UP00087_1_Tcfap2c_primary uniprobe_primary 1 Tcfap2c . . . . +UP00088 UP00088_1_Plagl1_primary uniprobe_primary 1 Plagl1 . . . . +UP00089 UP00089_1_Tcf1_primary uniprobe_primary 1 Tcf1 . . . . +UP00091 UP00091_1_Sox5_primary uniprobe_primary 1 Sox5 . . . . +UP00092 UP00092_1_Myb_primary uniprobe_primary 1 Myb . . . . +UP00093 UP00093_1_Klf7_primary uniprobe_primary 1 Klf7 . . . . +UP00094 UP00094_1_Zfp128_primary uniprobe_primary 1 Zfp128 . . . . +UP00095 UP00095_1_Zfp691_primary uniprobe_primary 1 Zfp691 . . . . +UP00096 UP00096_1_Sox13_primary uniprobe_primary 1 Sox13 . . . . +UP00097 UP00097_1_Mtf1_primary uniprobe_primary 1 Mtf1 . . . . +UP00098 UP00098_1_Rfx3_primary uniprobe_primary 1 Rfx3 . . . . +UP00099 UP00099_1_Ascl2_primary uniprobe_primary 1 Ascl2 . . . . +UP00100 UP00100_1_Gata6_primary uniprobe_primary 1 Gata6 . . . . +UP00101 UP00101_1_Sox12_primary uniprobe_primary 1 Sox12 . . . . +UP00102 UP00102_1_Zic1_primary uniprobe_primary 1 Zic1 . . . . +UP00103 UP00103_1_Jundm2_primary uniprobe_primary 1 Jundm2 . . . . +UP00104 UP00104_1_Hmx1_3423.1 uniprobe_primary 1 Hmx1 . . . . +UP00105 UP00105_1_Pou3f4_3773.1 uniprobe_primary 1 Pou3f4 . . . . +UP00106 UP00106_1_Vax2_3500.1 uniprobe_primary 1 Vax2 . . . . +UP00107 UP00107_1_Nkx2-4_3074.1 uniprobe_primary 1 Nkx2-4 . . . . +UP00108 UP00108_1_Alx3_3418.2 uniprobe_primary 1 Alx3 . . . . +UP00109 UP00109_1_Obox6_3440.2 uniprobe_primary 1 Obox6 . . . . +UP00110 UP00110_1_Dlx4_3488.2 uniprobe_primary 1 Dlx4 . . . . +UP00111 UP00111_1_Dmbx1_2277.1 uniprobe_primary 1 Dmbx1 . . . . +UP00112 UP00112_1_Gsc_2327.3 uniprobe_primary 1 Gsc . . . . +UP00113 UP00113_1_Hoxc4_3491.1 uniprobe_primary 1 Hoxc4 . . . . +UP00114 UP00114_1_Homez_1063.2 uniprobe_primary 1 Homez . . . . +UP00115 UP00115_1_Lhx2_0953.2 uniprobe_primary 1 Lhx2 . . . . +UP00116 UP00116_1_Rhox6_4251.1 uniprobe_primary 1 Rhox6 . . . . +UP00117 UP00117_1_Hoxd11_3873.1 uniprobe_primary 1 Hoxd11 . . . . +UP00118 UP00118_1_Pou4f3_2791.1 uniprobe_primary 1 Pou4f3 . . . . +UP00119 UP00119_1_Nkx2-9_3082.1 uniprobe_primary 1 Nkx2-9 . . . . +UP00120 UP00120_1_Lbx2_3869.2 uniprobe_primary 1 Lbx2 . . . . +UP00121 UP00121_1_Hoxd10_2368.2 uniprobe_primary 1 Hoxd10 . . . . +UP00122 UP00122_1_Tgif1_2342.2 uniprobe_primary 1 Tgif1 . . . . +UP00123 UP00123_1_Hlxb9_3422.1 uniprobe_primary 1 Hlxb9 . . . . +UP00124 UP00124_1_Ipf1_3815.1 uniprobe_primary 1 Ipf1 . . . . +UP00125 UP00125_1_Pitx2_2274.3 uniprobe_primary 1 Pitx2 . . . . +UP00126 UP00126_1_Dlx2_2273.2 uniprobe_primary 1 Dlx2 . . . . +UP00127 UP00127_1_Gsh2_3990.2 uniprobe_primary 1 Gsh2 . . . . +UP00128 UP00128_1_Pou3f2_2824.1 uniprobe_primary 1 Pou3f2 . . . . +UP00129 UP00129_1_Pou3f1_3819.1 uniprobe_primary 1 Pou3f1 . . . . +UP00130 UP00130_1_Lhx3_3431.1 uniprobe_primary 1 Lhx3 . . . . +UP00131 UP00131_1_Gbx2_3110.1 uniprobe_primary 1 Gbx2 . . . . +UP00132 UP00132_1_Evx2_2645.3 uniprobe_primary 1 Evx2 . . . . +UP00133 UP00133_1_Cdx2_4272.1 uniprobe_primary 1 Cdx2 . . . . +UP00134 UP00134_1_Hoxb13_3479.1 uniprobe_primary 1 Hoxb13 . . . . +UP00135 UP00135_1_Hoxc12_3480.1 uniprobe_primary 1 Hoxc12 . . . . +UP00136 UP00136_1_Prrx2_3072.1 uniprobe_primary 1 Prrx2 . . . . +UP00137 UP00137_1_Hoxb3_1720.2 uniprobe_primary 1 Hoxb3 . . . . +UP00138 UP00138_1_Bsx_3483.2 uniprobe_primary 1 Bsx . . . . +UP00139 UP00139_1_Nkx1-2_3214.1 uniprobe_primary 1 Nkx1-2 . . . . +UP00140 UP00140_1_Hoxd1_3448.1 uniprobe_primary 1 Hoxd1 . . . . +UP00141 UP00141_1_Vsx1_1728.1 uniprobe_primary 1 Vsx1 . . . . +UP00142 UP00142_1_Uncx4.1_2281.2 uniprobe_primary 1 Uncx4 . . . . +UP00143 UP00143_1_Dobox5_3493.1 uniprobe_primary 1 Dobox5 . . . . +UP00144 UP00144_1_Hoxb4_2627.1 uniprobe_primary 1 Hoxb4 . . . . +UP00145 UP00145_1_Barhl2_3868.1 uniprobe_primary 1 Barhl2 . . . . +UP00146 UP00146_1_Pou6f1_1731.2 uniprobe_primary 1 Pou6f1 . . . . +UP00147 UP00147_1_Nkx2-6_3437.1 uniprobe_primary 1 Nkx2-6 . . . . +UP00148 UP00148_1_Hdx_3845.3 uniprobe_primary 1 Hdx . . . . +UP00149 UP00149_1_Phox2b_3948.1 uniprobe_primary 1 Phox2b . . . . +UP00150 UP00150_1_Irx6_2623.2 uniprobe_primary 1 Irx6 . . . . +UP00151 UP00151_1_Barx2_3447.2 uniprobe_primary 1 Barx2 . . . . +UP00152 UP00152_1_Arx_1738.2 uniprobe_primary 1 Arx . . . . +UP00153 UP00153_1_Pitx1_2312.1 uniprobe_primary 1 Pitx1 . . . . +UP00154 UP00154_1_Dlx3_1030.1 uniprobe_primary 1 Dlx3 . . . . +UP00155 UP00155_1_Hmx2_3424.3 uniprobe_primary 1 Hmx2 . . . . +UP00156 UP00156_1_Msx2_3449.1 uniprobe_primary 1 Msx2 . . . . +UP00157 UP00157_1_Hmx3_3490.2 uniprobe_primary 1 Hmx3 . . . . +UP00158 UP00158_1_Pou1f1_3818.1 uniprobe_primary 1 Pou1f1 . . . . +UP00159 UP00159_1_Six2_2307.2 uniprobe_primary 1 Six2 . . . . +UP00160 UP00160_1_Obox3_3439.1 uniprobe_primary 1 Obox3 . . . . +UP00161 UP00161_1_Hmbox1_2674.1 uniprobe_primary 1 Hmbox1 . . . . +UP00162 UP00162_1_Evx1_3952.2 uniprobe_primary 1 Evx1 . . . . +UP00163 UP00163_1_En2_0952.1 uniprobe_primary 1 En2 . . . . +UP00164 UP00164_1_Hoxa7_2668.2 uniprobe_primary 1 Hoxa7 . . . . +UP00165 UP00165_1_Titf1_1722.2 uniprobe_primary 1 Titf1 . . . . +UP00166 UP00166_1_Barhl1_2590.2 uniprobe_primary 1 Barhl1 . . . . +UP00167 UP00167_1_En1_3123.2 uniprobe_primary 1 En1 . . . . +UP00168 UP00168_1_Hoxd8_2644.1 uniprobe_primary 1 Hoxd8 . . . . +UP00169 UP00169_1_Lmx1b_3433.2 uniprobe_primary 1 Lmx1b . . . . +UP00170 UP00170_1_Isl2_3430.1 uniprobe_primary 1 Isl2 . . . . +UP00171 UP00171_1_Msx3_3206.1 uniprobe_primary 1 Msx3 . . . . +UP00172 UP00172_1_Prop1_3949.1 uniprobe_primary 1 Prop1 . . . . +UP00173 UP00173_1_Hoxc13_3127.1 uniprobe_primary 1 Hoxc13 . . . . +UP00174 UP00174_1_Hoxa2_3079.1 uniprobe_primary 1 Hoxa2 . . . . +UP00175 UP00175_1_Lhx9_3492.1 uniprobe_primary 1 Lhx9 . . . . +UP00176 UP00176_1_Crx_3485.1 uniprobe_primary 1 Crx . . . . +UP00177 UP00177_1_Hoxd12_3481.1 uniprobe_primary 1 Hoxd12 . . . . +UP00178 UP00178_1_Og2x_3719.1 uniprobe_primary 1 Og2x . . . . +UP00179 UP00179_1_Pou2f3_3986.2 uniprobe_primary 1 Pou2f3 . . . . +UP00180 UP00180_1_Hoxd13_2356.1 uniprobe_primary 1 Hoxd13 . . . . +UP00181 UP00181_1_Barx1_2877.1 uniprobe_primary 1 Barx1 . . . . +UP00182 UP00182_1_Hoxa6_1040.1 uniprobe_primary 1 Hoxa6 . . . . +UP00183 UP00183_1_Hoxa13_3126.1 uniprobe_primary 1 Hoxa13 . . . . +UP00184 UP00184_1_Lhx8_2247.2 uniprobe_primary 1 Lhx8 . . . . +UP00185 UP00185_1_Pbx1_3203.1 uniprobe_primary 1 Pbx1 . . . . +UP00186 UP00186_1_Meis1_2335.1 uniprobe_primary 1 Meis1 . . . . +UP00187 UP00187_1_Alx4_1744.1 uniprobe_primary 1 Alx4 . . . . +UP00188 UP00188_1_Lmx1a_2238.2 uniprobe_primary 1 Lmx1a . . . . +UP00189 UP00189_1_Hoxa5_3415.1 uniprobe_primary 1 Hoxa5 . . . . +UP00190 UP00190_1_Nkx2-3_3435.1 uniprobe_primary 1 Nkx2-3 . . . . +UP00191 UP00191_1_Pou2f2_3748.1 uniprobe_primary 1 Pou2f2 . . . . +UP00192 UP00192_1_Six1_0935.2 uniprobe_primary 1 Six1 . . . . +UP00193 UP00193_1_Rhox11_1765.2 uniprobe_primary 1 Rhox11 . . . . +UP00194 UP00194_1_Irx4_2242.3 uniprobe_primary 1 Irx4 . . . . +UP00195 UP00195_1_Six3_1732.2 uniprobe_primary 1 Six3 . . . . +UP00196 UP00196_1_Hoxa4_3426.1 uniprobe_primary 1 Hoxa4 . . . . +UP00197 UP00197_1_Hoxc9_2367.2 uniprobe_primary 1 Hoxc9 . . . . +UP00198 UP00198_1_Cphx_3484.1 uniprobe_primary 1 Cphx . . . . +UP00199 UP00199_1_Six4_2860.1 uniprobe_primary 1 Six4 . . . . +UP00200 UP00200_1_Nkx6-1_2825.1 uniprobe_primary 1 Nkx6-1 . . . . +UP00201 UP00201_1_Emx2_3420.1 uniprobe_primary 1 Emx2 . . . . +UP00202 UP00202_1_Dlx1_1741.2 uniprobe_primary 1 Dlx1 . . . . +UP00203 UP00203_1_Pknox1_2364.2 uniprobe_primary 1 Pknox1 . . . . +UP00204 UP00204_1_Gbx1_2883.2 uniprobe_primary 1 Gbx1 . . . . +UP00205 UP00205_1_Pknox2_3077.2 uniprobe_primary 1 Pknox2 . . . . +UP00206 UP00206_1_Hoxb7_3953.1 uniprobe_primary 1 Hoxb7 . . . . +UP00207 UP00207_1_Hoxb9_3413.1 uniprobe_primary 1 Hoxb9 . . . . +UP00208 UP00208_1_Obox5_2284.1 uniprobe_primary 1 Obox5 . . . . +UP00209 UP00209_1_Cart1_0997.1 uniprobe_primary 1 Cart1 . . . . +UP00210 UP00210_1_Mrg2_2302.1 uniprobe_primary 1 Mrg2 . . . . +UP00211 UP00211_1_Pou3f3_3235.2 uniprobe_primary 1 Pou3f3 . . . . +UP00212 UP00212_1_Lhx5_2279.1 uniprobe_primary 1 Lhx5 . . . . +UP00213 UP00213_1_Hoxa9_2622.2 uniprobe_primary 1 Hoxa9 . . . . +UP00214 UP00214_1_Hoxb5_3122.2 uniprobe_primary 1 Hoxb5 . . . . +UP00215 UP00215_1_Vax1_3499.1 uniprobe_primary 1 Vax1 . . . . +UP00216 UP00216_1_Obox1_3970.2 uniprobe_primary 1 Obox1 . . . . +UP00217 UP00217_1_Hoxa10_2318.1 uniprobe_primary 1 Hoxa10 . . . . +UP00218 UP00218_1_Dbx2_3487.1 uniprobe_primary 1 Dbx2 . . . . +UP00219 UP00219_1_Cutl1_3494.1 uniprobe_primary 1 Cutl1 . . . . +UP00220 UP00220_1_Nkx1-1_3856.3 uniprobe_primary 1 Nkx1-1 . . . . +UP00221 UP00221_1_Phox2a_3947.1 uniprobe_primary 1 Phox2a . . . . +UP00222 UP00222_1_Tcf2_0913.2 uniprobe_primary 1 Tcf2 . . . . +UP00223 UP00223_1_Irx3_0920.1 uniprobe_primary 1 Irx3 . . . . +UP00224 UP00224_1_Pax6_3838.3 uniprobe_primary 1 Pax6 . . . . +UP00225 UP00225_1_Hlx1_2350.1 uniprobe_primary 1 Hlx1 . . . . +UP00226 UP00226_1_Mrg1_2246.2 uniprobe_primary 1 Mrg1 . . . . +UP00227 UP00227_1_Duxl_1286.2 uniprobe_primary 1 Duxl . . . . +UP00228 UP00228_1_Bapx1_2343.1 uniprobe_primary 1 Bapx1 . . . . +UP00229 UP00229_1_Otx1_2325.1 uniprobe_primary 1 Otx1 . . . . +UP00230 UP00230_1_Dlx5_3419.2 uniprobe_primary 1 Dlx5 . . . . +UP00231 UP00231_1_Nkx2-2_2823.1 uniprobe_primary 1 Nkx2-2 . . . . +UP00232 UP00232_1_Dobox4_3956.2 uniprobe_primary 1 Dobox4 . . . . +UP00233 UP00233_1_Meox1_2310.2 uniprobe_primary 1 Meox1 . . . . +UP00234 UP00234_1_Msx1_3031.2 uniprobe_primary 1 Msx1 . . . . +UP00235 UP00235_1_Hoxc11_3718.2 uniprobe_primary 1 Hoxc11 . . . . +UP00236 UP00236_1_Irx2_0900.3 uniprobe_primary 1 Irx2 . . . . +UP00237 UP00237_1_Otp_3496.1 uniprobe_primary 1 Otp . . . . +UP00238 UP00238_1_Nkx6-3_3446.1 uniprobe_primary 1 Nkx6-3 . . . . +UP00239 UP00239_1_Obox2_3438.2 uniprobe_primary 1 Obox2 . . . . +UP00240 UP00240_1_Cdx1_2245.1 uniprobe_primary 1 Cdx1 . . . . +UP00241 UP00241_1_Hoxd3_1742.2 uniprobe_primary 1 Hoxd3 . . . . +UP00242 UP00242_1_Hoxc8_3429.2 uniprobe_primary 1 Hoxc8 . . . . +UP00243 UP00243_1_Isx_3445.1 uniprobe_primary 1 Isx . . . . +UP00244 UP00244_1_Tlx2_3498.2 uniprobe_primary 1 Tlx2 . . . . +UP00245 UP00245_1_Hoxc10_2779.2 uniprobe_primary 1 Hoxc10 . . . . +UP00246 UP00246_1_Hoxa11_2218.1 uniprobe_primary 1 Hoxa11 . . . . +UP00247 UP00247_1_Pax4_3989.2 uniprobe_primary 1 Pax4 . . . . +UP00248 UP00248_1_Pax7_3783.1 uniprobe_primary 1 Pax7 . . . . +UP00249 UP00249_1_Nkx2-5_3436.1 uniprobe_primary 1 Nkx2-5 . . . . +UP00250 UP00250_1_Irx5_2385.1 uniprobe_primary 1 Irx5 . . . . +UP00251 UP00251_1_Esx1_3124.2 uniprobe_primary 1 Esx1 . . . . +UP00252 UP00252_1_Hoxc5_2630.2 uniprobe_primary 1 Hoxc5 . . . . +UP00253 UP00253_1_Rax_3443.1 uniprobe_primary 1 Rax . . . . +UP00254 UP00254_1_Pou2f1_3081.2 uniprobe_primary 1 Pou2f1 . . . . +UP00255 UP00255_1_Dbx1_3486.1 uniprobe_primary 1 Dbx1 . . . . +UP00256 UP00256_1_Lhx6_2272.1 uniprobe_primary 1 Lhx6 . . . . +UP00257 UP00257_1_Shox2_2641.2 uniprobe_primary 1 Shox2 . . . . +UP00258 UP00258_1_Tgif2_3451.1 uniprobe_primary 1 Tgif2 . . . . +UP00259 UP00259_1_Hoxb6_3428.2 uniprobe_primary 1 Hoxb6 . . . . +UP00260 UP00260_1_Hoxc6_3954.2 uniprobe_primary 1 Hoxc6 . . . . +UP00261 UP00261_1_Lhx4_1719.2 uniprobe_primary 1 Lhx4 . . . . +UP00262 UP00262_1_Lhx1_2240.2 uniprobe_primary 1 Lhx1 . . . . +UP00263 UP00263_1_Hoxb8_3780.2 uniprobe_primary 1 Hoxb8 . . . . +UP00264 UP00264_1_Hoxa1_3425.1 uniprobe_primary 1 Hoxa1 . . . . +UP00265 UP00265_1_Pitx3_3497.2 uniprobe_primary 1 Pitx3 . . . . +UP00266 UP00266_1_Prrx1_3442.1 uniprobe_primary 1 Prrx1 . . . . +UP00267 UP00267_1_Otx2_3441.1 uniprobe_primary 1 Otx2 . . . . +UP00391 UP00391_1_Hoxa3_primary uniprobe_primary 1 Hoxa3 . . . . +UP00406 UP00406_1_Spdef_primary uniprobe_primary 1 Spdef . . . . +UP00407 UP00407_1_Elf3_primary uniprobe_primary 1 Elf3 . . . . +UP00408 UP00408_1_Gabpa_primary uniprobe_primary 1 Gabpa . . . . diff --git a/data/motifs/uniprobe_secondary.mtf b/data/motifs/uniprobe_secondary.mtf index 2208158d4..fe489adfb 100644 --- a/data/motifs/uniprobe_secondary.mtf +++ b/data/motifs/uniprobe_secondary.mtf @@ -1,118 +1,119 @@ -UP00000 UP00000_2_Smad3_secondary uniprobe_secondary 2 Smad3 . -UP00001 UP00001_2_E2F2_secondary uniprobe_secondary 2 E2F2 . -UP00002 UP00002_2_Sp4_secondary uniprobe_secondary 2 Sp4 . -UP00003 UP00003_2_E2F3_secondary uniprobe_secondary 2 E2F3 . -UP00004 UP00004_2_Sox14_secondary uniprobe_secondary 2 Sox14 . -UP00005 UP00005_2_Tcfap2a_secondary uniprobe_secondary 2 Tcfap2a . -UP00006 UP00006_2_Zic3_secondary uniprobe_secondary 2 Zic3 . -UP00007 UP00007_2_Egr1_secondary uniprobe_secondary 2 Egr1 . -UP00008 UP00008_2_Six6_secondary uniprobe_secondary 2 Six6 . -UP00008 UP00008_4_Six6 uniprobe_secondary 4 Six6 . -UP00008 UP00008_3_Six6_2267.4 uniprobe_secondary 3 Six6 . -UP00009 UP00009_2_Nr2f2_secondary uniprobe_secondary 2 Nr2f2 . -UP00010 UP00010_2_Tcfap2b_secondary uniprobe_secondary 2 Tcfap2b . -UP00011 UP00011_2_Irf6_secondary uniprobe_secondary 2 Irf6 . -UP00012 UP00012_2_Bbx_secondary uniprobe_secondary 2 Bbx . -UP00014 UP00014_2_Sox17_secondary uniprobe_secondary 2 Sox17 . -UP00015 UP00015_2_Ehf_secondary uniprobe_secondary 2 Ehf . -UP00016 UP00016_2_Sry_secondary uniprobe_secondary 2 Sry . -UP00017 UP00017_3_Nkx3-1_2923.2 uniprobe_secondary 3 Nkx3-1 . -UP00017 UP00017_2_Nkx3-1_secondary uniprobe_secondary 2 Nkx3-1 . -UP00018 UP00018_2_Irf4_secondary uniprobe_secondary 2 Irf4 . -UP00019 UP00019_2_Zbtb12_secondary uniprobe_secondary 2 Zbtb12 . -UP00020 UP00020_2_Atf1_secondary uniprobe_secondary 2 Atf1 . -UP00021 UP00021_2_Zfp281_secondary uniprobe_secondary 2 Zfp281 . -UP00022 UP00022_2_Zfp740_secondary uniprobe_secondary 2 Zfp740 . -UP00023 UP00023_2_Sox30_secondary uniprobe_secondary 2 Sox30 . -UP00024 UP00024_2_Glis2_secondary uniprobe_secondary 2 Glis2 . -UP00025 UP00025_2_Foxk1_secondary uniprobe_secondary 2 Foxk1 . -UP00026 UP00026_2_Zscan4_secondary uniprobe_secondary 2 Zscan4 . -UP00027 UP00027_2_Osr1_secondary uniprobe_secondary 2 Osr1 . -UP00028 UP00028_2_Tcfap2e_secondary uniprobe_secondary 2 Tcfap2e . -UP00029 UP00029_2_Tbp_secondary uniprobe_secondary 2 Tbp . -UP00030 UP00030_2_Sox11_secondary uniprobe_secondary 2 Sox11 . -UP00031 UP00031_2_Zbtb3_secondary uniprobe_secondary 2 Zbtb3 . -UP00032 UP00032_2_Gata3_secondary uniprobe_secondary 2 Gata3 . -UP00033 UP00033_2_Zfp410_secondary uniprobe_secondary 2 Zfp410 . -UP00034 UP00034_2_Sox7_secondary uniprobe_secondary 2 Sox7 . -UP00035 UP00035_2_Hic1_secondary uniprobe_secondary 2 Hic1 . -UP00036 UP00036_2_Myf6_secondary uniprobe_secondary 2 Myf6 . -UP00037 UP00037_2_Zfp105_secondary uniprobe_secondary 2 Zfp105 . -UP00039 UP00039_2_Foxj3_secondary uniprobe_secondary 2 Foxj3 . -UP00040 UP00040_2_Irf5_secondary uniprobe_secondary 2 Irf5 . -UP00041 UP00041_2_Foxj1_secondary uniprobe_secondary 2 Foxj1 . -UP00042 UP00042_2_Gm397_secondary uniprobe_secondary 2 Gm397 . -UP00043 UP00043_2_Bcl6b_secondary uniprobe_secondary 2 Bcl6b . -UP00044 UP00044_2_Mafk_secondary uniprobe_secondary 2 Mafk . -UP00045 UP00045_2_Mafb_secondary uniprobe_secondary 2 Mafb . -UP00046 UP00046_2_Tcfe2a_secondary uniprobe_secondary 2 Tcfe2a . -UP00047 UP00047_2_Zbtb7b_secondary uniprobe_secondary 2 Zbtb7b . -UP00048 UP00048_2_Rara_secondary uniprobe_secondary 2 Rara . -UP00049 UP00049_2_Sp100_secondary uniprobe_secondary 2 Sp100 . -UP00050 UP00050_2_Bhlhb2_secondary uniprobe_secondary 2 Bhlhb2 . -UP00051 UP00051_2_Sox8_secondary uniprobe_secondary 2 Sox8 . -UP00052 UP00052_2_Osr2_secondary uniprobe_secondary 2 Osr2 . -UP00053 UP00053_2_Rxra_secondary uniprobe_secondary 2 Rxra . -UP00054 UP00054_2_Tcf7_secondary uniprobe_secondary 2 Tcf7 . -UP00055 UP00055_2_Hbp1_secondary uniprobe_secondary 2 Hbp1 . -UP00056 UP00056_2_Rfx4_secondary uniprobe_secondary 2 Rfx4 . -UP00057 UP00057_2_Zic2_secondary uniprobe_secondary 2 Zic2 . -UP00058 UP00058_2_Tcf3_secondary uniprobe_secondary 2 Tcf3 . -UP00059 UP00059_2_Arid5a_secondary uniprobe_secondary 2 Arid5a . -UP00060 UP00060_2_Max_secondary uniprobe_secondary 2 Max . -UP00061 UP00061_2_Foxl1_secondary uniprobe_secondary 2 Foxl1 . -UP00062 UP00062_2_Sox4_secondary uniprobe_secondary 2 Sox4 . -UP00064 UP00064_2_Sox18_secondary uniprobe_secondary 2 Sox18 . -UP00065 UP00065_2_Zfp161_secondary uniprobe_secondary 2 Zfp161 . -UP00066 UP00066_2_Hnf4a_secondary uniprobe_secondary 2 Hnf4a . -UP00067 UP00067_2_Lef1_secondary uniprobe_secondary 2 Lef1 . -UP00068 UP00068_2_Eomes_secondary uniprobe_secondary 2 Eomes . -UP00069 UP00069_2_Sox1_secondary uniprobe_secondary 2 Sox1 . -UP00070 UP00070_2_Gcm1_secondary uniprobe_secondary 2 Gcm1 . -UP00071 UP00071_2_Sox21_secondary uniprobe_secondary 2 Sox21 . -UP00072 UP00072_2_IRC900814_secondary uniprobe_secondary 2 IRC900814 . -UP00073 UP00073_2_Foxa2_secondary uniprobe_secondary 2 Foxa2 . -UP00074 UP00074_2_Isgf3g_secondary uniprobe_secondary 2 Isgf3g . -UP00075 UP00075_2_Sox15_secondary uniprobe_secondary 2 Sox15 . -UP00076 UP00076_2_Rfxdc2_secondary uniprobe_secondary 2 Rfxdc2 . -UP00077 UP00077_2_Srf_secondary uniprobe_secondary 2 Srf . -UP00078 UP00078_2_Arid3a_secondary uniprobe_secondary 2 Arid3a . -UP00079 UP00079_2_Esrra_secondary uniprobe_secondary 2 Esrra . -UP00080 UP00080_2_Gata5_secondary uniprobe_secondary 2 Gata5 . -UP00081 UP00081_2_Mybl1_secondary uniprobe_secondary 2 Mybl1 . -UP00082 UP00082_2_Zfp187_secondary uniprobe_secondary 2 Zfp187 . -UP00083 UP00083_2_Tcf7l2_secondary uniprobe_secondary 2 Tcf7l2 . -UP00084 UP00084_2_Gmeb1_secondary uniprobe_secondary 2 Gmeb1 . -UP00085 UP00085_2_Sfpi1_secondary uniprobe_secondary 2 Sfpi1 . -UP00086 UP00086_2_Irf3_secondary uniprobe_secondary 2 Irf3 . -UP00087 UP00087_2_Tcfap2c_secondary uniprobe_secondary 2 Tcfap2c . -UP00088 UP00088_2_Plagl1_secondary uniprobe_secondary 2 Plagl1 . -UP00089 UP00089_2_Tcf1_secondary uniprobe_secondary 2 Tcf1 . -UP00089 UP00089_3_Tcf1_2666.2 uniprobe_secondary 3 Tcf1 . -UP00091 UP00091_2_Sox5_secondary uniprobe_secondary 2 Sox5 . -UP00092 UP00092_2_Myb_secondary uniprobe_secondary 2 Myb . -UP00093 UP00093_2_Klf7_secondary uniprobe_secondary 2 Klf7 . -UP00094 UP00094_2_Zfp128_secondary uniprobe_secondary 2 Zfp128 . -UP00095 UP00095_2_Zfp691_secondary uniprobe_secondary 2 Zfp691 . -UP00096 UP00096_2_Sox13_secondary uniprobe_secondary 2 Sox13 . -UP00097 UP00097_2_Mtf1_secondary uniprobe_secondary 2 Mtf1 . -UP00098 UP00098_2_Rfx3_secondary uniprobe_secondary 2 Rfx3 . -UP00099 UP00099_2_Ascl2_secondary uniprobe_secondary 2 Ascl2 . -UP00100 UP00100_2_Gata6_secondary uniprobe_secondary 2 Gata6 . -UP00101 UP00101_2_Sox12_secondary uniprobe_secondary 2 Sox12 . -UP00102 UP00102_2_Zic1_secondary uniprobe_secondary 2 Zic1 . -UP00103 UP00103_2_Jundm2_secondary uniprobe_secondary 2 Jundm2 . -UP00146 UP00146_2_Pou6f1_3733.1 uniprobe_secondary 2 Pou6f1 . -UP00164 UP00164_2_Hoxa7_3750.1 uniprobe_secondary 2 Hoxa7 . -UP00193 UP00193_2_Rhox11_2205.1 uniprobe_secondary 2 Rhox11 . -UP00200 UP00200_2_Nkx6-1_2825.2 uniprobe_secondary 2 Nkx6-1 . -UP00208 UP00208_2_Obox5_3963.2 uniprobe_secondary 2 Obox5 . -UP00209 UP00209_2_Cart1_1275.1 uniprobe_secondary 2 Cart1 . -UP00219 UP00219_2_Cutl1_3494.2 uniprobe_secondary 2 Cutl1 . -UP00223 UP00223_2_Irx3_2226.1 uniprobe_secondary 2 Irx3 . -UP00256 UP00256_2_Lhx6_3432.1 uniprobe_secondary 2 Lhx6 . -UP00391 UP00391_2_Hoxa3_secondary uniprobe_secondary 2 Hoxa3 . -UP00391 UP00391_3_Hoxa3_2783.2 uniprobe_secondary 3 Hoxa3 . -UP00406 UP00406_2_Spdef_secondary uniprobe_secondary 2 Spdef . -UP00407 UP00407_2_Elf3_secondary uniprobe_secondary 2 Elf3 . -UP00408 UP00408_2_Gabpa_secondary uniprobe_secondary 2 Gabpa . +UP00000 UP00000_2_Smad3_secondary uniprobe_secondary 2 Smad3 . . . . +UP00001 UP00001_2_E2F2_secondary uniprobe_secondary 2 E2F2 . . . . +UP00002 UP00002_2_Sp4_secondary uniprobe_secondary 2 Sp4 . . . . +UP00003 UP00003_2_E2F3_secondary uniprobe_secondary 2 E2F3 . . . . +UP00004 UP00004_2_Sox14_secondary uniprobe_secondary 2 Sox14 . . . . +UP00005 UP00005_2_Tcfap2a_secondary uniprobe_secondary 2 Tcfap2a . . . . +UP00006 UP00006_2_Zic3_secondary uniprobe_secondary 2 Zic3 . . . . +UP00007 UP00007_2_Egr1_secondary uniprobe_secondary 2 Egr1 . . . . +UP00008 UP00008_3_Six6_2267.4 uniprobe_secondary 3 Six6 . . . . +UP00008 UP00008_2_Six6_secondary uniprobe_secondary 2 Six6 . . . . +UP00008 UP00008_4_Six6 uniprobe_secondary 4 Six6 . . . . +UP00009 UP00009_2_Nr2f2_secondary uniprobe_secondary 2 Nr2f2 . . . . +UP00010 UP00010_2_Tcfap2b_secondary uniprobe_secondary 2 Tcfap2b . . . . +UP00011 UP00011_2_Irf6_secondary uniprobe_secondary 2 Irf6 . . . . +UP00012 UP00012_2_Bbx_secondary uniprobe_secondary 2 Bbx . . . . +UP00014 UP00014_2_Sox17_secondary uniprobe_secondary 2 Sox17 . . . . +UP00015 UP00015_2_Ehf_secondary uniprobe_secondary 2 Ehf . . . . +UP00016 UP00016_2_Sry_secondary uniprobe_secondary 2 Sry . . . . +UP00017 UP00017_3_Nkx3-1_2923.2 uniprobe_secondary 3 Nkx3-1 . . . . +UP00017 UP00017_2_Nkx3-1_secondary uniprobe_secondary 2 Nkx3-1 . . . . +UP00018 UP00018_2_Irf4_secondary uniprobe_secondary 2 Irf4 . . . . +UP00019 UP00019_2_Zbtb12_secondary uniprobe_secondary 2 Zbtb12 . . . . +UP00020 UP00020_2_Atf1_secondary uniprobe_secondary 2 Atf1 . . . . +UP00021 UP00021_2_Zfp281_secondary uniprobe_secondary 2 Zfp281 . . . . +UP00022 UP00022_2_Zfp740_secondary uniprobe_secondary 2 Zfp740 . . . . +UP00023 UP00023_2_Sox30_secondary uniprobe_secondary 2 Sox30 . . . . +UP00024 UP00024_2_Glis2_secondary uniprobe_secondary 2 Glis2 . . . . +UP00025 UP00025_2_Foxk1_secondary uniprobe_secondary 2 Foxk1 . . . . +UP00026 UP00026_2_Zscan4_secondary uniprobe_secondary 2 Zscan4 . . . . +UP00027 UP00027_2_Osr1_secondary uniprobe_secondary 2 Osr1 . . . . +UP00028 UP00028_2_Tcfap2e_secondary uniprobe_secondary 2 Tcfap2e . . . . +UP00029 UP00029_2_Tbp_secondary uniprobe_secondary 2 Tbp . . . . +UP00030 UP00030_2_Sox11_secondary uniprobe_secondary 2 Sox11 . . . . +UP00031 UP00031_2_Zbtb3_secondary uniprobe_secondary 2 Zbtb3 . . . . +UP00032 UP00032_2_Gata3_secondary uniprobe_secondary 2 Gata3 . . . . +UP00033 UP00033_2_Zfp410_secondary uniprobe_secondary 2 Zfp410 . . . . +UP00034 UP00034_2_Sox7_secondary uniprobe_secondary 2 Sox7 . . . . +UP00035 UP00035_2_Hic1_secondary uniprobe_secondary 2 Hic1 . . . . +UP00036 UP00036_2_Myf6_secondary uniprobe_secondary 2 Myf6 . . . . +UP00037 UP00037_2_Zfp105_secondary uniprobe_secondary 2 Zfp105 . . . . +UP00039 UP00039_2_Foxj3_secondary uniprobe_secondary 2 Foxj3 . . . . +UP00040 UP00040_2_Irf5_secondary uniprobe_secondary 2 Irf5 . . . . +UP00041 UP00041_2_Foxj1_secondary uniprobe_secondary 2 Foxj1 . . . . +UP00042 UP00042_2_Gm397_secondary uniprobe_secondary 2 Gm397 . . . . +UP00043 UP00043_2_Bcl6b_secondary uniprobe_secondary 2 Bcl6b . . . . +UP00044 UP00044_2_Mafk_secondary uniprobe_secondary 2 Mafk . . . . +UP00045 UP00045_2_Mafb_secondary uniprobe_secondary 2 Mafb . . . . +UP00046 UP00046_2_Tcfe2a_secondary uniprobe_secondary 2 Tcfe2a . . . . +UP00047 UP00047_2_Zbtb7b_secondary uniprobe_secondary 2 Zbtb7b . . . . +UP00048 UP00048_2_Rara_secondary uniprobe_secondary 2 Rara . . . . +UP00049 UP00049_2_Sp100_secondary uniprobe_secondary 2 Sp100 . . . . +UP00050 UP00050_2_Bhlhb2_secondary uniprobe_secondary 2 Bhlhb2 . . . . +UP00051 UP00051_2_Sox8_secondary uniprobe_secondary 2 Sox8 . . . . +UP00052 UP00052_2_Osr2_secondary uniprobe_secondary 2 Osr2 . . . . +UP00053 UP00053_2_Rxra_secondary uniprobe_secondary 2 Rxra . . . . +UP00054 UP00054_2_Tcf7_secondary uniprobe_secondary 2 Tcf7 . . . . +UP00055 UP00055_2_Hbp1_secondary uniprobe_secondary 2 Hbp1 . . . . +UP00056 UP00056_2_Rfx4_secondary uniprobe_secondary 2 Rfx4 . . . . +UP00057 UP00057_2_Zic2_secondary uniprobe_secondary 2 Zic2 . . . . +UP00058 UP00058_2_Tcf3_secondary uniprobe_secondary 2 Tcf3 . . . . +UP00059 UP00059_2_Arid5a_secondary uniprobe_secondary 2 Arid5a . . . . +UP00060 UP00060_2_Max_secondary uniprobe_secondary 2 Max . . . . +UP00061 UP00061_2_Foxl1_secondary uniprobe_secondary 2 Foxl1 . . . . +UP00062 UP00062_2_Sox4_secondary uniprobe_secondary 2 Sox4 . . . . +UP00064 UP00064_2_Sox18_secondary uniprobe_secondary 2 Sox18 . . . . +UP00065 UP00065_2_Zfp161_secondary uniprobe_secondary 2 Zfp161 . . . . +UP00066 UP00066_2_Hnf4a_secondary uniprobe_secondary 2 Hnf4a . . . . +UP00067 UP00067_2_Lef1_secondary uniprobe_secondary 2 Lef1 . . . . +UP00068 UP00068_2_Eomes_secondary uniprobe_secondary 2 Eomes . . . . +UP00069 UP00069_2_Sox1_secondary uniprobe_secondary 2 Sox1 . . . . +UP00070 UP00070_2_Gcm1_secondary uniprobe_secondary 2 Gcm1 . . . . +UP00071 UP00071_2_Sox21_secondary uniprobe_secondary 2 Sox21 . . . . +UP00072 UP00072_2_IRC900814_secondary uniprobe_secondary 2 IRC900814 . . . . +UP00073 UP00073_2_Foxa2_secondary uniprobe_secondary 2 Foxa2 . . . . +UP00074 UP00074_2_Isgf3g_secondary uniprobe_secondary 2 Isgf3g . . . . +UP00075 UP00075_2_Sox15_secondary uniprobe_secondary 2 Sox15 . . . . +UP00076 UP00076_2_Rfxdc2_secondary uniprobe_secondary 2 Rfxdc2 . . . . +UP00077 UP00077_2_Srf_secondary uniprobe_secondary 2 Srf . . . . +UP00078 UP00078_2_Arid3a_secondary uniprobe_secondary 2 Arid3a . . . . +UP00079 UP00079_2_Esrra_secondary uniprobe_secondary 2 Esrra . . . . +UP00080 UP00080_2_Gata5_secondary uniprobe_secondary 2 Gata5 . . . . +UP00081 UP00081_2_Mybl1_secondary uniprobe_secondary 2 Mybl1 . . . . +UP00082 UP00082_2_Zfp187_secondary uniprobe_secondary 2 Zfp187 . . . . +UP00083 UP00083_2_Tcf7l2_secondary uniprobe_secondary 2 Tcf7l2 . . . . +UP00084 UP00084_2_Gmeb1_secondary uniprobe_secondary 2 Gmeb1 . . . . +UP00085 UP00085_2_Sfpi1_secondary uniprobe_secondary 2 Sfpi1 . . . . +UP00086 UP00086_2_Irf3_secondary uniprobe_secondary 2 Irf3 . . . . +UP00087 UP00087_2_Tcfap2c_secondary uniprobe_secondary 2 Tcfap2c . . . . +UP00088 UP00088_2_Plagl1_secondary uniprobe_secondary 2 Plagl1 . . . . +UP00089 UP00089_3_Tcf1_2666.2 uniprobe_secondary 3 Tcf1 . . . . +UP00089 UP00089_2_Tcf1_secondary uniprobe_secondary 2 Tcf1 . . . . +UP00091 UP00091_2_Sox5_secondary uniprobe_secondary 2 Sox5 . . . . +UP00092 UP00092_2_Myb_secondary uniprobe_secondary 2 Myb . . . . +UP00093 UP00093_2_Klf7_secondary uniprobe_secondary 2 Klf7 . . . . +UP00094 UP00094_2_Zfp128_secondary uniprobe_secondary 2 Zfp128 . . . . +UP00095 UP00095_2_Zfp691_secondary uniprobe_secondary 2 Zfp691 . . . . +UP00096 UP00096_2_Sox13_secondary uniprobe_secondary 2 Sox13 . . . . +UP00097 UP00097_2_Mtf1_secondary uniprobe_secondary 2 Mtf1 . . . . +UP00098 UP00098_2_Rfx3_secondary uniprobe_secondary 2 Rfx3 . . . . +UP00099 UP00099_2_Ascl2_secondary uniprobe_secondary 2 Ascl2 . . . . +UP00100 UP00100_2_Gata6_secondary uniprobe_secondary 2 Gata6 . . . . +UP00101 UP00101_2_Sox12_secondary uniprobe_secondary 2 Sox12 . . . . +UP00102 UP00102_2_Zic1_secondary uniprobe_secondary 2 Zic1 . . . . +UP00103 UP00103_2_Jundm2_secondary uniprobe_secondary 2 Jundm2 . . . . +UP00146 UP00146_2_Pou6f1_3733.1 uniprobe_secondary 2 Pou6f1 . . . . +UP00164 UP00164_2_Hoxa7_3750.1 uniprobe_secondary 2 Hoxa7 . . . . +UP00193 UP00193_2_Rhox11_2205.1 uniprobe_secondary 2 Rhox11 . . . . +UP00200 UP00200_2_Nkx6-1_2825.2 uniprobe_secondary 2 Nkx6-1 . . . . +UP00208 UP00208_2_Obox5_3963.2 uniprobe_secondary 2 Obox5 . . . . +UP00209 UP00209_2_Cart1_1275.1 uniprobe_secondary 2 Cart1 . . . . +UP00219 UP00219_2_Cutl1_3494.2 uniprobe_secondary 2 Cutl1 . . . . +UP00223 UP00223_2_Irx3_2226.1 uniprobe_secondary 2 Irx3 . . . . +UP00256 UP00256_2_Lhx6_3432.1 uniprobe_secondary 2 Lhx6 . . . . +UP00391 UP00391_3_Hoxa3_2783.2 uniprobe_secondary 3 Hoxa3 . . . . +UP00391 UP00391_2_Hoxa3_secondary uniprobe_secondary 2 Hoxa3 . . . . +UP00406 UP00406_2_Spdef_secondary uniprobe_secondary 2 Spdef . . . . +UP00407 UP00407_2_Elf3_secondary uniprobe_secondary 2 Elf3 . . . . +UP00408 UP00408_2_Gabpa_secondary uniprobe_secondary 2 Gabpa . . . . +>>>>>>> develop diff --git a/rgt/ExperimentalMatrix.py b/rgt/ExperimentalMatrix.py index 06c1f57a4..1b1912c14 100644 --- a/rgt/ExperimentalMatrix.py +++ b/rgt/ExperimentalMatrix.py @@ -41,7 +41,7 @@ def __init__(self): self.objectsDict = {} self.trash = [] - def read(self, file_path, is_bedgraph=False, verbose=False, test=False, add_region_len=False): + def read(self, file_path, is_bedgraph=False, verbose=False, test=False, add_region_len=False, load_bed=True): """Read Experimental matrix file. *Keyword arguments:* @@ -173,6 +173,7 @@ def read(self, file_path, is_bedgraph=False, verbose=False, test=False, add_regi self.remove_name() self.load_bed_url(".") + self.load_bed = load_bed self.load_objects(is_bedgraph, verbose=verbose, test=test) if add_region_len: @@ -191,6 +192,12 @@ def get_regionsets(self): """Returns the RegionSets.""" return [self.objectsDict[n] for i, n in enumerate(self.names) if self.types[i] == "regions"] + def get_regionset(self, name): + """Returns the RegionSets.""" + r = GenomicRegionSet(name=name) + r.read(os.path.abspath(self.files[name])) + return r + def get_regionsnames(self): """Returns the region names.""" return [n for i, n in enumerate(self.names) if self.types[i] == "regions"] @@ -221,13 +228,14 @@ def load_objects(self, is_bedgraph, verbose=False, test=False): if t == "regions": regions = GenomicRegionSet(self.names[i]) - if is_bedgraph: - regions.read(os.path.abspath(self.files[self.names[i]]), io=GRSFileIO.BedGraph) - else: - regions.read(os.path.abspath(self.files[self.names[i]])) - regions.sort() - if test: - regions.sequences = regions.sequences[0:11] + if self.load_bed: + if is_bedgraph: + regions.read(os.path.abspath(self.files[self.names[i]]), io=GRSFileIO.BedGraph) + else: + regions.read(os.path.abspath(self.files[self.names[i]])) + regions.sort() + if test: + regions.sequences = regions.sequences[0:10] self.objectsDict[self.names[i]] = regions elif t == "genes": diff --git a/rgt/GenomicRegionSet.py b/rgt/GenomicRegionSet.py index 951d17c32..cf38d1569 100644 --- a/rgt/GenomicRegionSet.py +++ b/rgt/GenomicRegionSet.py @@ -1940,8 +1940,8 @@ def complement(self, organism, chrom_X=True, chrom_Y=False, chrom_M=False): """ g = GenomicRegionSet("complement_" + self.name) g.get_genome_data(organism, chrom_X, chrom_Y, chrom_M) - g.subtract(self) - return g + h = g.subtract(self) + return h def count_by_region(self, region): """Return the number of intersection regions with the given GenomicRegion. @@ -2610,14 +2610,20 @@ def map_names(self, target, strand=False, convert_nt=False): last_j = len(target) - 1 j = 0 cont_loop = True - # pre_j = 0 - - if convert_nt and ")n" not in target[0].name: + # pre_j = + # print(target[0].name) + if target[0].name: + if convert_nt and ")n" not in target[0].name: + convert_nt = False + elif target[0].name == None: convert_nt = False + # for r in target: + # r.name = "" while cont_loop: - # When the regions overlap - + # When the regions + if not target[j].name: + target[j].name = "." if s.overlap(target[j]): if strand: if s.orientation == target[j].orientation: @@ -2687,3 +2693,20 @@ def is_stranded(self): return True else: return False + + def fragmentize(self, size): + """Fragmentize each region by the given size""" + z = GenomicRegionSet(self.name) + for r in self: + l = len(r) + if l < size: + z.add(r) + else: + for i in range(int(l/size)): + ff = r.initial+(i+1)*size + if ff > r.final: + ff = r.final + g = GenomicRegion(chrom=r.chrom, initial=r.initial+i*size, + final=ff, name=r.name, orientation=r.orientation) + z.add(g) + return z diff --git a/rgt/HINT/DifferentialAnalysis.py b/rgt/HINT/DifferentialAnalysis.py index 8164cac0b..3e61ecd64 100644 --- a/rgt/HINT/DifferentialAnalysis.py +++ b/rgt/HINT/DifferentialAnalysis.py @@ -9,16 +9,18 @@ import matplotlib.pyplot as plt import pyx -from scipy import stats +from scipy.stats.mvn import mvnun from argparse import SUPPRESS +from multiprocessing import Pool, cpu_count + # Internal -from ..Util import ErrorHandler, AuxiliaryFunctions, GenomeData +from rgt.Util import ErrorHandler, AuxiliaryFunctions, GenomeData, HmmData from rgt.GenomicRegionSet import GenomicRegionSet -from biasTable import BiasTable +from rgt.HINT.biasTable import BiasTable """ -Perform differential footprints analysis based on the prediction. +Perform differential footprints analysis based on the prediction of transcription factor binding sites. Authors: Eduardo G. Gusmao, Zhijian Li """ @@ -39,10 +41,6 @@ def diff_analysis_args(parser): help="The BAM file containing the DNase-seq or ATAC-seq reads for condition 1. DEFAULT: None") parser.add_argument("--reads-file2", type=str, metavar="FILE", default=None, help="The BAM file containing the DNase-seq or ATAC-seq reads for condition 2. DEFAULT: None") - parser.add_argument("--bias-table1", type=str, metavar="FILE1_F,FILE1_R", default=None, - help="Bias table files for condition 1. DEFAULT: None") - parser.add_argument("--bias-table2", type=str, metavar="FILE2_F,FILE2_R", default=None, - help="Bias table files for condition 2. DEFAULT: None") parser.add_argument("--window-size", type=int, metavar="INT", default=200, help="The window size for differential analysis. DEFAULT: 200") @@ -53,23 +51,162 @@ def diff_analysis_args(parser): parser.add_argument("--forward-shift", type=int, metavar="INT", default=5, help=SUPPRESS) parser.add_argument("--reverse-shift", type=int, metavar="INT", default=-4, help=SUPPRESS) + parser.add_argument("--bias-table1", type=str, metavar="FILE1_F,FILE1_R", default=None, help=SUPPRESS) + parser.add_argument("--bias-table2", type=str, metavar="FILE2_F,FILE2_R", default=None, help=SUPPRESS) + parser.add_argument("--condition1", type=str, metavar="STRING", default="condition1", help="The name of condition1. DEFAULT: condition1") parser.add_argument("--condition2", type=str, metavar="STRING", default="condition1", help="The name of condition2. DEFAULT: condition2") + parser.add_argument("--fdr", type=float, metavar="FLOAT", default=0.05, + help="The false discovery rate. DEFAULT: 0.05") + parser.add_argument("--bc", action="store_true", default=False, + help="If set, all analysis will be based on bias corrected signal. DEFAULT: False") + parser.add_argument("--nc", type=int, metavar="INT", default=cpu_count(), + help="The number of cores. DEFAULT: 1") # Output Options parser.add_argument("--output-location", type=str, metavar="PATH", default=os.getcwd(), help="Path where the output bias table files will be written. DEFAULT: current directory") parser.add_argument("--output-prefix", type=str, metavar="STRING", default="differential", help="The prefix for results files. DEFAULT: differential") + parser.add_argument("--standardize", action="store_true", default=False, + help="If set, the signal will be rescaled to (0, 1) for plotting.") + parser.add_argument("--output-profiles", default=False, action='store_true', + help="If set, the footprint profiles will be writen into a text, in which each row is a " + "specific instance of the given motif. DEFAULT: False") + + +def get_raw_signal(arguments): + (mpbs_name, mpbs_file1, mpbs_file2, reads_file1, reads_file2, organism, + window_size, forward_shift, reverse_shift) = arguments + + mpbs1 = GenomicRegionSet("Motif Predicted Binding Sites of Condition1") + mpbs1.read(mpbs_file1) + + mpbs2 = GenomicRegionSet("Motif Predicted Binding Sites of Condition2") + mpbs2.read(mpbs_file2) + + mpbs = mpbs1.combine(mpbs2, output=True) + mpbs.sort() + + bam1 = Samfile(reads_file1, "rb") + bam2 = Samfile(reads_file2, "rb") + + genome_data = GenomeData(organism) + fasta = Fastafile(genome_data.get_genome()) + + signal_1 = np.zeros(window_size) + signal_2 = np.zeros(window_size) + motif_len = None + pwm = dict([("A", [0.0] * window_size), ("C", [0.0] * window_size), + ("G", [0.0] * window_size), ("T", [0.0] * window_size), + ("N", [0.0] * window_size)]) + + mpbs_regions = mpbs.by_names([mpbs_name]) + num_motif = len(mpbs_regions) + + for region in mpbs_regions: + if motif_len is None: + motif_len = region.final - region.initial + + mid = (region.final + region.initial) / 2 + p1 = mid - window_size / 2 + p2 = mid + window_size / 2 + + if p1 <= 0: + continue + + # Fetch raw signal + for read in bam1.fetch(region.chrom, p1, p2): + if not read.is_reverse: + cut_site = read.pos + forward_shift + if p1 <= cut_site < p2: + signal_1[cut_site - p1] += 1.0 + else: + cut_site = read.aend + reverse_shift - 1 + if p1 <= cut_site < p2: + signal_1[cut_site - p1] += 1.0 + + for read in bam2.fetch(region.chrom, p1, p2): + if not read.is_reverse: + cut_site = read.pos + forward_shift + if p1 <= cut_site < p2: + signal_2[cut_site - p1] += 1.0 + else: + cut_site = read.aend + reverse_shift - 1 + if p1 <= cut_site < p2: + signal_2[cut_site - p1] += 1.0 + update_pwm(pwm, fasta, region, p1, p2) + + return signal_1, signal_2, motif_len, pwm, num_motif + + +def get_bc_signal(arguments): + (mpbs_name, mpbs_file1, mpbs_file2, reads_file1, reads_file2, organism, + window_size, forward_shift, reverse_shift, bias_table1, bias_table2) = arguments + + mpbs1 = GenomicRegionSet("Motif Predicted Binding Sites of Condition1") + mpbs1.read(mpbs_file1) + + mpbs2 = GenomicRegionSet("Motif Predicted Binding Sites of Condition2") + mpbs2.read(mpbs_file2) + + mpbs = mpbs1.combine(mpbs2, output=True) + mpbs.sort() + + bam1 = Samfile(reads_file1, "rb") + bam2 = Samfile(reads_file2, "rb") + + genome_data = GenomeData(organism) + fasta = Fastafile(genome_data.get_genome()) + + signal_1 = np.zeros(window_size) + signal_2 = np.zeros(window_size) + motif_len = None + pwm = dict([("A", [0.0] * window_size), ("C", [0.0] * window_size), + ("G", [0.0] * window_size), ("T", [0.0] * window_size), + ("N", [0.0] * window_size)]) + + mpbs_regions = mpbs.by_names([mpbs_name]) + num_motif = len(mpbs_regions) + + # Fetch bias corrected signal + for region in mpbs_regions: + if motif_len is None: + motif_len = region.final - region.initial + + mid = (region.final + region.initial) / 2 + p1 = mid - window_size / 2 + p2 = mid + window_size / 2 + + if p1 <= 0: + continue + # Fetch raw signal + signal1 = bias_correction(chrom=region.chrom, start=p1, end=p2, bam=bam1, + bias_table=bias_table1, genome_file_name=genome_data.get_genome(), + forward_shift=forward_shift, reverse_shift=reverse_shift) + + signal2 = bias_correction(chrom=region.chrom, start=p1, end=p2, bam=bam2, + bias_table=bias_table2, genome_file_name=genome_data.get_genome(), + forward_shift=forward_shift, reverse_shift=reverse_shift) + + if len(signal1) != len(signal_1) or len(signal2) != len(signal_2): + continue + + signal_1 = np.add(signal_1, np.array(signal1)) + signal_2 = np.add(signal_2, np.array(signal2)) + + update_pwm(pwm, fasta, region, p1, p2) + + return signal_1, signal_2, motif_len, pwm, num_motif def diff_analysis_run(args): # Initializing Error Handler err = ErrorHandler() - output_location = os.path.join(args.output_location, "{}_{}".format(args.condition1, args.condition2)) + output_location = os.path.join(args.output_location, "Lineplots") try: if not os.path.isdir(output_location): os.makedirs(output_location) @@ -84,135 +221,78 @@ def diff_analysis_run(args): mpbs = mpbs1.combine(mpbs2, output=True) mpbs.sort() - mpbs_name_list = list(set(mpbs.get_names())) - genome_data = GenomeData(args.organism) - fasta = Fastafile(genome_data.get_genome()) - - bam1 = Samfile(args.reads_file1, "rb") - bam2 = Samfile(args.reads_file2, "rb") - signal_dict_by_tf_1 = dict() signal_dict_by_tf_2 = dict() motif_len_dict = dict() + motif_num_dict = dict() pwm_dict_by_tf = dict() - if args.bias_table1 is None or args.bias_table2 is None: - # differential analysis using raw reads number + pool = Pool(processes=args.nc) + # differential analysis using bias corrected signal + if args.bc: + hmm_data = HmmData() + table_F = hmm_data.get_default_bias_table_F_ATAC() + table_R = hmm_data.get_default_bias_table_R_ATAC() + bias_table1 = BiasTable().load_table(table_file_name_F=table_F, table_file_name_R=table_R) + bias_table2 = BiasTable().load_table(table_file_name_F=table_F, table_file_name_R=table_R) + + mpbs_list = list() for mpbs_name in mpbs_name_list: - signal_dict_by_tf_1[mpbs_name] = list() - signal_dict_by_tf_2[mpbs_name] = list() - pwm_dict_by_tf[mpbs_name] = dict([("A", [0.0] * args.window_size), ("C", [0.0] * args.window_size), - ("G", [0.0] * args.window_size), ("T", [0.0] * args.window_size), - ("N", [0.0] * args.window_size)]) - motif_len_dict[mpbs_name] = 0 - - mpbs_regions = mpbs.by_names([mpbs_name]) - for region in mpbs_regions: - if motif_len_dict[mpbs_name] == 0: - motif_len_dict[mpbs_name] = region.final - region.initial - - mid = (region.final + region.initial) / 2 - p1 = max(mid - args.window_size / 2, 0) - p2 = mid + args.window_size / 2 - - # Fetch raw signal - tc1 = np.zeros(args.window_size) - for read in bam1.fetch(region.chrom, p1, p2): - if not read.is_reverse: - cut_site = read.pos + args.forward_shift - if p1 <= cut_site < p2: - tc1[cut_site - p1] += 1.0 - else: - cut_site = read.aend + args.reverse_shift - 1 - if p1 <= cut_site < p2: - tc1[cut_site - p1] += 1.0 - signal_dict_by_tf_1[mpbs_name].append(tc1.tolist()) - - tc2 = np.zeros(args.window_size) - for read in bam2.fetch(region.chrom, p1, p2): - if not read.is_reverse: - cut_site = read.pos + args.forward_shift - if p1 <= cut_site < p2: - tc2[cut_site - p1] += 1.0 - else: - cut_site = read.aend + args.reverse_shift - 1 - if p1 <= cut_site < p2: - tc2[cut_site - p1] += 1.0 - signal_dict_by_tf_2[mpbs_name].append(tc2.tolist()) - update_pwm(pwm_dict_by_tf[mpbs_name], fasta, region, p1, p2) - else: - # using bias corrected signal - bias_table1 = None - bias_table2 = None - if args.bias_table1: - table_list = args.bias_table1.split(",") - bias_table1 = BiasTable().load_table(table_file_name_F=table_list[0], table_file_name_R=table_list[1]) - if args.bias_table2: - table_list = args.bias_table2.split(",") - bias_table2 = BiasTable().load_table(table_file_name_F=table_list[0], table_file_name_R=table_list[1]) + mpbs_list.append((mpbs_name, args.mpbs_file1, args.mpbs_file2, args.reads_file1, args.reads_file2, + args.organism, args.window_size, args.forward_shift, args.reverse_shift, + bias_table1, bias_table2)) + res = pool.map(get_bc_signal, mpbs_list) + # differential analysis using raw signal + else: + mpbs_list = list() for mpbs_name in mpbs_name_list: - signal_dict_by_tf_1[mpbs_name] = list() - signal_dict_by_tf_2[mpbs_name] = list() - pwm_dict_by_tf[mpbs_name] = dict([("A", [0.0] * args.window_size), ("C", [0.0] * args.window_size), - ("G", [0.0] * args.window_size), ("T", [0.0] * args.window_size), - ("N", [0.0] * args.window_size)]) - motif_len_dict[mpbs_name] = 0 - - mpbs_regions = mpbs.by_names([mpbs_name]) - for region in mpbs_regions: - if motif_len_dict[mpbs_name] == 0: - motif_len_dict[mpbs_name] = region.final - region.initial - - mid = (region.final + region.initial) / 2 - p1 = max(mid - args.window_size / 2, 0) - p2 = mid + args.window_size / 2 - - # Fetch bias corrected signal - signal_1 = get_bc_signal(chrom=region.chrom, start=p1, end=p2, bam=bam1, - bias_table=bias_table1, genome_file_name=genome_data.get_genome(), - forward_shift=args.forward_shift, reverse_shift=args.reverse_shift) - signal_dict_by_tf_1[mpbs_name].append(signal_1) - - signal_2 = get_bc_signal(chrom=region.chrom, start=p1, end=p2, bam=bam2, - bias_table=bias_table2, genome_file_name=genome_data.get_genome(), - forward_shift=args.forward_shift, reverse_shift=args.reverse_shift) - signal_dict_by_tf_2[mpbs_name].append(signal_2) - - update_pwm(pwm_dict_by_tf[mpbs_name], fasta, region, p1, p2) + mpbs_list.append((mpbs_name, args.mpbs_file1, args.mpbs_file2, args.reads_file1, args.reads_file2, + args.organism, args.window_size, args.forward_shift, args.reverse_shift)) + res = pool.map(get_raw_signal, mpbs_list) + + for idx, mpbs_name in enumerate(mpbs_name_list): + signal_dict_by_tf_1[mpbs_name] = res[idx][0] + signal_dict_by_tf_2[mpbs_name] = res[idx][1] + motif_len_dict[mpbs_name] = res[idx][2] + pwm_dict_by_tf[mpbs_name] = res[idx][3] + motif_num_dict[mpbs_name] = res[idx][4] if args.factor1 is None or args.factor2 is None: args.factor1, args.factor2 = compute_factors(signal_dict_by_tf_1, signal_dict_by_tf_2) output_factor(args, args.factor1, args.factor2) + if args.output_profiles: + output_profiles(mpbs_name_list, signal_dict_by_tf_1, output_location, args.condition1) + output_profiles(mpbs_name_list, signal_dict_by_tf_2, output_location, args.condition2) + ps_tc_results_by_tf = dict() + plots_list = list() for mpbs_name in mpbs_name_list: - num_fp = len(signal_dict_by_tf_1[mpbs_name]) - - # print the line plot for each factor - fig, ax = plt.subplots() - line_plot(args, err, mpbs_name, num_fp, signal_dict_by_tf_1[mpbs_name], signal_dict_by_tf_2[mpbs_name], - pwm_dict_by_tf[mpbs_name], fig, ax) - plt.close(fig) - - ps_tc_results_by_tf[mpbs_name] = list() - - for i in range(num_fp): - signal_1 = np.array(signal_dict_by_tf_1[mpbs_name][i]) / args.factor1 - signal_2 = np.array(signal_dict_by_tf_2[mpbs_name][i]) / args.factor2 - - res = get_ps_tc_results(signal_1, signal_2, motif_len_dict[mpbs_name]) - ps_tc_results_by_tf[mpbs_name].append(res) + plots_list.append((mpbs_name, motif_num_dict[mpbs_name], signal_dict_by_tf_1[mpbs_name], + signal_dict_by_tf_2[mpbs_name], args.factor1, args.factor2, args.condition1, + args.condition2, pwm_dict_by_tf[mpbs_name], output_location, args.window_size, + args.standardize)) - #stat_results_by_tf = get_stat_results(ps_tc_results_by_tf) - #scatter_plot(args, stat_results_by_tf) - #output_stat_results(args, stat_results_by_tf) + pool.map(line_plot, plots_list) - -def get_bc_signal(chrom, start, end, bam, bias_table, genome_file_name, forward_shift, reverse_shift): + for mpbs_name in mpbs_name_list: + res = get_ps_tc_results(signal_dict_by_tf_1[mpbs_name], signal_dict_by_tf_2[mpbs_name], + args.factor1, args.factor2, motif_num_dict[mpbs_name], motif_len_dict[mpbs_name]) + # + # # only use the factors whose protection scores are greater than 0 + # if res[0] > 0 and res[1] < 0: + ps_tc_results_by_tf[mpbs_name] = res + # + stat_results_by_tf = get_stat_results(ps_tc_results_by_tf) + scatter_plot(args, stat_results_by_tf) + output_stat_results(args, stat_results_by_tf) + + +def bias_correction(chrom, start, end, bam, bias_table, genome_file_name, forward_shift, reverse_shift): # Parameters window = 50 defaultKmerValue = 1.0 @@ -228,6 +308,20 @@ def get_bc_signal(chrom, start, end, bam, bias_table, genome_file_name, forward_ p2_w = p2 + (window / 2) p1_wk = p1_w - int(floor(k_nb / 2.)) p2_wk = p2_w + int(ceil(k_nb / 2.)) + if (p1 <= 0 or p1_w <= 0 or p2_wk <= 0): + # Return raw counts + bc_signal = [0.0] * (p2 - p1) + for read in bam.fetch(chrom, p1, p2): + if not read.is_reverse: + cut_site = read.pos + forward_shift + if p1 <= cut_site < p2: + bc_signal[cut_site - p1] += 1.0 + else: + cut_site = read.aend + reverse_shift - 1 + if p1 <= cut_site < p2: + bc_signal[cut_site - p1] += 1.0 + + return bc_signal # Raw counts nf = [0.0] * (p2_w - p1_w) @@ -300,7 +394,12 @@ def get_bc_signal(chrom, start, end, bam, bias_table, genome_file_name, forward_ return bc_signal -def get_ps_tc_results(signal_1, signal_2, motif_len): +def get_ps_tc_results(signal_1, signal_2, factor1, factor2, num_motif, motif_len): + signal_1 = (signal_1 / factor1) / num_motif + signal_2 = (signal_2 / factor2) / num_motif + + # signal_1, signal_2 = standard(signal_1, signal_2) + signal_half_len = len(signal_1) / 2 nc = sum(signal_1[signal_half_len - motif_len / 2:signal_half_len + motif_len / 2]) @@ -308,17 +407,17 @@ def get_ps_tc_results(signal_1, signal_2, motif_len): nl = sum(signal_1[signal_half_len - motif_len / 2 - motif_len:signal_half_len - motif_len / 2]) protect_score1 = (nr - nc) / motif_len + (nl - nc) / motif_len - tc1 = sum(signal_1) / len(signal_1) + tc1 = (sum(signal_1) - nc) / (len(signal_1) - motif_len) nc = sum(signal_2[signal_half_len - motif_len / 2:signal_half_len + motif_len / 2]) nr = sum(signal_2[signal_half_len + motif_len / 2:signal_half_len + motif_len / 2 + motif_len]) nl = sum(signal_2[signal_half_len - motif_len / 2 - motif_len:signal_half_len - motif_len / 2]) protect_score2 = (nr - nc) / motif_len + (nl - nc) / motif_len - tc2 = sum(signal_2) / len(signal_2) + tc2 = (sum(signal_2) - nc) / (len(signal_2) - motif_len) - protect_diff = protect_score1 - protect_score2 - tc_diff = tc1 - tc2 + protect_diff = protect_score2 - protect_score1 + tc_diff = tc2 - tc1 return [protect_score1, protect_score2, protect_diff, tc1, tc2, tc_diff] @@ -344,11 +443,9 @@ def compute_factors(signal_dict_by_tf_1, signal_dict_by_tf_2): signal_1 = np.zeros(len(keys)) signal_2 = np.zeros(len(keys)) - for key in keys: - for i in range(len(signal_dict_by_tf_1[key])): - idx = keys.index(key) - signal_1[idx] += sum(signal_dict_by_tf_1[key][i]) - signal_2[idx] += sum(signal_dict_by_tf_2[key][i]) + for idx, key in enumerate(keys): + signal_1[idx] = sum(signal_dict_by_tf_1[key]) + signal_2[idx] = sum(signal_dict_by_tf_2[key]) # Take log log_tc1 = np.log(signal_1) @@ -375,18 +472,17 @@ def compute_factors(signal_dict_by_tf_1, signal_dict_by_tf_2): return factor1, factor2 -def line_plot(args, err, mpbs_name, num_fp, signal_tf_1, signal_tf_2, pwm_dict, fig, ax): - # compute the average signal - mean_signal_1 = np.zeros(args.window_size) - mean_signal_2 = np.zeros(args.window_size) - for i in range(num_fp): - mean_signal_1 = np.add(mean_signal_1, signal_tf_1[i]) - mean_signal_2 = np.add(mean_signal_2, signal_tf_2[i]) +def line_plot(arguments): + (mpbs_name, num_fp, signal_1, signal_2, factor1, factor2, condition1, condition2, + pwm_dict, output_location, window_size, standardize) = arguments - mean_signal_1 = (mean_signal_1 / num_fp) / args.factor1 - mean_signal_2 = (mean_signal_2 / num_fp) / args.factor2 + mpbs_name = mpbs_name.replace("(", "_") + mpbs_name = mpbs_name.replace(")", "") + mean_signal_1 = (signal_1 / num_fp) / factor1 + mean_signal_2 = (signal_2 / num_fp) / factor2 - output_location = os.path.join(args.output_location, "{}_{}".format(args.condition1, args.condition2)) + if standardize: + mean_signal_1, mean_signal_2 = standard(mean_signal_1, mean_signal_2) # Output PWM and create logo pwm_fname = os.path.join(output_location, "{}.pwm".format(mpbs_name)) @@ -397,19 +493,19 @@ def line_plot(args, err, mpbs_name, num_fp, signal_tf_1, signal_tf_2, pwm_dict, logo_fname = os.path.join(output_location, "{}.logo.eps".format(mpbs_name)) pwm = motifs.read(open(pwm_fname), "pfm") - pwm.weblogo(logo_fname, format="eps", stack_width="large", stacks_per_line=str(args.window_size), + pwm.weblogo(logo_fname, format="eps", stack_width="large", stacks_per_line=str(window_size), color_scheme="color_classic", unit_name="", show_errorbars=False, logo_title="", show_xaxis=False, xaxis_label="", show_yaxis=False, yaxis_label="", show_fineprint=False, show_ends=False) - start = -(args.window_size / 2) - end = (args.window_size / 2) - 1 - x = np.linspace(start, end, num=args.window_size) - - #fig, ax = plt.subplots() + start = -(window_size / 2) + end = (window_size / 2) - 1 + x = np.linspace(start, end, num=window_size) - ax.plot(x, mean_signal_1, color='red', label=args.condition1) - ax.plot(x, mean_signal_2, color='blue', label=args.condition2) + plt.close('all') + fig, ax = plt.subplots() + ax.plot(x, mean_signal_1, color='red', label=condition1) + ax.plot(x, mean_signal_2, color='blue', label=condition2) ax.text(0.15, 0.9, 'n = {}'.format(num_fp), verticalalignment='bottom', horizontalalignment='right', transform=ax.transAxes, fontweight='bold') @@ -430,7 +526,7 @@ def line_plot(args, err, mpbs_name, num_fp, signal_tf_1, signal_tf_2, pwm_dict, ax.set_xlim(start, end) ax.set_ylim([min_signal, max_signal]) ax.legend(loc="upper right", frameon=False) - ax.spines['bottom'].set_position(('outward', 40)) + ax.spines['bottom'].set_position(('outward', 70)) figure_name = os.path.join(output_location, "{}.line.eps".format(mpbs_name)) fig.tight_layout() @@ -438,11 +534,12 @@ def line_plot(args, err, mpbs_name, num_fp, signal_tf_1, signal_tf_2, pwm_dict, # Creating canvas and printing eps / pdf with merged results output_fname = os.path.join(output_location, "{}.eps".format(mpbs_name)) + c = pyx.canvas.canvas() c.insert(pyx.epsfile.epsfile(0, 0, figure_name, scale=1.0)) c.insert(pyx.epsfile.epsfile(0.45, 0.8, logo_fname, width=16.5, height=3)) c.writeEPSfile(output_fname) - os.system("epstopdf " + output_fname) + os.system(" ".join(["epstopdf", output_fname])) os.remove(figure_name) os.remove(logo_fname) @@ -453,16 +550,20 @@ def line_plot(args, err, mpbs_name, num_fp, signal_tf_1, signal_tf_2, pwm_dict, def scatter_plot(args, stat_results_by_tf): tc_diff = list() ps_diff = list() - mpbs_names = list() - for mpbs_name in stat_results_by_tf.keys(): - mpbs_names.append(mpbs_name) - tc_diff.append(stat_results_by_tf[mpbs_name][-2]) - ps_diff.append(stat_results_by_tf[mpbs_name][2]) - - fig, ax = plt.subplots(figsize=(12,12)) - ax.scatter(tc_diff, ps_diff, alpha=0.0) - for i, txt in enumerate(mpbs_names): - ax.annotate(txt, (tc_diff[i], ps_diff[i]), alpha=0.6) + mpbs_name_list = stat_results_by_tf.keys() + P_values = list() + for mpbs_name in mpbs_name_list: + ps_diff.append(float(stat_results_by_tf[mpbs_name][2])) + tc_diff.append(float(stat_results_by_tf[mpbs_name][-3])) + P_values.append(np.log10(float(stat_results_by_tf[mpbs_name][-1]))) + + fig, ax = plt.subplots(figsize=(12, 12)) + for i, mpbs_name in enumerate(mpbs_name_list): + if stat_results_by_tf[mpbs_name][-1] < args.fdr: + ax.scatter(tc_diff[i], ps_diff[i], c="red") + ax.annotate(mpbs_name, (tc_diff[i], ps_diff[i]), alpha=0.6) + else: + ax.scatter(tc_diff[i], ps_diff[i], c="black", alpha=0.6) ax.margins(0.05) tc_diff_mean = np.mean(tc_diff) @@ -470,21 +571,36 @@ def scatter_plot(args, stat_results_by_tf): ax.axvline(x=tc_diff_mean, linewidth=2, linestyle='dashed') ax.axhline(y=ps_diff_mean, linewidth=2, linestyle='dashed') - ax.set_xlabel("TC DIFF of {} - {}".format(args.condition1, args.condition2), fontweight='bold') - ax.set_ylabel("Protection Score of {} - {}".format(args.condition1, args.condition2), fontweight='bold', rotation=90) + ax.set_xlabel("{} $\longrightarrow$ {} \n $\Delta$ Open Chromatin Score".format(args.condition1, args.condition2), + fontweight='bold', fontsize=20) + ax.set_ylabel("$\Delta$ Protection Score \n {} $\longrightarrow$ {}".format(args.condition1, args.condition2), + fontweight='bold', rotation=90, fontsize=20) figure_name = os.path.join(args.output_location, "{}_{}_statistics.pdf".format(args.condition1, args.condition2)) fig.savefig(figure_name, format="pdf", dpi=300) +def output_results(args, ps_tc_results_by_tf): + mpbs_name_list = ps_tc_results_by_tf.keys() + header = ["Motif", + "Protection_Score_{}".format(args.condition1), "Protection_Score_{}".format(args.condition2), + "Protection_Diff_{}_{}".format(args.condition1, args.condition2), + "TC_{}".format(args.condition1), "TC_{}".format(args.condition2), + "TC_Diff_{}_{}".format(args.condition1, args.condition2)] + output_fname = os.path.join(args.output_location, "{}_{}_results.txt".format(args.condition1, args.condition2)) + with open(output_fname, "w") as f: + f.write("\t".join(header) + "\n") + for mpbs_name in mpbs_name_list: + f.write(mpbs_name + "\t" + "\t".join(map(str, ps_tc_results_by_tf[mpbs_name])) + "\n") + + def output_stat_results(args, stat_results_by_tf): output_fname = os.path.join(args.output_location, "{}_{}_statistics.txt".format(args.condition1, args.condition2)) header = ["Motif", "Protection_Score_{}".format(args.condition1), "Protection_Score_{}".format(args.condition2), "Protection_Diff_{}_{}".format(args.condition1, args.condition2), "TC_{}".format(args.condition1), "TC_{}".format(args.condition2), - "TC_Diff_{}_{}".format(args.condition1, args.condition2), "P_values"] - + "TC_Diff_{}_{}".format(args.condition1, args.condition2), "P_values", "Adjust_p_values"] with open(output_fname, "w") as f: f.write("\t".join(header) + "\n") for mpbs_name in stat_results_by_tf.keys(): @@ -508,44 +624,66 @@ def output_mu(args, median_diff_prot, median_diff_tc): def get_stat_results(ps_tc_results_by_tf): - stat_results_by_tf = dict() - for mpbs_name in ps_tc_results_by_tf.keys(): - stat_results_by_tf[mpbs_name] = list() - mean_results = np.zeros(6) - num = 0 - for i in range(len(ps_tc_results_by_tf[mpbs_name])): - mean_results = np.add(mean_results, np.array(ps_tc_results_by_tf[mpbs_name][i])) - num += 1 - mean_results = mean_results / num - stat_results_by_tf[mpbs_name] = mean_results.tolist() - - ps_diff_mu = 0 - tc_diff_mu = 0 - num = 0 - for mpbs_name in stat_results_by_tf.keys(): - ps_diff_mu += stat_results_by_tf[mpbs_name][2] - tc_diff_mu += stat_results_by_tf[mpbs_name][-1] - num += 1 - - ps_diff_mu = ps_diff_mu / num - tc_diff_mu = tc_diff_mu / num - - mu = [tc_diff_mu, ps_diff_mu] - for mpbs_name in ps_tc_results_by_tf.keys(): - ps_diff_by_tf = list() - tc_diff_by_tf = list() - for i in range(len(ps_tc_results_by_tf[mpbs_name])): - ps_diff_by_tf.append(ps_tc_results_by_tf[mpbs_name][i][2]) - tc_diff_by_tf.append(ps_tc_results_by_tf[mpbs_name][i][-1]) - - X = np.array([ps_diff_by_tf, tc_diff_by_tf]).T - x = X - mu - n = x.shape[0] - k = x.shape[1] - m = x.mean(axis=0) # mean vector - S = np.cov(x.T) # covariance - t2 = n * np.dot(np.dot(m.T, np.linalg.inv(S)), m) - pvalue = stats.chi2.sf(t2, k) - stat_results_by_tf[mpbs_name].append(pvalue) - - return stat_results_by_tf + ps_diff = list() + tc_diff = list() + mpbs_name_list = ps_tc_results_by_tf.keys() + for mpbs_name in mpbs_name_list: + ps_diff.append(ps_tc_results_by_tf[mpbs_name][2]) + tc_diff.append(ps_tc_results_by_tf[mpbs_name][-1]) + + ps_tc_diff = np.array([ps_diff, tc_diff]).T + mu = np.mean(ps_tc_diff, axis=0) + cov_ps_tc_diff = np.cov(ps_tc_diff.T) + + low = np.zeros(2) + upp = np.zeros(2) + p_values = list() + for idx, mpbs_name in enumerate(mpbs_name_list): + if ps_diff[idx] >= mu[0]: + low[0] = ps_diff[idx] + upp[0] = float('inf') + else: + low[0] = -float('inf') + upp[0] = ps_diff[idx] + + if tc_diff[idx] >= mu[1]: + low[1] = tc_diff[idx] + upp[1] = float('inf') + else: + low[1] = -float('inf') + upp[1] = tc_diff[idx] + + p_value, i = mvnun(low, upp, mu, cov_ps_tc_diff) + ps_tc_results_by_tf[mpbs_name].append(p_value) + p_values.append(p_value) + + adjusted_p_values = adjust_p_values(p_values) + for idx, mpbs_name in enumerate(mpbs_name_list): + ps_tc_results_by_tf[mpbs_name].append(adjusted_p_values[idx]) + + return ps_tc_results_by_tf + + +def standard(vector1, vector2): + max_ = max(max(vector1), max(vector2)) + min_ = min(min(vector1), min(vector2)) + if max_ > min_: + return [(e - min_) / (max_ - min_) for e in vector1], [(e - min_) / (max_ - min_) for e in vector2] + else: + return vector1, vector2 + + +def adjust_p_values(p_values): + p = np.asfarray(p_values) + by_descend = p.argsort()[::-1] + by_orig = by_descend.argsort() + steps = float(len(p)) / np.arange(len(p), 0, -1) + q = np.minimum(1, np.minimum.accumulate(steps * p[by_descend])) + return q[by_orig] + + +def output_profiles(mpbs_name_list, signal_dict_by_tf, output_location, condition): + for mpbs_name in mpbs_name_list: + output_fname = os.path.join(output_location, "{}_{}.txt".format(mpbs_name, condition)) + with open(output_fname, "w") as f: + f.write("\t".join(map(str, signal_dict_by_tf[mpbs_name])) + "\n") diff --git a/rgt/HINT/Footprinting.py b/rgt/HINT/Footprinting.py index 8a501f093..d9ea7e10f 100644 --- a/rgt/HINT/Footprinting.py +++ b/rgt/HINT/Footprinting.py @@ -6,9 +6,9 @@ from rgt.Util import ErrorHandler, HmmData, GenomeData, OverlapType from rgt.GenomicRegion import GenomicRegion from rgt.GenomicRegionSet import GenomicRegionSet -from signalProcessing import GenomicSignal -from hmm import HMM, _compute_log_likelihood -from biasTable import BiasTable +from rgt.HINT.signalProcessing import GenomicSignal +from rgt.HINT.hmm import HMM, _compute_log_likelihood +from rgt.HINT.biasTable import BiasTable # External import types diff --git a/rgt/HINT/Main.py b/rgt/HINT/Main.py index 3461b989b..5fd2c2089 100644 --- a/rgt/HINT/Main.py +++ b/rgt/HINT/Main.py @@ -1,22 +1,20 @@ from __future__ import print_function - -# Internal -from rgt import __version__ -from Training import training_args, training_run -from Plotting import plotting_args, plotting_run -from DifferentialAnalysis import diff_analysis_args, diff_analysis_run -from Footprinting import footprinting_args, footprinting_run -from Estimation import estimation_args, estimation_run -from Evaluation import evaluation_args, evaluation_run -from Evidence import evidence_args, evidence_run -from Tracks import tracks_args, tracks_run - -# External import sys import time from random import seed from argparse import ArgumentParser +# Internal +from rgt import __version__ +from rgt.HINT.Training import training_args, training_run +from rgt.HINT.Plotting import plotting_args, plotting_run +from rgt.HINT.DifferentialAnalysis import diff_analysis_args, diff_analysis_run +from rgt.HINT.Footprinting import footprinting_args, footprinting_run +from rgt.HINT.Estimation import estimation_args, estimation_run +from rgt.HINT.Evaluation import evaluation_args, evaluation_run +from rgt.HINT.Evidence import evidence_args, evidence_run +from rgt.HINT.Tracks import tracks_args, tracks_run + """ HINT - HMM-based Identification of TF Footprints. diff --git a/rgt/HINT/Plotting.py b/rgt/HINT/Plotting.py index 7a5e343e6..e60bd71a0 100644 --- a/rgt/HINT/Plotting.py +++ b/rgt/HINT/Plotting.py @@ -5,22 +5,20 @@ import numpy as np from pysam import Samfile, Fastafile from Bio import motifs - import matplotlib - matplotlib.use('Agg') import matplotlib.pyplot as plt -import pyx -# Internal -from ..Util import GenomeData -from signalProcessing import GenomicSignal -from rgt.GenomicRegionSet import GenomicRegionSet -from biasTable import BiasTable -from ..Util import AuxiliaryFunctions from scipy.signal import savgol_filter from scipy.stats import scoreatpercentile from argparse import SUPPRESS +import pyx + +# Internal +from rgt.Util import GenomeData, AuxiliaryFunctions +from rgt.HINT.signalProcessing import GenomicSignal +from rgt.GenomicRegionSet import GenomicRegionSet +from rgt.HINT.biasTable import BiasTable def plotting_args(parser): @@ -56,6 +54,7 @@ def plotting_args(parser): # plot type parser.add_argument("--seq-logo", default=False, action='store_true') parser.add_argument("--bias-raw-bc-line", default=False, action='store_true') + parser.add_argument("--raw-bc-line", default=False, action='store_true') parser.add_argument("--strand-line", default=False, action='store_true') parser.add_argument("--unstrand-line", default=False, action='store_true') parser.add_argument("--bias-line", default=False, action='store_true') @@ -69,6 +68,8 @@ def plotting_run(args): bias_raw_bc_line(args) if args.strand_line: strand_line(args) + if args.raw_bc_line: + raw_bc_line(args) def seq_logo(args): @@ -175,13 +176,376 @@ def bias_raw_bc_line(args): mean_signal_bias_f = np.zeros(args.window_size) mean_signal_bias_r = np.zeros(args.window_size) mean_signal_raw = np.zeros(args.window_size) + mean_signal_raw_f = np.zeros(args.window_size) + mean_signal_raw_r = np.zeros(args.window_size) mean_signal_bc = np.zeros(args.window_size) + mean_signal_bc_f = np.zeros(args.window_size) + mean_signal_bc_r = np.zeros(args.window_size) + + motif_len = 0 + + for region in mpbs_regions: + #if str(region.name).split(":")[-1] == "Y": + # Extend by window_size + mid = (region.initial + region.final) / 2 + p1 = mid - (args.window_size / 2) + p2 = mid + (args.window_size / 2) + motif_len = region.final - region.initial + + signal_bias_f, signal_bias_r, raw, raw_f, raw_r, bc, bc_f, bc_r = \ + signal.get_bias_raw_bc_signal(ref=region.chrom, start=p1, end=p2, bam=bam, + fasta=fasta, bias_table=table, + forward_shift=args.forward_shift, + reverse_shift=args.reverse_shift, + strand=True) + + num_sites += 1 + mean_signal_bias_f = np.add(mean_signal_bias_f, np.array(signal_bias_f)) + mean_signal_bias_r = np.add(mean_signal_bias_r, np.array(signal_bias_r)) + mean_signal_raw = np.add(mean_signal_raw, np.array(raw)) + mean_signal_raw_f = np.add(mean_signal_raw_f, np.array(raw_f)) + mean_signal_raw_r = np.add(mean_signal_raw_r, np.array(raw_r)) + mean_signal_bc = np.add(mean_signal_bc, np.array(bc)) + mean_signal_bc_f = np.add(mean_signal_bc_f, np.array(bc_f)) + mean_signal_bc_r = np.add(mean_signal_bc_r, np.array(bc_r)) + + # Update pwm + aux_plus = 1 + dna_seq = str(fasta.fetch(region.chrom, p1, p2)).upper() + if (region.final - region.initial) % 2 == 0: + aux_plus = 0 + + if region.orientation == "+": + for i in range(len(dna_seq)): + pwm_dict[dna_seq[i]][i] += 1 + + mean_signal_bias_f = mean_signal_bias_f / num_sites + mean_signal_bias_r = mean_signal_bias_r / num_sites + mean_signal_raw = mean_signal_raw / num_sites + mean_signal_raw_f = mean_signal_raw_f / num_sites + mean_signal_raw_r = mean_signal_raw_r / num_sites + mean_signal_bc = mean_signal_bc / num_sites + mean_signal_bc_f = mean_signal_bc_f / num_sites + mean_signal_bc_r = mean_signal_bc_r / num_sites + + + # Output the norm and slope signal + output_fname = os.path.join(args.output_location, "{}.txt".format(args.output_prefix)) + f = open(output_fname, "w") + f.write("\t".join((map(str, mean_signal_bias_f))) + "\n") + f.write("\t".join((map(str, mean_signal_bias_r))) + "\n") + f.write("\t".join((map(str, mean_signal_raw))) + "\n") + f.write("\t".join((map(str, mean_signal_bc))) + "\n") + f.close() + + # Output PWM and create logo + pwm_fname = os.path.join(args.output_location, "{}.pwm".format(args.output_prefix)) + pwm_file = open(pwm_fname, "w") + for e in ["A", "C", "G", "T"]: + pwm_file.write(" ".join([str(int(f)) for f in pwm_dict[e]]) + "\n") + pwm_file.close() + + logo_fname = os.path.join(args.output_location, "{}.logo.eps".format(args.output_prefix)) + pwm = motifs.read(open(pwm_fname), "pfm") + + pwm.weblogo(logo_fname, format="eps", stack_width="large", stacks_per_line=str(args.window_size), + color_scheme="color_classic", unit_name="", show_errorbars=False, logo_title="", + show_xaxis=False, xaxis_label="", show_yaxis=False, yaxis_label="", + show_fineprint=False, show_ends=False) + + fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(8, 6)) + + start = -(args.window_size / 2) + end = (args.window_size / 2) - 1 + x = np.linspace(start, end, num=args.window_size) + + if motif_len % 2 == 0: + x1 = int(- (motif_len / 2)) + x2 = int(motif_len / 2) + else: + x1 = int(-(motif_len / 2) - 1) + x2 = int((motif_len / 2) + 1) + + ############################################################ + # bias signal per strand + fp_score = sum(mean_signal_raw[args.window_size / 2 + x1: args.window_size / 2 + x2]) + shoulder_l = sum(mean_signal_raw[args.window_size / 2 + x1 - motif_len:args.window_size / 2 + x1]) + shoulder_r = sum(mean_signal_raw[args.window_size / 2 + x2:args.window_size / 2 + x2 + motif_len]) + sfr = (shoulder_l + shoulder_r) / (2 * fp_score) + min_ax1 = min(mean_signal_raw) + max_ax1 = max(mean_signal_raw) + ax1.plot(x, mean_signal_raw, color='blue', label='Uncorrected') + ax1.text(0.15, 0.9, 'n = {}'.format(num_sites), verticalalignment='bottom', + horizontalalignment='right', transform=ax1.transAxes, fontweight='bold') + ax1.text(0.35, 0.15, 'SFR = {}'.format(round(sfr, 2)), verticalalignment='bottom', + horizontalalignment='right', transform=ax1.transAxes, fontweight='bold') + ax1.xaxis.set_ticks_position('bottom') + ax1.yaxis.set_ticks_position('left') + ax1.spines['top'].set_visible(False) + ax1.spines['right'].set_visible(False) + ax1.spines['left'].set_position(('outward', 15)) + ax1.spines['bottom'].set_position(('outward', 5)) + ax1.tick_params(direction='out') + ax1.set_xticks([start, 0, end]) + ax1.set_xticklabels([str(start), 0, str(end)]) + ax1.set_yticks([min_ax1, max_ax1]) + ax1.set_yticklabels([str(round(min_ax1, 2)), str(round(max_ax1, 2))], rotation=90) + ax1.set_title(args.output_prefix, fontweight='bold') + ax1.set_xlim(start, end) + ax1.set_ylim([min_ax1, max_ax1]) + ax1.legend(loc="lower right", frameon=False) + #################################################################### + + ##################################################################### + # Bias corrected, non-bias corrected (not strand specific) + fp_score = sum(mean_signal_bc[args.window_size / 2 + x1: args.window_size / 2 + x2]) + shoulder_l = sum(mean_signal_bc[args.window_size / 2 + x1 - motif_len:args.window_size / 2 + x1]) + shoulder_r = sum(mean_signal_bc[args.window_size / 2 + x2:args.window_size / 2 + x2 + motif_len]) + sfr = (shoulder_l + shoulder_r) / (2 * fp_score) + min_ax2 = min(mean_signal_bc) + max_ax2 = max(mean_signal_bc) + ax2.plot(x, mean_signal_bc, color='red', label='Corrected') + ax2.text(0.35, 0.15, 'SFR = {}'.format(round(sfr, 2)), verticalalignment='bottom', + horizontalalignment='right', transform=ax2.transAxes, fontweight='bold') + ax2.xaxis.set_ticks_position('bottom') + ax2.yaxis.set_ticks_position('left') + ax2.spines['top'].set_visible(False) + ax2.spines['right'].set_visible(False) + ax2.spines['left'].set_position(('outward', 15)) + ax2.tick_params(direction='out') + ax2.set_xticks([start, 0, end]) + ax2.set_xticklabels([str(start), 0, str(end)]) + ax2.set_yticks([min_ax2, max_ax2]) + ax2.set_yticklabels([str(round(min_ax2, 2)), str(round(max_ax2, 2))], rotation=90) + ax2.set_xlim(start, end) + ax2.set_ylim([min_ax2, max_ax2]) + ax2.legend(loc="lower right", frameon=False) + + fp_score_f = sum(mean_signal_bc_f[args.window_size / 2 + x1: args.window_size / 2 + x2]) + shoulder_l_f = sum(mean_signal_bc_f[args.window_size / 2 + x1 - motif_len:args.window_size / 2 + x1]) + shoulder_r_f = sum(mean_signal_bc_f[args.window_size / 2 + x2:args.window_size / 2 + x2 + motif_len]) + sfr_f = (shoulder_l_f + shoulder_r_f) / (2 * fp_score_f) + fp_score_r = sum(mean_signal_bc_r[args.window_size / 2 + x1: args.window_size / 2 + x2]) + shoulder_l_r = sum(mean_signal_bc_r[args.window_size / 2 + x1 - motif_len:args.window_size / 2 + x1]) + shoulder_r_r = sum(mean_signal_bc_r[args.window_size / 2 + x2:args.window_size / 2 + x2 + motif_len]) + sfr_r = (shoulder_l_r + shoulder_r_r) / (2 * fp_score_r) + min_ax3 = min(min(mean_signal_bc_f), min(mean_signal_bc_r)) + max_ax3 = max(max(mean_signal_bc_f), max(mean_signal_bc_r)) + ax3.plot(x, mean_signal_bc_f, color='purple', label='Forward') + ax3.plot(x, mean_signal_bc_r, color='green', label='Reverse') + ax3.text(0.35, 0.15, 'SFR_f = {}'.format(round(sfr_f, 2)), verticalalignment='bottom', + horizontalalignment='right', transform=ax3.transAxes, fontweight='bold') + ax3.text(0.35, 0.05, 'SFR_r = {}'.format(round(sfr_r, 2)), verticalalignment='bottom', + horizontalalignment='right', transform=ax3.transAxes, fontweight='bold') + ax3.xaxis.set_ticks_position('bottom') + ax3.yaxis.set_ticks_position('left') + ax3.spines['top'].set_visible(False) + ax3.spines['right'].set_visible(False) + ax3.spines['left'].set_position(('outward', 15)) + ax3.tick_params(direction='out') + ax3.set_xticks([start, 0, end]) + ax3.set_xticklabels([str(start), 0, str(end)]) + ax3.set_yticks([min_ax3, max_ax3]) + ax3.set_yticklabels([str(round(min_ax3, 2)), str(round(max_ax3, 2))], rotation=90) + ax3.set_xlim(start, end) + ax3.set_ylim([min_ax3, max_ax3]) + ax3.legend(loc="lower right", frameon=False) + + ax3.spines['bottom'].set_position(('outward', 40)) + + ax1.axvline(x=x1, ymin=-0.3, ymax=1, c="black", lw=0.5, ls='dashed', zorder=0, clip_on=False) + ax1.axvline(x=x2, ymin=-0.3, ymax=1, c="black", lw=0.5, ls='dashed', zorder=0, clip_on=False) + ax2.axvline(x=x1, ymin=-0.5, ymax=1.2, c="black", lw=0.5, ls='dashed', zorder=0, clip_on=False) + ax2.axvline(x=x2, ymin=-0.5, ymax=1.2, c="black", lw=0.5, ls='dashed', zorder=0, clip_on=False) + ############################################################################### + # merge the above figures + figure_name = os.path.join(args.output_location, "{}.line.eps".format(args.output_prefix)) + fig.subplots_adjust(bottom=.2, hspace=.5) + fig.tight_layout() + fig.savefig(figure_name, format="eps", dpi=300) + + # Creating canvas and printing eps / pdf with merged results + output_fname = os.path.join(args.output_location, "{}.eps".format(args.output_prefix)) + c = pyx.canvas.canvas() + c.insert(pyx.epsfile.epsfile(0, 0, figure_name, scale=1.0)) + c.insert(pyx.epsfile.epsfile(1.45, 0.89, logo_fname, width=18.3, height=1.75)) + c.writeEPSfile(output_fname) + os.system("epstopdf " + figure_name) + os.system("epstopdf " + logo_fname) + os.system("epstopdf " + output_fname) + + os.remove(pwm_fname) + os.remove(os.path.join(args.output_location, "{}.line.eps".format(args.output_prefix))) + os.remove(os.path.join(args.output_location, "{}.logo.eps".format(args.output_prefix))) + os.remove(os.path.join(args.output_location, "{}.line.pdf".format(args.output_prefix))) + os.remove(os.path.join(args.output_location, "{}.logo.pdf".format(args.output_prefix))) + os.remove(os.path.join(args.output_location, "{}.eps".format(args.output_prefix))) + + +def raw_bc_line(args): + signal = GenomicSignal(args.reads_file) + signal.load_sg_coefs(slope_window_size=9) + bias_table = BiasTable() + bias_table_list = args.bias_table.split(",") + table = bias_table.load_table(table_file_name_F=bias_table_list[0], + table_file_name_R=bias_table_list[1]) + + genome_data = GenomeData(args.organism) + fasta = Fastafile(genome_data.get_genome()) + pwm_dict = dict([("A", [0.0] * args.window_size), ("C", [0.0] * args.window_size), + ("G", [0.0] * args.window_size), ("T", [0.0] * args.window_size), + ("N", [0.0] * args.window_size)]) + + num_sites = 0 + + mpbs_regions = GenomicRegionSet("Motif Predicted Binding Sites") + mpbs_regions.read(args.motif_file) + + bam = Samfile(args.reads_file, "rb") + + mean_signal_raw = np.zeros(args.window_size) + mean_signal_bc = np.zeros(args.window_size) + for region in mpbs_regions: if str(region.name).split(":")[-1] == "Y": # Extend by window_size mid = (region.initial + region.final) / 2 p1 = mid - (args.window_size / 2) p2 = mid + (args.window_size / 2) + signal_bias_f, signal_bias_r, raw, raw_f, raw_r, bc, bc_f, bc_r = \ + signal.get_bias_raw_bc_signal(ref=region.chrom, start=p1, end=p2, bam=bam, + fasta=fasta, bias_table=table, + forward_shift=args.forward_shift, + reverse_shift=args.reverse_shift, + strand=True) + + num_sites += 1 + mean_signal_raw = np.add(mean_signal_raw, np.array(raw)) + mean_signal_bc = np.add(mean_signal_bc, np.array(bc)) + + # Update pwm + aux_plus = 1 + dna_seq = str(fasta.fetch(region.chrom, p1, p2)).upper() + if (region.final - region.initial) % 2 == 0: + aux_plus = 0 + + if region.orientation == "+": + for i in range(len(dna_seq)): + pwm_dict[dna_seq[i]][i] += 1 + + mean_signal_raw = mean_signal_raw / num_sites + mean_signal_bc = mean_signal_bc / num_sites + + # Output the norm and slope signal + output_fname = os.path.join(args.output_location, "{}.txt".format(args.output_prefix)) + f = open(output_fname, "w") + f.write("\t".join((map(str, mean_signal_raw))) + "\n") + f.write("\t".join((map(str, mean_signal_bc))) + "\n") + f.close() + + # Output PWM and create logo + pwm_fname = os.path.join(args.output_location, "{}.pwm".format(args.output_prefix)) + pwm_file = open(pwm_fname, "w") + for e in ["A", "C", "G", "T"]: + pwm_file.write(" ".join([str(int(f)) for f in pwm_dict[e]]) + "\n") + pwm_file.close() + + logo_fname = os.path.join(args.output_location, "{}.logo.eps".format(args.output_prefix)) + pwm = motifs.read(open(pwm_fname), "pfm") + + pwm.weblogo(logo_fname, format="eps", stack_width="large", stacks_per_line=str(args.window_size), + color_scheme="color_classic", unit_name="", show_errorbars=False, logo_title="", + show_xaxis=False, xaxis_label="", show_yaxis=False, yaxis_label="", + show_fineprint=False, show_ends=False) + + fig = plt.figure(figsize=(8, 4)) + ax = fig.add_subplot(111) + + start = -(args.window_size / 2) + end = (args.window_size / 2) - 1 + x = np.linspace(start, end, num=args.window_size) + + ############################################################ + min_ = min(min(mean_signal_raw), min(mean_signal_bc)) + max_ = max(max(mean_signal_raw), max(mean_signal_bc)) + ax.plot(x, mean_signal_raw, color='red', label='Uncorrected') + ax.plot(x, mean_signal_bc, color='blue', label='Corrected') + ax.text(0.15, 0.9, 'n = {}'.format(num_sites), verticalalignment='bottom', + horizontalalignment='right', transform=ax.transAxes, fontweight='bold') + ax.xaxis.set_ticks_position('bottom') + ax.yaxis.set_ticks_position('left') + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + ax.spines['left'].set_position(('outward', 15)) + ax.spines['bottom'].set_position(('outward', 5)) + ax.tick_params(direction='out') + ax.set_xticks([start, 0, end]) + ax.set_xticklabels([str(start), 0, str(end)]) + ax.set_yticks([min_, max_]) + ax.set_yticklabels([str(round(min_, 2)), str(round(max_, 2))], rotation=90) + ax.set_title(args.output_prefix, fontweight='bold') + ax.set_xlim(start, end) + ax.set_ylim(min_, max_) + ax.legend(loc="lower right", frameon=False) + + ax.spines['bottom'].set_position(('outward', 40)) + + ############################################################################### + # merge the above figures + figure_name = os.path.join(args.output_location, "{}.line.eps".format(args.output_prefix)) + fig.subplots_adjust(bottom=.2, hspace=.5) + fig.tight_layout() + fig.savefig(figure_name, format="eps", dpi=300) + + # Creating canvas and printing eps / pdf with merged results + output_fname = os.path.join(args.output_location, "{}.eps".format(args.output_prefix)) + c = pyx.canvas.canvas() + c.insert(pyx.epsfile.epsfile(0, 0, figure_name, scale=1.0)) + c.insert(pyx.epsfile.epsfile(1.45, 0.89, logo_fname, width=18.3, height=1.75)) + c.writeEPSfile(output_fname) + os.system("epstopdf " + figure_name) + os.system("epstopdf " + logo_fname) + os.system("epstopdf " + output_fname) + + os.remove(pwm_fname) + os.remove(os.path.join(args.output_location, "{}.line.eps".format(args.output_prefix))) + os.remove(os.path.join(args.output_location, "{}.logo.eps".format(args.output_prefix))) + os.remove(os.path.join(args.output_location, "{}.line.pdf".format(args.output_prefix))) + os.remove(os.path.join(args.output_location, "{}.logo.pdf".format(args.output_prefix))) + os.remove(os.path.join(args.output_location, "{}.eps".format(args.output_prefix))) + + +def bias_raw_bc_strand_line(args): + signal = GenomicSignal(args.reads_file) + signal.load_sg_coefs(slope_window_size=9) + bias_table = BiasTable() + bias_table_list = args.bias_table.split(",") + table = bias_table.load_table(table_file_name_F=bias_table_list[0], + table_file_name_R=bias_table_list[1]) + + genome_data = GenomeData(args.organism) + fasta = Fastafile(genome_data.get_genome()) + pwm_dict = dict([("A", [0.0] * args.window_size), ("C", [0.0] * args.window_size), + ("G", [0.0] * args.window_size), ("T", [0.0] * args.window_size), + ("N", [0.0] * args.window_size)]) + + num_sites = 0 + + mpbs_regions = GenomicRegionSet("Motif Predicted Binding Sites") + mpbs_regions.read(args.motif_file) + + bam = Samfile(args.reads_file, "rb") + + mean_signal_bias_f = np.zeros(args.window_size) + mean_signal_bias_r = np.zeros(args.window_size) + mean_signal_raw = np.zeros(args.window_size) + mean_signal_bc = np.zeros(args.window_size) + for region in mpbs_regions: + if True: + #if str(region.name).split(":")[-1] == "Y": + mid = (region.initial + region.final) / 2 + p1 = mid - (args.window_size / 2) + p2 = mid + (args.window_size / 2) signal_bias_f, signal_bias_r, signal_raw, signal_bc = \ signal.get_bias_raw_bc_signal(ref=region.chrom, start=p1, end=p2, bam=bam, @@ -204,17 +568,12 @@ def bias_raw_bc_line(args): if region.orientation == "+": for i in range(0, len(dna_seq)): pwm_dict[dna_seq[i]][i] += 1 - elif region.orientation == "-": - for i in range(0, len(dna_seq_rev)): - pwm_dict[dna_seq_rev[i]][i] += 1 mean_signal_bias_f = mean_signal_bias_f / num_sites mean_signal_bias_r = mean_signal_bias_r / num_sites mean_signal_raw = mean_signal_raw / num_sites mean_signal_bc = mean_signal_bc / num_sites - # mean_signal_raw = self.rescaling(mean_signal_raw) - # mean_signal_bc = self.rescaling(mean_signal_bc) # Output the norm and slope signal output_fname = os.path.join(args.output_location, "{}.txt".format(args.output_prefix)) @@ -252,6 +611,8 @@ def bias_raw_bc_line(args): max_ = max(max(mean_signal_bias_f), max(mean_signal_bias_r)) ax1.plot(x, mean_signal_bias_f, color='purple', label='Forward') ax1.plot(x, mean_signal_bias_r, color='green', label='Reverse') + ax1.text(0.15, 0.9, 'n = {}'.format(num_sites), verticalalignment='bottom', + horizontalalignment='right', transform=ax1.transAxes, fontweight='bold') ax1.xaxis.set_ticks_position('bottom') ax1.yaxis.set_ticks_position('left') ax1.spines['top'].set_visible(False) @@ -471,6 +832,13 @@ def strand_line(args): os.remove(os.path.join(args.output_location, "{}.eps".format(args.output_prefix))) +def rescaling(vector): + _sum = sum(vector) + #maxN = max(vector) + #minN = min(vector) + return [e / _sum for e in vector] + + class Plot: def __init__(self, organism, reads_file, motif_file, window_size, diff --git a/rgt/HINT/Tracks.py b/rgt/HINT/Tracks.py index d5cc5b857..02df6c007 100644 --- a/rgt/HINT/Tracks.py +++ b/rgt/HINT/Tracks.py @@ -2,12 +2,13 @@ from argparse import SUPPRESS import numpy as np from pysam import Samfile, Fastafile +from scipy.stats import scoreatpercentile # Internal -from ..Util import GenomeData, HmmData, ErrorHandler -from ..GenomicRegionSet import GenomicRegionSet -from biasTable import BiasTable -from signalProcessing import GenomicSignal +from rgt.Util import GenomeData, HmmData, ErrorHandler +from rgt.GenomicRegionSet import GenomicRegionSet +from rgt.HINT.biasTable import BiasTable +from rgt.HINT.signalProcessing import GenomicSignal def tracks_args(parser): @@ -65,34 +66,42 @@ def get_raw_tracks(args): if len(args.input_files) != 2: err.throw_error("ME_FEW_ARG", add_msg="You must specify reads and regions file.") - output_fname = os.path.join(args.output_location, "{}.raw.wig".format(args.output_prefix)) + output_fname = os.path.join(args.output_location, "{}.wig".format(args.output_prefix)) bam = Samfile(args.input_files[0], "rb") regions = GenomicRegionSet("Interested regions") regions.read(args.input_files[1]) + regions.merge() + reads_file = GenomicSignal() with open(output_fname, "a") as output_f: for region in regions: # Raw counts - raw_signal = [0.0] * (region.final - region.initial) + signal = [0.0] * (region.final - region.initial) for read in bam.fetch(region.chrom, region.initial, region.final): if not read.is_reverse: cut_site = read.pos + args.forward_shift if region.initial <= cut_site < region.final: - raw_signal[cut_site - region.initial] += 1.0 + signal[cut_site - region.initial] += 1.0 else: cut_site = read.aend + args.reverse_shift - 1 if region.initial <= cut_site < region.final: - raw_signal[cut_site - region.initial] += 1.0 + signal[cut_site - region.initial] += 1.0 + + if args.norm: + signal = reads_file.boyle_norm(signal) + perc = scoreatpercentile(signal, 98) + std = np.std(signal) + signal = reads_file.hon_norm_atac(signal, perc, std) output_f.write("fixedStep chrom=" + region.chrom + " start=" + str(region.initial + 1) + " step=1\n" + - "\n".join([str(e) for e in np.nan_to_num(raw_signal)]) + "\n") + "\n".join([str(e) for e in np.nan_to_num(signal)]) + "\n") output_f.close() if args.bigWig: genome_data = GenomeData(args.organism) chrom_sizes_file = genome_data.get_chromosome_sizes() - bw_filename = os.path.join(args.output_location, "{}.raw.bw".format(args.output_prefix)) + bw_filename = os.path.join(args.output_location, "{}.bw".format(args.output_prefix)) os.system(" ".join(["wigToBigWig", output_fname, chrom_sizes_file, bw_filename, "-verbose=0"])) os.remove(output_fname) @@ -104,9 +113,10 @@ def get_bc_tracks(args): if len(args.input_files) != 2: err.throw_error("ME_FEW_ARG", add_msg="You must specify reads and regions file.") - output_fname = os.path.join(args.output_location, "{}.bc.wig".format(args.output_prefix)) + output_fname = os.path.join(args.output_location, "{}.wig".format(args.output_prefix)) regions = GenomicRegionSet("Interested regions") regions.read(args.input_files[1]) + regions.merge() reads_file = GenomicSignal() @@ -127,20 +137,26 @@ def get_bc_tracks(args): with open(output_fname, "a") as output_f: for region in regions: - bc_signal = reads_file.get_bc_signal_by_fragment_length(ref=region.chrom, start=region.initial, + signal = reads_file.get_bc_signal_by_fragment_length(ref=region.chrom, start=region.initial, end=region.final, bam=bam, fasta=fasta, bias_table=bias_table, forward_shift=args.forward_shift, reverse_shift=args.reverse_shift, min_length=None, max_length=None, strand=False) + if args.norm: + signal = reads_file.boyle_norm(signal) + perc = scoreatpercentile(signal, 98) + std = np.std(signal) + signal = reads_file.hon_norm_atac(signal, perc, std) + output_f.write("fixedStep chrom=" + region.chrom + " start=" + str(region.initial + 1) + " step=1\n" + - "\n".join([str(e) for e in np.nan_to_num(bc_signal)]) + "\n") + "\n".join([str(e) for e in np.nan_to_num(signal)]) + "\n") output_f.close() if args.bigWig: genome_data = GenomeData(args.organism) chrom_sizes_file = genome_data.get_chromosome_sizes() - bw_filename = os.path.join(args.output_location, "{}.bc.bw".format(args.output_prefix)) + bw_filename = os.path.join(args.output_location, "{}.bw".format(args.output_prefix)) os.system(" ".join(["wigToBigWig", output_fname, chrom_sizes_file, bw_filename, "-verbose=0"])) os.remove(output_fname) diff --git a/rgt/HINT/Training.py b/rgt/HINT/Training.py index f43110f27..b4db78e7c 100644 --- a/rgt/HINT/Training.py +++ b/rgt/HINT/Training.py @@ -3,13 +3,13 @@ from collections import Counter from sklearn.externals import joblib from scipy import linalg +from argparse import SUPPRESS # Internal -from ..Util import GenomeData -from signalProcessing import GenomicSignal -from hmm import HMM, SemiSupervisedGaussianHMM -from biasTable import BiasTable -from argparse import SUPPRESS +from rgt.Util import GenomeData +from rgt.HINT.signalProcessing import GenomicSignal +from rgt.HINT.hmm import HMM, SemiSupervisedGaussianHMM +from rgt.HINT.biasTable import BiasTable """ Train a hidden Markov model (HMM) based on the annotation data diff --git a/rgt/HINT/__init__.py b/rgt/HINT/__init__.py index 59e4d5963..a9a2c5b3b 100644 --- a/rgt/HINT/__init__.py +++ b/rgt/HINT/__init__.py @@ -1 +1 @@ -__all__ = ["biasTable", "hmm", "Main", "pileupRegion", "signalProcessing"] +__all__ = [] diff --git a/rgt/HINT/biasTable.py b/rgt/HINT/biasTable.py index 0792bc8cb..444b6336b 100644 --- a/rgt/HINT/biasTable.py +++ b/rgt/HINT/biasTable.py @@ -1,12 +1,8 @@ ################################################################################################### # Libraries ################################################################################################### - -# Python - from rgt.GenomicRegionSet import GenomicRegionSet - class BiasTable: """ Represent a bias table. diff --git a/rgt/HINT/signalProcessing.py b/rgt/HINT/signalProcessing.py index 27c9177a4..568a3d32c 100644 --- a/rgt/HINT/signalProcessing.py +++ b/rgt/HINT/signalProcessing.py @@ -1,21 +1,16 @@ ################################################################################################### # Libraries ################################################################################################### - -# Python - from math import log, ceil, floor, isnan - -# External import numpy as np from numpy import exp, array, abs, int, mat, linalg, convolve, nan_to_num from pysam import Samfile, Fastafile from pysam import __version__ as ps_version from scipy.stats import scoreatpercentile -from .pileupRegion import PileupRegion # Internal -from ..Util import AuxiliaryFunctions +from rgt.Util import AuxiliaryFunctions +from rgt.HINT.pileupRegion import PileupRegion """ Processes DNase-seq and histone modification signal for @@ -337,7 +332,21 @@ def bias_correction_atac(self, bias_table, genome_file_name, chrName, start, end p2_w = p2 + (window / 2) p1_wk = p1_w - int(floor(k_nb / 2.)) p2_wk = p2_w + int(ceil(k_nb / 2.)) - # if (p1 <= 0 or p1_w <= 0 or p2_wk <= 0): return signal + if (p1 <= 0 or p1_w <= 0 or p2_wk <= 0): + # Return raw counts + nf = [0.0] * (p2 - p1) + nr = [0.0] * (p2 - p1) + for read in self.bam.fetch(chrName, p1, p2): + if not read.is_reverse: + cut_site = read.pos + forward_shift + if p1 <= cut_site < p2: + nf[cut_site - p1] += 1.0 + else: + cut_site = read.aend + reverse_shift - 1 + if p1 <= cut_site < p2: + nr[cut_site - p1] += 1.0 + + return nf, nr # Raw counts nf = [0.0] * (p2_w - p1_w) @@ -430,7 +439,20 @@ def bias_correction_atac2(self, bias_table, genome_file_name, chrName, start, en p2_w = p2 + (window / 2) p1_wk = p1_w - int(floor(k_nb / 2.)) p2_wk = p2_w + int(ceil(k_nb / 2.)) - # if (p1 <= 0 or p1_w <= 0 or p2_wk <= 0): return signal + if (p1 <= 0 or p1_w <= 0 or p2_wk <= 0): + # Return raw counts + signal = [0.0] * (p2 - p1) + for read in self.bam.fetch(chrName, p1, p2): + if not read.is_reverse: + cut_site = read.pos + forward_shift + if p1 <= cut_site < p2: + signal[cut_site - p1] += 1.0 + else: + cut_site = read.aend + reverse_shift - 1 + if p1 <= cut_site < p2: + signal[cut_site - p1] += 1.0 + + return signal # Raw counts nf = [0.0] * (p2_w - p1_w) @@ -962,7 +984,8 @@ def get_bc_signal_by_fragment_length(self, ref, start, end, bam, fasta, bias_tab else: return np.add(np.array(bc_f), np.array(bc_r)) - def get_bias_raw_bc_signal(self, ref, start, end, bam, fasta, bias_table, forward_shift, reverse_shift): + def get_bias_raw_bc_signal(self, ref, start, end, bam, fasta, bias_table, forward_shift, reverse_shift, + strand=False): # Parameters window = 50 defaultKmerValue = 1.0 @@ -1034,14 +1057,22 @@ def get_bias_raw_bc_signal(self, ref, start, end, bam, fasta, bias_table, forwar bias_f = [] bias_r = [] raw = [] + raw_f = [] + raw_r = [] bc = [] + bc_f = [] + bc_r = [] for i in range((window / 2), len(signal_bias_f) - (window / 2)): nhatf = Nf[i - (window / 2)] * (signal_bias_f[i] / fSum) nhatr = Nr[i - (window / 2)] * (signal_bias_r[i] / rSum) bias_f.append(signal_bias_f[i]) bias_r.append(signal_bias_r[i]) raw.append(signal_raw_f[i] + signal_raw_r[i]) + raw_f.append(signal_raw_f[i]) + raw_r.append(signal_raw_r[i]) bc.append(nhatf + nhatr) + bc_f.append(nhatf) + bc_r.append(nhatr) fSum -= fLast fSum += signal_bias_f[i + (window / 2)] fLast = signal_bias_f[i - (window / 2) + 1] @@ -1049,4 +1080,7 @@ def get_bias_raw_bc_signal(self, ref, start, end, bam, fasta, bias_table, forwar rSum += signal_bias_r[i + (window / 2)] rLast = signal_bias_r[i - (window / 2) + 1] - return bias_f, bias_r, raw, bc + if strand: + return bias_f, bias_r, raw, raw_f, raw_r, bc, bc_f, bc_r + else: + return bias_f, bias_r, raw, bc \ No newline at end of file diff --git a/rgt/MotifSet.py b/rgt/MotifSet.py index 77acd8ccc..544452794 100644 --- a/rgt/MotifSet.py +++ b/rgt/MotifSet.py @@ -1,5 +1,3 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- """ MotifSet =================== @@ -7,348 +5,377 @@ """ -# Python +# Python 3 compatibility from __future__ import print_function -import os -import glob +# Python +import glob +import os # Internal +from rgt.Util import npath, MotifData, strmatch -# TODO: -# - connect motif with file and biopython object -# - keep track of databases (???) -class Motif: - """Represents transcription factor motifs. +class MotifAnnotation: + """ + Represents a transcription factor with all available annotation (from MTF files). *Keyword arguments:* - tf_id -- Transcription factor ID. - name -- Transcription factor name (symbol). - database -- Database/repository in which this motif was obtained from. - - tf_class -- Class of transcription factor motif. - - genes -- Genes in which transcription factor binds to. - - genes_suffix -- Gene suffixes. + - family -- Class/Family of transcription factor motif (can be any string). + - gene_names -- List of gene names for this transcription factor (usually only one, sometimes two) + - uniprot_ids -- List of UniProt accession IDs for this transcription factor (like above) + - data_source -- A string representing the 'Source' this transcription factor was generated from, + eg ChiP-Seq, SELEX.. """ - def __init__(self, tf_id, name, database, tf_class, genes, genes_suffix): - self.id = tf_id + def __init__(self, tf_id, name, database, version, gene_names, family, uniprot_ids, data_source): + self.tf_id = tf_id self.name = name self.database = database - self.tf_class = tf_class - self.genes = genes - self.genes_suffix = genes_suffix + self.version = version + self.gene_names = gene_names + self.family = family + self.uniprot_ids = uniprot_ids + self.data_source = data_source + + def __str__(self): + return str(self.__dict__) + + def __repr__(self): + return str(self.__dict__) class MotifSet: - """Represents a set of motifs.""" + """ + Represents a set of motifs. It contains MotifAnnotation instances. + """ - def __init__(self): + def __init__(self, preload_motifs=False): self.motifs_map = {} - self.genes_map = {} - self.genes_suffix_map = {} self.networks = {} self.motifs_enrichment = {} self.conditions = [] - def add(self, new_motif): - """Adds a new motif to this set. + if preload_motifs: + motif_data = MotifData() + + self.read_mtf(motif_data.mtf_list) + + def __len__(self): + return len(self.motifs_map) + + def __iter__(self): + return iter(self.motifs_map.values()) + + def __getitem__(self, key): + return self.motifs_map[key] + + def __str__(self): + return "MotifSet:" + str(self.__dict__) + + def __repr__(self): + return self.__str__() + + def add(self, motif): + """ + Adds a new motif to this set. *Keyword arguments:* - - new_motif -- New motif to be added. + - motif -- Must be an instance of MotifAnnotation """ - # new_motif=Motif(tf_id, name, database, tf_class, genes, genes_suffix) - try: - self.motifs_map[new_motif.name] - except: - self.motifs_map[new_motif.name] = new_motif - for g in new_motif.genes: - g = g.upper() - try: - motifs_aux = self.genes_map[g] - motifs_aux.append(new_motif) - except: - self.genes_map[g] = [new_motif] - for g in new_motif.genes_suffix: - try: - motifs_aux = self.genes_suffix_map[g] - motifs_aux.append(new_motif) - except: - self.genes_suffix_map[g] = [new_motif] - - def match_suffix(self, gene_name): - """Match with gene suffix + self.motifs_map[motif.name] = motif + + def filter(self, keys, key_type="name", search="exact"): + """ + Returns a new MotifSet containing all matching motifs. By default, it expects a list of motif names, but this + can be configured via the key_type parameter. *Keyword arguments:* - - gene_name -- Gene name to perform the match. + - values -- List of strings representing the motif to filter this set on. Actual values depend on key_type. + - key_type -- "name" for matching on the motif name; + "family" for motif family/description; + "uniprot_ids" for matching on UniProt IDs (might be more than one); + "gene_names" for matching on the gene names (symbols); + "data_source" for Chip-Seq, SELEX, etc. + - search -- Search mode (default = 'exact'). If "exact", only perfect matches will be accepted. If + "inexact", key inclusion will be considered a match. For example, if keys=["ARNT"] and + key_type="gene_names" and search="inexact", all motifs corresponding to the gene names "ARNT", "ARNT2", + "ARNTL", etc will be included. If search="exact", only the motifs corresponding to the gene name "ARNT" + are included. *Return:* - - res -- ID of mapped genes. + - motif_set -- Set of filtered motifs. """ - res = [] - gene_name = gene_name.upper() - for s in self.genes_suffix_map.keys(): - if gene_name.startswith(s): - res.append(s) - return res + if not isinstance(keys, list): + raise ValueError("keys must be a list") + + valid_keys = ["name", "gene_names", "family", "uniprot_ids", "data_source"] + + if key_type not in valid_keys: + raise ValueError("key_type must be one of {}".format(valid_keys)) + + motif_set = MotifSet(preload_motifs=False) + + for key in keys: + for m in self.motifs_map.values(): + attr_vals = getattr(m, key_type) + # this is to avoid duplicating code for string-attributes and list-attributes + if not isinstance(attr_vals, list): + attr_vals = [attr_vals] - def filter_by_motifs(self, motifs): - """Filter this motif set by defined motifs. + for attr_val in attr_vals: + if strmatch(key, attr_val, search=search): + motif_set.add(m) + + return motif_set + + def get_mappings(self, key_type="gene_names"): + """ + Returns two dictionaries, the first mapping each motif to its specified "keys" (see filter/3 for more details), + the second mapping each "key" to corresponding motifs. *Keyword arguments:* - - motifs -- Motifs in which to filter this set. + - key_type -- "name" for matching on the motif name; + "family" for motif family/description; + "uniprot_ids" for matching on UniProt IDs (might be more than one); + "gene_names" for matching on the gene names (symbols); + "data_source" for Chip-Seq, SELEX, etc. *Return:* - - motif_sets -- Filtered motif sets. + - motif2keys -- Dictionary mapping all motif names to their corresponding attribute values. For example, + if filtering by genes, it will provide a quick mapping between motif names and all the matching gene names. + - key2motifs -- Inverse of motif2keys. It maps the key values to their corresponding motifs. """ - motif_sets = MotifSet() - for m in motifs: - try: - motif_sets.add(self.motifs_map[m]) - except Exception: - print("motif not found: " + str(m)) - return motif_sets + valid_keys = ["gene_names", "family", "uniprot_ids", "data_source"] - def filter_by_genes(self, genes, search="exact"): - """This method returns motifs associated to genes. The search has three modes: - 1. 'exact' - exact match only - 2. 'inexact' - genes with no exact match are searched for inexact matcth - 3. 'all' - all genes are applied to an inexact match + if key_type not in valid_keys: + raise ValueError("key_type must be one of {}".format(valid_keys)) - *Keyword arguments:* + motif2keys = {} + key2motifs = {} - - genes -- Gene set to perform the filtering. - - search -- Search mode (default = 'exact'). + for m in self.motifs_map.values(): + attr_vals = getattr(m, key_type) + # this is to avoid duplicating code for string-attributes and list-attributes + if not isinstance(attr_vals, list): + attr_vals = [attr_vals] - *Return:* + for attr_val in attr_vals: + # add this motif+attribute to motif2key dict + if m.name in motif2keys: + motif2keys[m.name].append(attr_val) + else: + motif2keys[m.name] = [attr_val] + + # add this attribute+motif to key2motif dict + if attr_val in key2motifs: + key2motifs[attr_val].append(m.name) + else: + key2motifs[attr_val] = [m.name] - - motif_sets -- Filtered motif sets. - - genes_motifs -- Dictionary of genes to motifs. - - motifs_genes -- Dictionary of motifs to genes. + return motif2keys, key2motifs + + def read_mtf(self, mtf_filenames): """ - motif_sets = MotifSet() - motifs_genes = {} - genes_motifs = {} - not_found_genes = [] # keep genes for inexact search - genes = genes.genes - for g in genes: - g = g.upper() - try: - motifs = self.genes_map[g] - for m in motifs: - motif_sets.add(m) - try: - genes_motifs[g].append(m.name) - except: - genes_motifs[g] = [m.name] - try: - motifs_genes[m.name].append(g) - except: - motifs_genes[m.name] = [g] - - except: - not_found_genes.append(g) # keep genes for inexact search - if search == "inexact": - genes = not_found_genes - elif search == "exact": - genes = [] - for g in genes: - suffs = self.match_suffix(g) - for s in suffs: - motifs = self.genes_suffix_map[s] - for m in motifs: - motif_sets.add(m) - try: - genes_motifs[g].append(m.name) - except: - genes_motifs[g] = [m.name] - try: - motifs_genes[m.name].append(g) - except: - motifs_genes[m.name] = [g] - return motif_sets, genes_motifs, motifs_genes - - def read_file(self, file_name_list): - """Reads TF annotation in mtf (internal format; check manual) format. - + Reads TF annotation in mtf (internal format; check manual) format. + *Keyword arguments:* - - file_name_list -- A list with .mtf files. + - file_name_list -- A string, or a list of strings, representing .mtf file paths. """ + if isinstance(mtf_filenames, list): + file_list = [filename for pattern in mtf_filenames for filename in glob.glob(npath(pattern))] + else: + file_list = glob.glob(npath(mtf_filenames)) + # Iterating over the file name list - for file_name in file_name_list: + for filename in file_list: # Opening MTF file - # try: - mtf_file = open(file_name, "r") - # except Exception: pass # TODO + mtf_file = open(filename, "r") # Reading file for line in mtf_file: # Processing line line_list = line.strip().split("\t") - tf_id = line_list[0] - name = line_list[1] - database = line_list[2] - tf_class = int(line_list[3]) - genes = line_list[4].split(";") - genes_suffix = line_list[5].split(";") + tf_id = line_list[0].strip() + name = line_list[1].strip() + database = line_list[2].strip() + version = int(line_list[3].strip()) + gene_names = line_list[4].strip().split(";") + tf_class = line_list[5].strip() + uniprot_ids = line_list[6].strip().split(";") + data_source = line_list[7].strip() - self.add(Motif(tf_id, name, database, tf_class, genes, genes_suffix)) + self.add(MotifAnnotation(tf_id, name, database, version, gene_names, tf_class, uniprot_ids, data_source)) # Termination mtf_file.close() - def read_motif_targets_enrichment(self, enrichment_files, pvalue_threshold): - """Reads current output of motif enrichment analysis to get gene targets. + def read_enrichment(self, enrichment_files, threshold=1): + """ + Reads current output of motif enrichment analysis to get gene targets. *Keyword arguments:* - - enrichment_files -- Enrichment files to read. - - pvalue_threshold -- P-value threshold for motif acceptance. + - enrichment_files -- One string, or a list of strings, representing enrichment file paths. + - threshold -- P-value threshold for motif acceptance. """ + + if isinstance(enrichment_files, list): + file_list = [filename for pattern in enrichment_files for filename in glob.glob(npath(pattern))] + else: + file_list = glob.glob(npath(enrichment_files)) + # reading networks - for f in glob.glob(enrichment_files): + for filename in file_list: # use last dir name as name for condition - condition = os.path.dirname(f) + condition = os.path.dirname(filename) condition = condition.split("/")[-1] self.conditions.append(condition) + network = {} - for line in open(f): + + f = open(filename, "r") + + # skip header + next(f) + + for line in f: line = line.strip("\n") values = line.split("\t") motif = values[0] - try: - self.motifs_map[motif] + + if motif in self.motifs_map: p_value = float(values[2]) genes = values[9].split(",") - if pvalue_threshold >= p_value: + + if threshold >= p_value: network[motif] = genes - try: - p_values = self.motifs_enrichment[motif] - p_values[condition] = p_value - except: - p_values = {condition: p_value} - self.motifs_enrichment[motif] = p_values - except Exception as e: - print("motif not found: " + motif + str(e)) + + if motif in self.motifs_enrichment: + self.motifs_enrichment[motif][condition] = p_value + else: + self.motifs_enrichment[motif] = {condition: p_value} + else: + print("motif not found: " + motif) self.networks[condition] = network - def write_enrichment_table(self, threshold, out_file, motifs_map): - """Writes enrichment table for network generation. + f.close() + + def write_enrichment(self, out_file, threshold=1): + """ + Writes enrichment table for network generation. *Keyword arguments:* - - threshold -- P-value threshold for motif acceptance. - out_file -- Output file name. - - motifs_map -- Mapping of motifs. + - threshold -- P-value threshold for motif acceptance. """ - f = open(out_file, "w") + f = open(npath(out_file), "w") f.write("\t" + ("\t".join(self.conditions)) + "\n") - motifs = motifs_map.keys() - for v in self.motifs_enrichment.keys(): + + for v in self.motifs_enrichment: values = self.motifs_enrichment[v] filter_p = False p_values = [] + for c in self.conditions: - try: + if c in values: pvalue = values[c] p_values.append(str(pvalue)) + if pvalue <= threshold: filter_p = True - except: + else: p_values.append("1") - if filter_p & (v in motifs): - genes = "|".join(motifs_map[v]) + + if filter_p and (v in self.motifs_map) and self.motifs_map[v].gene_names: + genes = "|".join(self.motifs_map[v].gene_names) f.write(v + "|" + genes + "\t" + ("\t".join(p_values)) + "\n") - def write_cytoscape_network(self, genes, gene_mapping_search, out_path, targets, threshold): - """Write files to be used as input for cytoscape. It recieves a list of genes to map to, a mapping search strategy and path for outputting files. + def write_network(self, targets, out_path, threshold=1): + """ + If enrichment information has been loaded before (via read_enrichment), this function creates + a cytoscape-compatible network into the output folder. *Keyword arguments:* - - genes -- Gene set. - - gene_mapping_search -- Gene mapping. - - out_path -- Output path. - targets -- Gene targets. + - out_path -- Output path. - threshold -- Threshold for motif acceptance. """ - # getting mapping of genes to motifs - [_, genes_motifs, _] = self.filter_by_genes(genes, gene_mapping_search) - - f = open(out_path + "/mapping_tf_genes.txt", "w") - motifs_all = {} - for gene in genes_motifs.keys(): - motifs = genes_motifs[gene] - for m in motifs: - try: - g = motifs_all[m] - if gene not in g: - motifs_all[m].append(gene) - except: - motifs_all[m] = [gene] - f.write(gene + "\t" + m + "\n") - f.close() - - self.write_enrichment_table(threshold, out_path + "/pvalue_table_" + str(threshold * 100) + ".txt", motifs_all) + + self.write_enrichment(out_path + "/pvalue_table_" + str(threshold * 100) + ".txt", threshold) + + out_path = npath(out_path) + + _, genes_motifs = self.get_mappings(key_type="gene_names") net_pairs = {} net_tfs = {} all_pairs = set() all_tfs = set() all_genes = set() - if not targets: - filter_targets = False - else: + + if targets: filter_targets = True - targets = [g.upper() for g in targets.genes] + else: + filter_targets = False # using genes to motif mapping to get network in all conditions - for net_name in self.networks.keys(): + for net_name in self.networks: net = self.networks[net_name] pairs = set() tfs = set() net_pairs[net_name] = pairs net_tfs[net_name] = tfs - for tf in genes_motifs.keys(): + for tf in genes_motifs: motifs = genes_motifs[tf] for m in motifs: - try: + if m in net: for target in net[m]: if not filter_targets or (target in targets): pairs.add((tf, target)) tfs.add(tf) all_genes.add(tf) all_genes.add(target) - except Exception as e: - print("motif not in network: " + str(m) + " " + str(tf) + " " + str(e)) + else: + print("motif not in network: " + m + " " + str(tf) + " ") + all_pairs = all_pairs.union(pairs) all_tfs = all_tfs.union(tfs) # printing out network - for net_name in net_pairs.keys(): + for net_name, pairs_aux in net_pairs.items(): f = open(out_path + "/" + net_name + "_targets.txt", "w") - pairs_aux = net_pairs[net_name] + for pair in all_pairs: # check if pair is active in the network if pair in pairs_aux: f.write(pair[0] + "\t" + pair[1] + "\tactive\n") else: f.write(pair[0] + "\t" + pair[1] + "\tinactive\n") + f.close() + f = open(out_path + "/" + net_name + "_genes.txt", "w") + for gene in all_genes: # check if gene is tf active in network if gene in net_tfs[net_name]: @@ -357,4 +384,5 @@ def write_cytoscape_network(self, genes, gene_mapping_search, out_path, targets, f.write(gene + "\ttf_inactive\n") else: f.write(gene + "\ttarget\n") + f.close() diff --git a/rgt/SequenceSet.py b/rgt/SequenceSet.py index 29936eb47..fe367b45a 100644 --- a/rgt/SequenceSet.py +++ b/rgt/SequenceSet.py @@ -61,24 +61,31 @@ def methylate(self, cpg_sites): def complement(self): """Return another Sequence which is the complement to original sequence.""" - if self.strand == "RNA": - print("No complement strand for RNA " + self.name) - return - elif self.strand == "+": - strand = "-" - else: - strand = "-" - - seq = copy.deepcopy(self.seq) - seq.replace("A", "G") - seq.replace("G", "A") - seq.replace("C", "T") - seq.replace("T", "C") - seq.replace("m", "1") - seq.replace("1", "m") - - s = Sequence(seq, name=self.name + "_complement", strand=strand) - return s + # if self.strand == "RNA": + # print("No complement strand for RNA " + self.name) + # return + # elif self.strand == "+": + # strand = "-" + # else: + # strand = "-" + + t = {"A": "T", "T": "A", "C": "G", "G": "C", "N": "N"} + ns = "" + for s in self.seq: + ns += t[s] + return ns + # self.seq = ns + # + # seq = copy.deepcopy(self.seq) + # seq.replace("A", "G") + # seq.replace("G", "A") + # seq.replace("C", "T") + # seq.replace("T", "C") + # seq.replace("m", "1") + # seq.replace("1", "m") + # + # s = Sequence(seq=seq, name=self.name + "_complement", strand=strand) + # return s #################################################################################### @@ -137,7 +144,7 @@ def read_fasta(self, fasta_file): pass elif line[0] == ">": if pre_seq: - self.sequences.append(Sequence(seq=seq, strand=strand, name=info)) + self.add(Sequence(seq=seq, strand=strand, name=info)) pre_seq = False info = line.split()[0][1:] seq = "" @@ -153,9 +160,16 @@ def read_fasta(self, fasta_file): pre_seq = True if not strand: print("Error: There is no header in " + fasta_file) - self.sequences.append(Sequence(seq=seq, strand=".", name=info)) + self.add(Sequence(seq=seq, strand=".", name=info)) else: - self.sequences.append(Sequence(seq=seq, strand=strand, name=info)) + self.add(Sequence(seq=seq, strand=strand, name=info)) + + def write_fasta(self, filename): + with open(filename, "w") as f: + for s in self: + print(">"+ s.name, file=f) + for ss in [s.seq[i:i + 80] for i in range(0, len(s), 80)]: + print(ss, file=f) def read_regions(self, regionset, genome_fasta, ex=0): genome = pysam.Fastafile(genome_fasta) diff --git a/rgt/THOR/THOR.py b/rgt/THOR/THOR.py index b7564a9a6..d2fb4e383 100644 --- a/rgt/THOR/THOR.py +++ b/rgt/THOR/THOR.py @@ -133,11 +133,11 @@ def run_HMM(region_giver, options, bamfiles, genome, chrom_sizes, dims, inputs, pcutoff=options.pcutoff, debug=options.debug, p=options.par, no_correction=options.no_correction, merge_bin=options.merge_bin, deadzones=options.deadzones) - + # if not inst_output: output += inst_output pvalues += inst_pvalues ratios += inst_ratios - + res_output, res_pvalues, res_filter_pass = filter_by_pvalue_strand_lag(ratios, options.pcutoff, pvalues, output, options.no_correction, options.name, options.singlestrand) diff --git a/rgt/THOR/dpc_help.py b/rgt/THOR/dpc_help.py index ddf85262e..cc32e7645 100644 --- a/rgt/THOR/dpc_help.py +++ b/rgt/THOR/dpc_help.py @@ -336,6 +336,7 @@ def get_peaks(name, DCS, states, exts, merge, distr, pcutoff, debug, no_correcti pvalues, peaks, = _merge_consecutive_bins(tmp_peaks, distr, merge_bin) #merge consecutive peaks and compute p-value regions = merge_delete(exts, merge, peaks, pvalues) #postprocessing, returns GenomicRegionSet with merged regions + if deadzones: regions = filter_deadzones(deadzones, regions) output = [] diff --git a/rgt/THOR/postprocessing.py b/rgt/THOR/postprocessing.py index b7908f3d4..26a46b254 100644 --- a/rgt/THOR/postprocessing.py +++ b/rgt/THOR/postprocessing.py @@ -75,21 +75,20 @@ def merge_delete(ext_size, merge, peak_list, pvalue_list): for i, t in enumerate(peak_list): chrom, start, end, c1, c2, strand, ratio = t[0], t[1], t[2], t[3], t[4], t[5], t[6] - r = GenomicRegion(chrom = chrom, initial = start, final = end, name = '', \ + r = GenomicRegion(chrom = chrom, initial = start, final = end, name = '', orientation = strand, data = str((c1, c2, pvalue_list[i], ratio))) - if end - start > ext_size: - if strand == '+': - if last_orientation == '+': - region_plus.add(r) - else: - regions_unmergable.add(r) - elif strand == '-': - if last_orientation == '-': - region_mins.add(r) - else: - regions_unmergable.add(r) - - + # if end - start > ext_size: + if strand == '+': + if last_orientation == '+': + regions_plus.add(r) + else: + regions_unmergable.add(r) + elif strand == '-': + if last_orientation == '-': + regions_minus.add(r) + else: + regions_unmergable.add(r) + if merge: regions_plus.extend(ext_size/2, ext_size/2) regions_plus.merge() @@ -114,6 +113,7 @@ def merge_delete(ext_size, merge, peak_list, pvalue_list): def filter_by_pvalue_strand_lag(ratios, pcutoff, pvalues, output, no_correction, name, singlestrand): """Filter DPs by strang lag and pvalue""" + if not singlestrand: zscore_ratios = zscore(ratios) ratios_pass = np.where(np.bitwise_and(zscore_ratios > -2, zscore_ratios < 2) == True, True, False) diff --git a/rgt/Util.py b/rgt/Util.py index d81405462..893601308 100644 --- a/rgt/Util.py +++ b/rgt/Util.py @@ -10,11 +10,30 @@ import os import sys import shutil +import re +import codecs from configparser import ConfigParser import traceback from optparse import OptionParser, BadOptionError, AmbiguousOptionError +def strmatch(pattern, string, search="exact", case_insensitive=True): + valid_types = ["exact", "inexact", "regex"] + + if case_insensitive: + pattern = pattern.lower() + string = string.lower() + + if search == "exact": + return pattern == string + elif search == "inexact": + return pattern in string + elif search == "regex": + return re.search(pattern, string) + else: + raise ValueError("search must be one of these: {}".format(valid_types)) + + def cmp(a, b): return (a > b) - (a < b) @@ -44,11 +63,12 @@ def __init__(self): # Parsing config file self.config = ConfigParser() - self.config.read(data_config_file_name) + # self.config.read(data_config_file_name) + self.config.read_file(codecs.open(data_config_file_name, "r", "utf8")) # Overwriting config using user options - self.config.read(data_config_file_name + ".user") - + # self.config.read(data_config_file_name + ".user") + self.config.read_file(codecs.open(data_config_file_name + ".user", "r", "utf8")) # Reading data directory self.data_dir = os.path.split(data_config_file_name)[0] diff --git a/rgt/__version__.py b/rgt/__version__.py index 9eba37823..49e069177 100644 --- a/rgt/__version__.py +++ b/rgt/__version__.py @@ -1,2 +1,2 @@ - -__version__ = "0.11.2" +0 +__version__ = "0.11.3" diff --git a/rgt/motifanalysis/Enrichment.py b/rgt/motifanalysis/Enrichment.py index 269be0c33..a77b6356f 100644 --- a/rgt/motifanalysis/Enrichment.py +++ b/rgt/motifanalysis/Enrichment.py @@ -7,6 +7,7 @@ from __future__ import division # Python +import base64 import os import sys from glob import glob @@ -67,10 +68,14 @@ def options(parser): "this option are printed. Use 1.0 to print all MPBSs.") group.add_argument("--bigbed", action="store_true", default=False, help="If this option is used, all bed files will be written as bigbed.") - group.add_argument("--no-copy-logos", action="store_true", default=False, - help="If set, the logos to be showed on the enrichment statistics page will NOT be " - "copied to a local directory; instead, the HTML result file will contain absolute " - "paths to the logos in your RGTDATA folder.") + + # Logo mutually exclusive: + group = parser.add_mutually_exclusive_group(required=False) + group.add_argument("--logo-copy", action="store_true", default=False, + help="The logos are copied to a local directory. The HTML report will contain relative " + "paths to this directory.") + group.add_argument("--logo-embed", action="store_true", default=False, + help="The logos are embedded directly into the HTML report.") parser.add_argument('background_file', metavar='background.bed', type=str, help='BED file containing background regions.') @@ -589,8 +594,8 @@ def main(args): output_file.write(str(r) + "\n") output_file.close() - # unless explicitly forbidden, we copy the logo images locally - if not args.no_copy_logos: + # we copy the logo images locally + if args.logo_copy: logo_dir_path = npath(os.path.join(output_location, "logos")) try: os.stat(logo_dir_path) @@ -605,12 +610,16 @@ def main(args): logo_file_name = npath(os.path.join(rep, r.name + ".png")) if os.path.isfile(logo_file_name): - if not args.no_copy_logos: + if args.logo_copy: copy(logo_file_name, npath(os.path.join(logo_dir_path, r.name + ".png"))) # use relative paths in the html # FIXME can we do it in a better way? (inside the Html class) logo_file_name = os.path.join("..", "logos", r.name + ".png") + elif args.logo_embed: + # encoding image with Base64 and adding HTML special URI to embed it + data_uri = base64.b64encode(open(logo_file_name, 'rb').read()).decode('utf-8') + logo_file_name = "data:image/png;base64,{0}".format(data_uri) curr_motif_tuple = [logo_file_name, logo_width] break @@ -730,8 +739,8 @@ def main(args): output_file.write(str(r) + "\n") output_file.close() - # unless explicitly forbidden, we copy the logo images locally - if not args.no_copy_logos: + # we copy the logo images locally + if args.logo_copy: logo_dir_path = npath(os.path.join(output_location, "logos")) try: os.stat(logo_dir_path) @@ -746,12 +755,16 @@ def main(args): logo_file_name = npath(os.path.join(rep, r.name + ".png")) if os.path.isfile(logo_file_name): - if not args.no_copy_logos: + if args.logo_copy: copy(logo_file_name, npath(os.path.join(logo_dir_path, r.name + ".png"))) # use relative paths in the html # FIXME can we do it in a better way? (inside the Html class) logo_file_name = os.path.join("..", "logos", r.name + ".png") + elif args.logo_embed: + # encoding image with Base64 and adding HTML special URI to embed it + data_uri = base64.b64encode(open(logo_file_name, 'rb').read()).decode('utf-8') + logo_file_name = "data:image/png;base64,{0}".format(data_uri) curr_motif_tuple = [logo_file_name, logo_width] break diff --git a/rgt/motifanalysis/Main.py b/rgt/motifanalysis/Main.py index 4b728c4dc..3ffe50a61 100644 --- a/rgt/motifanalysis/Main.py +++ b/rgt/motifanalysis/Main.py @@ -46,7 +46,6 @@ def main(): Enrichment.options(enrichment) enrichment.set_defaults(func=Enrichment.main) - # TODO # mapper = subparsers.add_parser('mapper', help='map genes names to TF IDs and viceversa') # Mapper.options(mapper) # mapper.set_defaults(func=Mapper.main) diff --git a/rgt/motifanalysis/Mapper.py b/rgt/motifanalysis/Mapper.py index d6ab80b37..aed697f4b 100644 --- a/rgt/motifanalysis/Mapper.py +++ b/rgt/motifanalysis/Mapper.py @@ -19,7 +19,7 @@ from rgt.GenomicRegionSet import GenomicRegionSet from rgt.GenomicRegion import GenomicRegion from rgt.AnnotationSet import AnnotationSet -from Motif import Motif, Thresholds +from Motif import Motif, ThresholdTable from Util import bed_to_bb # External @@ -46,6 +46,7 @@ def options(parser): help="Motif IDs will be converted to gene IDs") group.add_argument("--gene-to-motifs", action="store_true", help="Gene IDs will be converted to motif IDs") + parser.add_argument("--motif-db", choices=('jaspar_vertebrates', 'hocomoco', 'uniprobe_primary', 'uniprobe_secondary')) parser.add_argument("--gene-db", choices=('ensembl', 'names')) diff --git a/rgt/motifanalysis/Match.py b/rgt/motifanalysis/Match.py index 6b32cd857..a3dd057b5 100644 --- a/rgt/motifanalysis/Match.py +++ b/rgt/motifanalysis/Match.py @@ -19,7 +19,7 @@ from ..GenomicRegionSet import GenomicRegionSet from ..GenomicRegion import GenomicRegion from ..AnnotationSet import AnnotationSet -from .Motif import Motif, Thresholds +from .Motif import Motif, ThresholdTable from .Util import bed_to_bb # External @@ -308,7 +308,7 @@ def main(args): print(">> motif repositories:", motif_data.repositories_list) # Creating thresholds object - thresholds = Thresholds(motif_data) + threshold_table = ThresholdTable(motif_data) # Fetching list with all motif file names motif_file_names = [] @@ -323,7 +323,7 @@ def main(args): # Iterating on grouped file name list for motif_file_name in motif_file_names: # Append motif motif_list - motif_list.append(Motif(motif_file_name, args.pseudocounts, args.fpr, thresholds)) + motif_list.append(Motif(motif_file_name, args.pseudocounts, args.fpr, threshold_table)) # Performing normalized threshold strategy if requested if args.norm_threshold: diff --git a/rgt/motifanalysis/Motif.py b/rgt/motifanalysis/Motif.py index b6ecf88ff..4aa303bd7 100644 --- a/rgt/motifanalysis/Motif.py +++ b/rgt/motifanalysis/Motif.py @@ -1,20 +1,21 @@ - ################################################################################################### # Libraries ################################################################################################### +from __future__ import division # Python 3 compatibility from __future__ import print_function -from __future__ import division # Python from os.path import basename -# Internal - # External from MOODS import tools, parsers + +# Internal + + ################################################################################################### # Classes ################################################################################################### @@ -39,7 +40,7 @@ def __init__(self, input_file_name, pseudocounts, fpr, thresholds): max -- Maximum PSSM score possible. is_palindrome -- True if consensus is biologically palindromic. """ - + # Initializing name self.name = ".".join(basename(input_file_name).split(".")[:-1]) repository = input_file_name.split("/")[-2] @@ -75,7 +76,7 @@ def __init__(self, input_file_name, pseudocounts, fpr, thresholds): self.is_palindrome = [max(e) for e in self.pssm] == [max(e) for e in reversed(self.pssm)] -class Thresholds: +class ThresholdTable: """ Container for all motif thresholds given default FPRs. @@ -117,5 +118,5 @@ def __init__(self, motif_data): self.dict[fpr_name][ll[0]] = dict() for i in range(1, len(ll)): # Updating dictionary - self.dict[fpr_name][ll[0]][fpr_values[i-1]] = float(ll[i]) + self.dict[fpr_name][ll[0]][fpr_values[i - 1]] = float(ll[i]) fpr_file.close() diff --git a/rgt/tdf/Main.py b/rgt/tdf/Main.py index 965504c22..1f835cf72 100644 --- a/rgt/tdf/Main.py +++ b/rgt/tdf/Main.py @@ -214,7 +214,7 @@ def main(): # Project level index file for item in os.listdir(args.path): - # print("\t"+item) + print("\t"+item) if item == "style": continue elif os.path.isfile(os.path.join(args.path, item)): continue diff --git a/rgt/tdf/Report.py b/rgt/tdf/Report.py index 60a166523..83cbeae12 100644 --- a/rgt/tdf/Report.py +++ b/rgt/tdf/Report.py @@ -16,9 +16,12 @@ from ..Util import Html, SequenceType # Color code for all analysis -target_color = "mediumblue" +# target_color = "mediumblue" +target_color = "royalblue" +# nontarget_color = "darkgrey" nontarget_color = "darkgrey" -sig_color = "powderblue" +sig_color = "lightgrey" +legend_fontsize=8 def uniq(seq): seen = set() @@ -243,22 +246,15 @@ def lineplot(self, tpx, rnalen, rnaname, dirp, sig_region, cut_off, log, ylabel, min_y = 0 if ac: - min_y = float(max_y * (-0.09)) + # min_y = float(max_y * (-0.09)) + max_y = float(max_y * (1.05)) # Plotting for rbs in sig_region: - rect = patches.Rectangle(xy=(rbs.initial, 0), width=len(rbs), height=max_y, facecolor=sig_color, - edgecolor="none", alpha=0.5, lw=None, label="Significant DBD") + rect = patches.Rectangle(xy=(rbs.initial, 0), width=len(rbs), height=0.95*max_y, facecolor=sig_color, + edgecolor="none", alpha=1, lw=None, label="Significant DBD", zorder=2) ax.add_patch(rect) - lw = 1.5 - if showpa: - ax.plot(x, all_y, color=target_color, alpha=1, lw=lw, label="Parallel + Anti-parallel") - ax.plot(x, p_y, color="purple", alpha=1, lw=lw, label="Parallel") - ax.plot(x, a_y, color="dimgrey", alpha=.8, lw=lw, label="Anti-parallel") - else: - ax.plot(x, all_y, color="mediumblue", alpha=1, lw=lw, label=linelabel) - # RNA accessbility if ac: if isinstance(ac, list): @@ -274,13 +270,32 @@ def lineplot(self, tpx, rnalen, rnaname, dirp, sig_region, cut_off, log, ylabel, last_i = i drawing = True elif drawing: - ax.add_patch(patches.Rectangle((last_i, min_y), i - last_i, -min_y, - fill=True, color="silver", snap=False, linewidth=0, + # ax.add_patch(patches.Rectangle((last_i, min_y), i - last_i, -min_y, + # fill=True, color="silver", snap=False, linewidth=0, + # label="Autobinding")) + + ax.add_patch(patches.Rectangle(xy=(last_i, 0.95*max_y), width=i - last_i, height=0.05*max_y, + fill=True, color="lightcoral", snap=False, linewidth=0,zorder=2, label="Autobinding")) drawing = False else: continue + lw = 1.5 + if showpa: + ax.plot(x, all_y, color=target_color, alpha=1, lw=lw, label="Parallel + Anti-parallel") + ax.plot(x, p_y, color="purple", alpha=1, lw=lw, label="Parallel") + ax.plot(x, a_y, color="dimgrey", alpha=.8, lw=lw, label="Anti-parallel") + else: + ax.plot(x, all_y, color=target_color, alpha=0, lw=lw, markeredgecolor="none", zorder=10) + ax.fill_between(x, 0, all_y, facecolor=target_color, alpha=1, edgecolor="none", label=linelabel, zorder=10) + ax.spines['right'].set_visible(False) + ax.spines['top'].set_visible(False) + ax.yaxis.set_ticks_position('left') + ax.xaxis.set_ticks_position('bottom') + + + # Legend handles, labels = ax.get_legend_handles_labels() legend_h = [] @@ -288,16 +303,16 @@ def lineplot(self, tpx, rnalen, rnaname, dirp, sig_region, cut_off, log, ylabel, for uniqlabel in uniq(labels): legend_h.append(handles[labels.index(uniqlabel)]) legend_l.append(uniqlabel) - ax.legend(legend_h, legend_l, + ax.legend(legend_h, legend_l, fontsize=legend_fontsize, bbox_to_anchor=(0., 1.02, 1., .102), loc=2, mode="expand", borderaxespad=0., - prop={'size': 9}, ncol=3) + ncol=3) # XY axis ax.set_xlim(left=0, right=rnalen) ax.set_ylim([min_y, max_y]) for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(9) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(9) - ax.set_xlabel(rnaname + " sequence (bp)", fontsize=9) + ax.set_xlabel(rnaname + " sequence (nt)", fontsize=9) ax.set_ylabel(ylabel, fontsize=9, rotation=90) @@ -321,10 +336,10 @@ def lineplot(self, tpx, rnalen, rnaname, dirp, sig_region, cut_off, log, ylabel, w += l ax.text(rnalen * 0.01, max_y - 2 * h, "exon boundaries", fontsize=5, color='black') - f.tight_layout(pad=1.08, h_pad=None, w_pad=None) + # f.tight_layout(pad=1.08) f.savefig(os.path.join(dirp, filename), facecolor='w', edgecolor='w', - bbox_extra_artists=(plt.gci()), bbox_inches='tight', dpi=300) + bbox_extra_artists=(plt.gci()),dpi=300) # PDF for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(12) @@ -332,12 +347,12 @@ def lineplot(self, tpx, rnalen, rnaname, dirp, sig_region, cut_off, log, ylabel, tick.label.set_fontsize(12) ax.xaxis.label.set_size(14) ax.yaxis.label.set_size(14) - ax.legend(legend_h, legend_l, + ax.legend(legend_h, legend_l,fontsize=legend_fontsize, bbox_to_anchor=(0., 1.02, 1., .102), loc=2, mode="expand", borderaxespad=0., prop={'size': 12}, ncol=3) - # pp = PdfPages(os.path.splitext(os.path.join(dirp,filename))[0] +'.pdf') - # pp.savefig(f, bbox_inches='tight') # bbox_extra_artists=(plt.gci()), - # pp.close() + pp = PdfPages(os.path.splitext(os.path.join(dirp,filename))[0] +'.pdf') + pp.savefig(f) # bbox_extra_artists=(plt.gci()),, bbox_inches='tight' + pp.close() def barplot(self, filename, dbs=False): """Generate the barplot to show the difference between target promoters and non-target promoters""" @@ -367,7 +382,7 @@ def to_percent(y, position): for i, rbs in enumerate(self.stat.rbss): if rbs in self.stat.sig_DBD: rect = patches.Rectangle(xy=(i + 0.05, 0), width=0.9, height=max_y, facecolor=sig_color, - edgecolor="none", alpha=0.5, lw=None, label="Significant DBD") + edgecolor="none", lw=None, label="Significant DBD") ax.add_patch(rect) rects_de = ax.bar([i + 0.325 for i in ind], propor_de, width, color=target_color, @@ -376,13 +391,13 @@ def to_percent(y, position): edgecolor="none", label="Non-target promoters") # Legend - tr_legend, = plt.plot([1, 1], color=target_color, linewidth=6, alpha=1) - ntr_legend, = plt.plot([1, 1], color=nontarget_color, linewidth=6, alpha=1) - ax.legend([tr_legend, ntr_legend, rect], ["Target promoters", "Non-target promoters", "Significant DBD"], - bbox_to_anchor=(0., 1.02, 1., .102), loc=2, mode="expand", borderaxespad=0., - prop={'size': 9}, ncol=3) - tr_legend.set_visible(False) - ntr_legend.set_visible(False) + # tr_legend, = plt.plot([1, 1], color=target_color, linewidth=6, alpha=1) + # ntr_legend, = plt.plot([1, 1], color=nontarget_color, linewidth=6, alpha=1) + ax.legend([rects_de, rects_nde, rect], ["Target promoters", "Non-target promoters", "Significant DBD"], + bbox_to_anchor=(0., 1.02, 1., .102), loc=2, mode="expand", borderaxespad=0.,fontsize=legend_fontsize, + ncol=3) + # tr_legend.set_visible(False) + # ntr_legend.set_visible(False) tick_size = 8 # Y axis @@ -390,35 +405,36 @@ def to_percent(y, position): formatter = FuncFormatter(to_percent) # Set the formatter ax.yaxis.set_major_formatter(formatter) - ax.tick_params(axis='y', which='both', left='on', right='off', labelbottom='off') + ax.tick_params(axis='y', which='both', left=True, right=False, labelbottom=False) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(9) ax.set_ylabel("Proportion of promoters (%)", fontsize=9, rotation=90) # X axis ax.set_xlim([0, len(self.stat.rbss)]) - ax.tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='on') + ax.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=True) ax.set_xticks([i + 0.5 for i in range(len(self.stat.rbss))]) - ax.set_xticklabels([dbd.str_rna(pa=False) for dbd in self.stat.rbss], rotation=35, - ha="right", fontsize=tick_size) + # ax.set_xticklabels([dbd.str_rna(pa=False) for dbd in self.stat.rbss], rotation=35, + # ha="right", fontsize=tick_size) + ax.set_xticklabels([dbd.str_rna(pa=False) for dbd in self.stat.rbss], ha="center", fontsize=tick_size) for spine in ['top', 'right']: ax.spines[spine].set_visible(False) for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(9) ax.set_xlabel(self.pars.rn + " DNA Binding Domains", fontsize=9) - f.tight_layout(pad=1.08, h_pad=None, w_pad=None) + # f.tight_layout(pad=1.08) f.savefig(os.path.join(self.pars.o, filename), facecolor='w', edgecolor='w', - bbox_extra_artists=(plt.gci()), bbox_inches='tight', dpi=300) + bbox_extra_artists=(plt.gci()), dpi=300) # PDF - # for tick in ax.xaxis.get_major_ticks(): - # tick.label.set_fontsize(12) - # for tick in ax.yaxis.get_major_ticks(): - # tick.label.set_fontsize(12) - # ax.xaxis.label.set_size(14) - # ax.yaxis.label.set_size(14) - # pp = PdfPages(os.path.splitext(os.path.join(self.pars.o, filename))[0] + '.pdf') - # pp.savefig(f, bbox_extra_artists=(plt.gci()), bbox_inches='tight') - # pp.close() + for tick in ax.xaxis.get_major_ticks(): + tick.label.set_fontsize(12) + for tick in ax.yaxis.get_major_ticks(): + tick.label.set_fontsize(12) + ax.xaxis.label.set_size(14) + ax.yaxis.label.set_size(14) + pp = PdfPages(os.path.splitext(os.path.join(self.pars.o, filename))[0] + '.pdf') + pp.savefig(f, bbox_extra_artists=(plt.gci())) + pp.close() def boxplot(self, filename, matrix, sig_region, truecounts, sig_boolean, ylabel): """Generate the visualized plot""" @@ -452,7 +468,7 @@ def boxplot(self, filename, matrix, sig_region, truecounts, sig_boolean, ylabel) # Plot target regions plt.plot(range(1, len(self.stat.rbss) + 1), truecounts, markerfacecolor=target_color, - marker='o', markersize=5, linestyle='None', markeredgecolor="white", zorder=z + 5) + marker='o', markersize=8, linestyle='None', markeredgecolor="white", zorder=z + 5, label="Target Regions") ax.set_xlabel(self.pars.rn + " DNA Binding Domains", fontsize=label_size) ax.set_ylabel(ylabel, fontsize=label_size, rotation=90) @@ -460,29 +476,29 @@ def boxplot(self, filename, matrix, sig_region, truecounts, sig_boolean, ylabel) ax.set_ylim([min_y, max_y]) ax.yaxis.set_major_locator(MaxNLocator(integer=True)) - ax.set_xticklabels([dbd.str_rna(pa=False) for dbd in self.stat.rbss], rotation=35, - ha="right", fontsize=tick_size) + # ax.set_xticklabels([dbd.str_rna(pa=False) for dbd in self.stat.rbss], rotation=35, + # ha="right", fontsize=tick_size) + ax.set_xticklabels([dbd.str_rna(pa=False) for dbd in self.stat.rbss], ha="center", fontsize=tick_size) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(tick_size) for spine in ['top', 'right']: ax.spines[spine].set_visible(False) - ax.tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='on') - ax.tick_params(axis='y', which='both', left='on', right='off', labelbottom='off') + ax.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=True) + ax.tick_params(axis='y', which='both', left=True, right=False, labelbottom=False) # Legend dot_legend, = plt.plot([1, 1], color=target_color, marker='o', markersize=5, markeredgecolor="white", linestyle='None') - bp_legend, = plt.plot([1, 1], color=nontarget_color, linewidth=6, alpha=1) + # bp_legend, = plt.plot([1, 1], color=nontarget_color, linewidth=6, alpha=1) - ax.legend([dot_legend, bp_legend, rect], ["Target Regions", "Non-target regions", "Significant DBD"], - bbox_to_anchor=(0., 1.02, 1., .102), loc=2, mode="expand", borderaxespad=0., - prop={'size': 9}, ncol=3, numpoints=1) - bp_legend.set_visible(False) - dot_legend.set_visible(False) + ax.legend([dot_legend, bp["boxes"][0], rect], ["Target Regions", "Non-target regions", "Significant DBD"], + bbox_to_anchor=(0., 1.02, 1., .102), loc=2, mode="expand", borderaxespad=0.,fontsize=legend_fontsize, + ncol=3, numpoints=1) + # bp_legend.set_visible(False) + # dot_legend.set_visible(False) # f.tight_layout(pad=1.08, h_pad=None, w_pad=None) - f.savefig(os.path.join(self.pars.o, filename), facecolor='w', edgecolor='w', - bbox_extra_artists=(plt.gci()), bbox_inches='tight', dpi=300) + f.savefig(os.path.join(self.pars.o, filename), facecolor='w', edgecolor='w', bbox_extra_artists=(plt.gci()), dpi=300) # PDF for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(12) @@ -920,16 +936,9 @@ def gen_html_genes(self, align = 50, nonDE=False): if self.stat.promoter["de"]["dbs"][promoter.toString()] == 0: continue else: - # try: - # gn = self.ensembl2symbol[promoter.name] - # except: gn = promoter.name html.add_heading(split_gene_name(gene_name=gn, org=self.pars.organism), idtag=promoter.toString()) - # html.add_free_content(['' + - # promoter.toString(space=True) + '']) + data_table = [] for j, rd in enumerate(self.stat.promoter["de"]["rd"][promoter.toString()]): @@ -938,7 +947,6 @@ def gen_html_genes(self, align = 50, nonDE=False): # rbsm = rbsm.partition(":")[2].split("-") if rd.rna.overlap(rbsm): rbs = "" + rbs + "" - # print(rd.match) data_table.append([str(j + 1), rbs, rd.dna.toString(space=True), rd.dna.orientation, rd.score, rd.motif, rd.orient, '
' + "\n".join(rd.match) + "
"]) @@ -1398,7 +1406,11 @@ def gen_html_regiontest(self): ############################ # Subpages for targeted region centered page # region_dbs.html - header_list = ["RBS", "DBS", "Strand", "Score", "Motif", "Orientation"] + header_list = ["RBS", "DBS", "Strand", "Score", "Motif", "Orientation", "Sequence"] + header_titles = ["", "RNA Binding Site", "DNA Binding Site", "Strand of DBS on DNA", + "Score of binding event", "Motif of binding by triple helix rule", + "Orientation of interaction between DNA and RNA. 'P'- Parallel; 'A'-Antiparallel", + "Binding Sequence between DNA and RNA"] html = Html(name=html_header, links_dict=link_ds, # fig_dir=os.path.join(self.pars.o,"style"), fig_rpath="../style", RGT_header=False, other_logo="TDF", homepage="../index.html") @@ -1425,9 +1437,10 @@ def gen_html_regiontest(self): "&position=" + rd.dna.chrom + "%3A" + str(rd.dna.initial) + "-" + str( rd.dna.final) + '" style="text-align:left">' + rd.dna.toString(space=True) + '', - rd.dna.orientation, rd.score, rd.motif, rd.orient]) + rd.dna.orientation, rd.score, rd.motif, rd.orient, + '
' + "\n".join(rd.match) + "
"]) html.add_zebra_table(header_list, col_size_list, type_list, data_table, align=align, cell_align="left", - auto_width=True, clean=True) + header_titles=header_titles, auto_width=True, clean=True) html.write(os.path.join(self.pars.o, "region_dbs.html")) ################################################################ diff --git a/rgt/tdf/Statistics.py b/rgt/tdf/Statistics.py index 11f5c6adf..35261a53a 100644 --- a/rgt/tdf/Statistics.py +++ b/rgt/tdf/Statistics.py @@ -287,6 +287,9 @@ def write_stat(self, filename): except: continue + + + def dbs_motif(self, tpx): tpx.motif_statistics() for i, mode in enumerate(tpx.motifs): @@ -388,6 +391,12 @@ def random_test(self, repeats, target_regions, filter_bed, mp, genome_fasta): # try: self.stat["p_value"] = str(min(self.data["region"]["p"])) # except: self.stat["p_value"] = "1" + with open(os.path.join(self.pars.o, "counts_random_matrix.txt"), "w") as f: + for l in self.region_matrix: + print("\t".join([str(x) for x in l]), file=f) + with open(os.path.join(self.pars.o, "counts_dbs.txt"), "w") as f: + print("\t".join([str(x) for x in self.counts_dbs.values()]), file=f) + def target_stat(self, target_regions, tpx, tpxf): # self.stat["DBSs_target_all"] = str(len(self.txpf)) tpx.merge_rbs(rm_duplicate=True, region_set=target_regions, diff --git a/rgt/tdf/triplexTools.py b/rgt/tdf/triplexTools.py index 859f7e7b2..9b688f811 100644 --- a/rgt/tdf/triplexTools.py +++ b/rgt/tdf/triplexTools.py @@ -613,7 +613,7 @@ def run_triplexator(ss, ds, output, l=None, e=None, c=None, fr=None, fm=None, of arg_ptr[0] = "triplexator" # to simulate calling from cmd line for i, s in enumerate(arg_strings): arg_ptr[i + 1] = s - # print(arg_ptr) + # print(arg_strings) triplex_lib.pyTriplexator(len(arg_strings) + 1, arg_ptr) silentremove(os.path.join(output + ".summary")) silentremove(os.path.join(output + ".log")) diff --git a/rgt/viz/Main.py b/rgt/viz/Main.py index 8fc142b4f..78573714e 100644 --- a/rgt/viz/Main.py +++ b/rgt/viz/Main.py @@ -82,6 +82,12 @@ def main(): help='Define whether to perform strand-specific comparison for each reference corresponding to the labels (T or F)') parser_bedprofile.add_argument('-other', metavar=' ', default=None, help='Define whether to count "else" for each reference corresponding to the labels (T or F)') + parser_bedprofile.add_argument('-background', metavar=' ', default=None, + help='Add the background to the first row of the figures (T or F)') + parser_bedprofile.add_argument('-coverage', action="store_true", default=False, + help='Calculate the overlapping region by coverage in bp instead of simple counting') + parser_bedprofile.add_argument('-test', action="store_true", default=False, + help='test script') ################### Projection test ########################################## parser_projection = subparsers.add_parser('projection', @@ -111,8 +117,10 @@ def main(): help='Define the width of single panel. (default: %(default)s)') parser_projection.add_argument('-ph', metavar=' ', type=int, default=3, help='Define the height of single panel. (default: %(default)s)') - parser_projection.add_argument('-cfp', metavar=' ', type=float, default=0.01, + parser_projection.add_argument('-cfp', metavar=' ', type=float, default=0, help='Define the cutoff of the proportion. (default: %(default)s)') + parser_projection.add_argument('-load', action="store_false", default=True, + help='Load the BED files later during processing, which saves memory usage when dealing with large number of BED files.') ################### Intersect Test ########################################## parser_intersect = subparsers.add_parser('intersect', @@ -266,7 +274,7 @@ def main(): parser_lineplot.add_argument('-scol', action="store_true", help="Share y axis among columns. (default: %(default)s)") parser_lineplot.add_argument('-srow', action="store_true", help="Share y axis among rows. (default: %(default)s)") - parser_lineplot.add_argument('-organism', metavar=' ', default='hg19', + parser_lineplot.add_argument('-organism', metavar=' ', help='Define the organism. (default: %(default)s)') parser_lineplot.add_argument('-color', action="store_true", help=help_define_color) parser_lineplot.add_argument('-pw', metavar=' ', type=int, default=3, @@ -293,6 +301,10 @@ def main(): help='Show only the average of the replicates. (default: %(default)s)') parser_lineplot.add_argument('-flip_negative', action="store_true", default=False, help='Flip the negative strand (default: %(default)s)') + parser_lineplot.add_argument('-extend_outside', action="store_true", default=False, + help='Extend the window outside of the given regions and compress the given region into fixed internal. (default: %(default)s)') + parser_lineplot.add_argument('-add_region_number', action="store_true", default=False, + help="Add the number of regions in the axis label. (default: %(default)s)") ################### Heatmap ########################################## parser_heatmap = subparsers.add_parser('heatmap', help='Generate heatmap with various modes.') @@ -456,14 +468,14 @@ def main(): bed_profile.plot_distribution_length() bed_profile.plot_motif_composition() if args.biotype: - bed_profile.plot_ref(ref_dir=args.biotype, tag="Biotype", other=True, strand=True) + bed_profile.plot_ref(ref_dir=args.biotype, tag="Biotype", other=True, strand=True, background=True) if args.repeats: - bed_profile.plot_ref(ref_dir=args.repeats, tag="Repeats", other=True) + bed_profile.plot_ref(ref_dir=args.repeats, tag="Repeats", other=True, background=True) if args.genposi: bed_profile.plot_ref(ref_dir=args.genposi, tag="Genetic position", other=False, strand=False) if args.labels: for i, label in enumerate(args.labels): - bed_profile.plot_ref(ref_dir=args.sources[i], tag=label, other=args.other[i], strand=args.strand[i]) + bed_profile.plot_ref(ref_dir=args.sources[i], tag=label, other=args.other[i], strand=args.strand[i], background=True) bed_profile.write_tables(args.o, args.t) bed_profile.save_fig(filename=os.path.join(args.o, args.t, "figure_" + args.t)) bed_profile.gen_html(args.o, args.t) @@ -482,7 +494,7 @@ def main(): print2(parameter, "\tOutput directory: " + os.path.basename(args.o)) print2(parameter, "\tExperiment title: " + args.t) - projection = Projection(args.r, args.q) + projection = Projection(args.r, args.q, load_bed=args.load) projection.group_refque(args.g) projection.colors(args.c, args.color) @@ -735,7 +747,7 @@ def main(): print("\n################ Lineplot #################") # Read experimental matrix t0 = time.time() - if "reads" not in (args.col, args.c, args.row): + if "reads" not in (args.g, args.col, args.c, args.row): print("Please add 'reads' tag as one of grouping, sorting, or coloring argument.") sys.exit(1) # if "regions" not in (args.col, args.c, args.row): @@ -755,7 +767,8 @@ def main(): organism=args.organism, center=args.center, extend=args.e, rs=args.rs, bs=args.bs, ss=args.ss, df=args.df, dft=args.dft, fields=[args.g, args.col, args.row, args.c], - test=args.test, sense=args.sense, strand=args.strand, flipnegative=args.flip_negative) + test=args.test, sense=args.sense, strand=args.strand, flipnegative=args.flip_negative, + outside=args.extend_outside, add_number=args.add_region_number) # Processing the regions by given parameters print2(parameter, "Step 1/3: Processing regions by given parameters") lineplot.relocate_bed() diff --git a/rgt/viz/bed_profile.py b/rgt/viz/bed_profile.py index 1157ed488..219ccde85 100644 --- a/rgt/viz/bed_profile.py +++ b/rgt/viz/bed_profile.py @@ -23,7 +23,7 @@ class BedProfile: def __init__(self, input_path, organism, args): - + self.testing = args.test if os.path.isdir(input_path): self.beds = [] self.bednames = [] @@ -33,6 +33,8 @@ def __init__(self, input_path, organism, args): name = os.path.basename(f).replace(".bed", "") bed = GenomicRegionSet(name) bed.read(os.path.join(dirpath, f)) + if args.test: + bed.sequences = bed.sequences[0:10] bed.sort() self.beds.append(bed) self.bednames.append(name) @@ -46,6 +48,8 @@ def __init__(self, input_path, organism, args): name = os.path.basename(input_path).replace(".bed", "") bed = GenomicRegionSet(name) bed.read(input_path) + if args.test: + bed.sequences = bed.sequences[0:10] bed.sort() self.beds = [bed] self.bednames = [name] @@ -93,6 +97,11 @@ def __init__(self, input_path, organism, args): self.table_h[self.bednames[i]].append("strand") self.tables[self.bednames[i]].append([r.orientation if r.orientation else "." for r in bed]) self.count_table[bed.name] = {} + if args.coverage: + self.coverage = True + else: + self.coverage = False + self.background = [] def cal_statistics(self): for i, bed in enumerate(self.beds): @@ -197,7 +206,7 @@ def plot_motif_composition(self): ax.set_xticks(range(len(self.bednames))) ax.set_xticklabels(self.bednames, fontsize=7, rotation=20, ha="right") ax.set_ylabel("Percentage %") - # ax.tick_params(axis='x', which='both', top='off', bottom='off', labelbottom='on') + # ax.tick_params(axis='x', which='both', top='off', bottom='off', labelbottom=True) ax.set_ylim([0, 100]) ax.set_xlim([-0.5, len(self.bednames) - 0.5]) # ax.legend(bars, ntlist, ax=ax) @@ -253,7 +262,7 @@ def plot_motif_composition(self): # ax.set_xlabel("Length (bp)") # ax.set_ylabel("Frequency") - def plot_ref(self, ref_dir, tag, other=False, strand=False): + def plot_ref(self, ref_dir, tag, other=False, strand=False, background=False): print("Processing " + tag + " ....") refs = [] refs_names = [] @@ -263,6 +272,8 @@ def plot_ref(self, ref_dir, tag, other=False, strand=False): name = os.path.basename(f).replace(".bed", "") bed = GenomicRegionSet(name) bed.read(os.path.join(ref_dir, f)) + if self.testing: + bed.sequences = bed.sequences[0:10] # bed.merge() refs.append(bed) refs_names.append(name) @@ -270,6 +281,8 @@ def plot_ref(self, ref_dir, tag, other=False, strand=False): name = os.path.basename(ref_dir).replace(".bed", "") bed = GenomicRegionSet(name) bed.read(ref_dir) + if self.testing: + bed.sequences = bed.sequences[0:10] # bed.merge() refs.append(bed) refs_names.append(name) @@ -277,18 +290,44 @@ def plot_ref(self, ref_dir, tag, other=False, strand=False): print("*** Error: Not a valid directory: " + ref_dir) sys.exit(1) + + if background and len(refs) == 1: + background = False + self.background = self.background + [len(ref) for ref in refs] index = natsort.index_natsorted(refs_names) refs = natsort.order_by_index(refs, index) refs_names = natsort.order_by_index(refs_names, index) self.count_tableh = self.count_tableh + refs_names if other: refs_names.append("Else") + self.count_tableh = self.count_tableh + [tag+"_else"] if strand: ref_plus = [] ref_minus = [] for ref in refs: ref_plus.append(ref.filter_strand(strand="+")) ref_minus.append(ref.filter_strand(strand="-")) + if background: + # refs_names.append("Background") + if self.coverage: + # background_counts = [len(ref) for ref in refs] + background_cov = [ref.total_coverage() for ref in refs] + background_prop = [float(100) * b / sum(background_cov) for b in background_cov] + if other: + b = background_cov + [0] + else: + b = background_cov + self.background = self.background + b + else: + background_counts = [ len(ref) for ref in refs ] + background_prop = [ float(100) * b/sum(background_counts) for b in background_counts] + if other: + b = background_counts + [0] + else: + b = background_counts + self.background = self.background + b + else: + self.background = self.background + [0] * len(refs) # Counting through all references overlapping_counts = [] for i, bed in enumerate(self.beds): @@ -296,16 +335,51 @@ def plot_ref(self, ref_dir, tag, other=False, strand=False): if strand: bed_plus = bed.filter_strand(strand="+") bed_minus = bed.filter_strand(strand="-") + if other: + sum_ref_plus = GenomicRegionSet("ref_plus") + sum_ref_minus = GenomicRegionSet("ref_minus") + else: + if other: + sum_ref = GenomicRegionSet("ref") + for j, ref in enumerate(refs): + # print([bed.name, ref.name]) if strand: - cc = bed_plus.count_by_regionset(ref_plus[j]) + bed_minus.count_by_regionset(ref_minus[j]) + if self.coverage: + cc = bed_plus.intersect(ref_plus[j]).total_coverage() + \ + bed_minus.intersect(ref_minus[j]).total_coverage() + else: + cc = bed_plus.count_by_regionset(ref_plus[j]) + bed_minus.count_by_regionset(ref_minus[j]) + if other: + sum_ref_plus.combine(ref_plus[j]) + sum_ref_minus.combine(ref_minus[j]) else: - cc = bed.count_by_regionset(ref) + if self.coverage: + cc = bed.intersect(ref).total_coverage() + else: + cc = bed.count_by_regionset(ref) + if other: + sum_ref.combine(ref) c.append(cc) self.count_table[bed.name][ref.name] = cc if other: - c.append(max(0, len(bed) - sum(c))) + if self.coverage: + c.append(bed.total_coverage() - sum(c)) + else: + if strand: + sum_ref_plus.merge() + sum_ref_minus.merge() + + remain_regions_p = bed_plus.subtract(sum_ref_plus, whole_region=True) + remain_regions_m = bed_minus.subtract(sum_ref_minus, whole_region=True) + remain_regions = remain_regions_p.combine(remain_regions_m, output=True) + else: + sum_ref.merge() + remain_regions = bed.subtract(sum_ref, whole_region=True) + c.append(len(remain_regions)) + for j, ref in enumerate(refs): + self.count_table[bed.name][tag+"_else"] = c[-1] overlapping_counts.append(c) # Tables for i, bed in enumerate(self.beds): @@ -329,7 +403,7 @@ def plot_ref(self, ref_dir, tag, other=False, strand=False): except: ax = self.fig_axs if i == 0: - # print(overlapping_counts) + proportion = [] for counts in overlapping_counts: ss = sum(counts) @@ -337,25 +411,37 @@ def plot_ref(self, ref_dir, tag, other=False, strand=False): proportion.append([x / ss * 100 for x in counts]) else: proportion.append([0 for x in counts]) - # print(proportion) + if background: + if other: + proportion.append(background_prop + [0]) + len_ref = len(refs) + 1 + else: + proportion.append(background_prop) + len_ref = len(refs) + bottom = [0] * (len(self.bednames) + 1) + xlabels = self.bednames + ["Background"] + else: + len_ref = len(refs) + bottom = [0] * len(self.bednames) + xlabels = self.bednames ptable = [] - for j in range(len(refs_names)): + # print(proportion) + # print(len_ref) + for j in range(len_ref): ptable.append([x[j] for x in proportion]) - # print(ptable) width = 0.6 - bottom = [0] * len(self.bednames) for j, y in enumerate(ptable): - ax.bar(range(len(self.bednames)), y, width=width, bottom=bottom, color=color_list[j], + ax.bar(range(len(bottom)), y, width=width, bottom=bottom, color=color_list[j], edgecolor="none", align='center') bottom = [x + y for x, y in zip(bottom, y)] ax.set_title(tag) ax.yaxis.tick_left() - ax.set_xticks(range(len(self.bednames))) - ax.set_xticklabels(self.bednames, fontsize=7, rotation=20, ha="right") + ax.set_xticks(range(len(xlabels))) + ax.set_xticklabels(xlabels, fontsize=7, rotation=20, ha="right") ax.set_ylabel("Percentage %") - # ax.tick_params(axis='x', which='both', top='off', bottom='off', labelbottom='on') + # ax.tick_params(axis='x', which='both', top='off', bottom='off', labelbottom=True) ax.set_ylim([0, 100]) - ax.set_xlim([-0.5, len(self.bednames) - 0.5]) + ax.set_xlim([-0.5, len(xlabels) - 0.5]) plt.tight_layout() elif i > 0: @@ -425,5 +511,10 @@ def write_tables(self, out_dir, title): print("\t".join(line), file=f) with open(os.path.join(target_dir, "count_table.txt"), "w") as f: print("\t".join(["Counts"] + self.count_tableh), file=f) + t = [] for bed in self.bednames: - print("\t".join([bed] + [str(self.count_table[bed][ref]) for ref in self.count_tableh]), file=f) + t.append("\t".join([bed] + [str(self.count_table[bed][ref]) for ref in self.count_tableh])) + if self.background: + t.append("\t".join(["Background", "na"] + [str(v) for v in self.background])) + for tt in t: + print(tt, file=f) diff --git a/rgt/viz/boxplot.py b/rgt/viz/boxplot.py index ff49bb32c..6cb93400e 100644 --- a/rgt/viz/boxplot.py +++ b/rgt/viz/boxplot.py @@ -345,7 +345,7 @@ def plot(self, title, scol, logT=False, ylim=False, pw=3, ph=4): # axarr[i].set_ylim(bottom=0.95) for spine in ['top', 'right', 'left', 'bottom']: axarr[i].spines[spine].set_visible(False) - axarr[i].tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='on') + axarr[i].tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=True) axarr[i].tick_params(labelsize=ticklabelsize + 1) if scol: # plt.setp(axarr[i].get_yticklabels(),visible=False) @@ -354,7 +354,7 @@ def plot(self, title, scol, logT=False, ylim=False, pw=3, ph=4): # axarr[i].tick_params(axis='y', which='both', left='off', right='off', labelbottom='off') else: plt.setp(axarr[i].get_yticklabels(), visible=True) - axarr[i].tick_params(axis='y', which='both', left='on', right='off', labelbottom='on') + axarr[i].tick_params(axis='y', which='both', left=True, right=False, labelbottom=True) # plt.setp(axarr[i].get_yticks(),visible=False) axarr[-1].legend(legends[0:len(self.color_tags)], self.color_tags, loc='center left', handlelength=1, diff --git a/rgt/viz/intersection_test.py b/rgt/viz/intersection_test.py index 945baa77d..114d21d9d 100644 --- a/rgt/viz/intersection_test.py +++ b/rgt/viz/intersection_test.py @@ -246,7 +246,7 @@ def barplot(self, logt=False, percentage=False): ax.yaxis.tick_left() ax.set_xticks([i + 0.5 - 0.5 * width for i in range(len(r_label))]) ax.set_xticklabels(r_label, fontsize=9, rotation=self.xtickrotation, ha=self.xtickalign) - ax.tick_params(axis='x', which='both', top='off', bottom='off', labelbottom='on') + ax.tick_params(axis='x', which='both', top=False, bottom=False, labelbottom=True) ax.set_xlim([0, len(self.counts.values()[ai].keys()) - 0.1]) ax.legend(self.color_tags, loc='center left', handlelength=1, handletextpad=1, @@ -306,7 +306,7 @@ def stackedbar(self): ax.yaxis.tick_left() ax.set_xticks(range(len(r_label))) ax.set_xticklabels(r_label, fontsize=9, rotation=self.xtickrotation, ha=self.xtickalign) - ax.tick_params(axis='x', which='both', top='off', bottom='off', labelbottom='on') + ax.tick_params(axis='x', which='both', top=False, bottom=False, labelbottom=True) ax.set_xlim([-0.5, ind_r + 0.5]) # handles, labels = ax.get_legend_handles_labels() @@ -381,7 +381,7 @@ def percentagebar(self): ax.set_xticks(range(len(r_label))) ax.set_xticklabels(r_label, fontsize=9, rotation=self.xtickrotation, ha=self.xtickalign) - ax.tick_params(axis='x', which='both', top='off', bottom='off', labelbottom='on') + ax.tick_params(axis='x', which='both', top=False, bottom=False, labelbottom=True) ax.set_xlim([-0.5, ind_r + 0.5]) ax.set_ylim([0, 100]) @@ -745,7 +745,7 @@ def comb_stacked_plot(self): ax.yaxis.tick_left() ax.set_xticks(range(len(q_label))) ax.set_xticklabels(q_label, fontsize=9, rotation=self.xtickrotation, ha=self.xtickalign) - ax.tick_params(axis='x', which='both', top='off', bottom='off', labelbottom='on') + ax.tick_params(axis='x', which='both', top=False, bottom=False, labelbottom=True) ax.set_xlim([-0.5, len(q_label) + 0.5]) ax.set_ylim([0, 1]) diff --git a/rgt/viz/jaccard_test.py b/rgt/viz/jaccard_test.py index 68aa157db..f04192939 100644 --- a/rgt/viz/jaccard_test.py +++ b/rgt/viz/jaccard_test.py @@ -160,13 +160,13 @@ def plot(self, logT=False, pw=3, ph=3): # axarr[i].set_ylim(bottom=0.95) for spine in ['top', 'right', 'left', 'bottom']: axarr[i].spines[spine].set_visible(False) - axarr[i].tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='off') + axarr[i].tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False) if i > 0: plt.setp(axarr[i].get_yticklabels(), visible=False) # plt.setp(axarr[i].get_yticks(),visible=False) axarr[i].minorticks_off() - axarr[i].tick_params(axis='y', which='both', left='off', right='off', labelbottom='off') + axarr[i].tick_params(axis='y', which='both', left=False, right=False, labelbottom=False) plt.setp([a.get_yticklabels() for a in axarr[1:]], visible=False) axarr[-1].legend(legends[0:len(self.jlist[t][r].keys())], self.jlist[t][r].keys(), loc='center left', diff --git a/rgt/viz/lineplot.py b/rgt/viz/lineplot.py index e3e66dd9a..078ddc4ba 100644 --- a/rgt/viz/lineplot.py +++ b/rgt/viz/lineplot.py @@ -7,7 +7,7 @@ import os from collections import OrderedDict, defaultdict from scipy.stats import stats - +from scipy.interpolate import spline # Local Libraries # Distal Libraries from rgt.Util import Html @@ -27,12 +27,12 @@ class Lineplot: def __init__(self, em_path, title, annotation, organism, center, extend, rs, bs, ss, - df, dft, fields, test, sense, strand, flipnegative): + df, dft, fields, test, sense, strand, flipnegative, outside, add_number): # Read the Experimental Matrix self.title = title self.exps = ExperimentalMatrix() - self.exps.read(em_path, test=test, add_region_len=True) + self.exps.read(em_path, test=test, add_region_len=add_number) for f in self.exps.fields: if f not in ['name', 'type', 'file', "reads", "regions", "factors"]: self.exps.match_ms_tags(f, test=test) @@ -63,32 +63,44 @@ def __init__(self, em_path, title, annotation, organism, center, extend, rs, bs, self.processed_beds = [] self.processed_bedsF = [] # Processed beds to be flapped + self.outside = outside def relocate_bed(self): - - for bed in self.beds: - if self.center == 'bothends': - newbed = bed.relocate_regions(center='leftend', - left_length=self.extend + self.bs, - right_length=self.extend + self.bs) - self.processed_beds.append(newbed) - newbedF = bed.relocate_regions(center='rightend', - left_length=self.extend + self.bs, - right_length=self.extend + self.bs) - self.processed_bedsF.append(newbedF) - elif self.center == 'upstream' or self.center == 'downstream': - allbed = bed.relocate_regions(center=self.center, - left_length=self.extend + self.bs, - right_length=self.extend + self.bs) - newbed = allbed.filter_strand(strand="+") - self.processed_beds.append(newbed) - newbedF = allbed.filter_strand(strand="-") - self.processed_bedsF.append(newbedF) - else: - allbed = bed.relocate_regions(center=self.center, - left_length=self.extend + int(0.5 * self.bs) + 2 * self.ss, - right_length=self.extend + int(0.5 * self.bs) + 2 * self.ss) + if not self.outside: + + for bed in self.beds: + if self.center == 'bothends': + newbed = bed.relocate_regions(center='leftend', + left_length=self.extend + self.bs, + right_length=self.extend + self.bs) + self.processed_beds.append(newbed) + newbedF = bed.relocate_regions(center='rightend', + left_length=self.extend + self.bs, + right_length=self.extend + self.bs) + self.processed_bedsF.append(newbedF) + elif self.center == 'upstream' or self.center == 'downstream': + allbed = bed.relocate_regions(center=self.center, + left_length=self.extend + self.bs, + right_length=self.extend + self.bs) + self.processed_beds.append(allbed) + self.processed_bedsF.append(False) + # newbed = allbed.filter_strand(strand="+") + # self.processed_beds.append(newbed) + # newbedF = allbed.filter_strand(strand="-") + # self.processed_bedsF.append(newbedF) + else: + allbed = bed.relocate_regions(center=self.center, + left_length=self.extend + int(0.5 * self.bs) + 2 * self.ss, + right_length=self.extend + int(0.5 * self.bs) + 2 * self.ss) + self.processed_beds.append(allbed) + self.processed_bedsF.append(False) + else: + for bed in self.beds: + allbed = bed.extend(left=self.extend + int(0.5 * self.bs) + 2 * self.ss, + right=self.extend + int(0.5 * self.bs) + 2 * self.ss, w_return=True) self.processed_beds.append(allbed) + self.processed_bedsF.append(False) + def group_tags(self, groupby, rowby, columnby, colorby): """Generate the tags for the grouping of plot @@ -138,15 +150,6 @@ def gen_cues(self): self.cuebed = OrderedDict() self.cuebam = OrderedDict() - # if self.annotation: - # #all_tags = [] - # #for dictt in self.exps.fieldsDict.values(): - # # for tag in dictt.keys(): - # # all_tags.append(tag) - # for bed in self.bednames: - # # self.cuebed[bed] = set([bed]+all_tags) - # self.cuebed[bed] = set([bed]) - # else: for bed in self.bednames: self.cuebed[bed] = set(tag_from_r(self.exps, self.tag_type, bed)) # print(self.cuebed[bed]) @@ -179,7 +182,6 @@ def annot_ind(bednames, tags): for c in self.column_tags: data[g][r][c] = OrderedDict() for cc in self.color_tags: - # if self.df: data[s][g][c] = [] data[g][r][c][cc] = OrderedDict() if not self.dft: dfs = [cc] @@ -194,12 +196,12 @@ def annot_ind(bednames, tags): for d in dfs: data[g][r][c][cc][d] = defaultdict(list) for bed in self.cuebed.keys(): + # print(self.cuebed[bed]) # print(set([s,g,c,d])) # print(self.cuebed[bed].issubset(set([s,g,c,d]))) - if len(self.cuebed[bed].intersection({g, r, c, cc, d})) > 2 or self.cuebed[ - bed].issubset( - {g, r, c, cc, d}): + if len(self.cuebed[bed].intersection({g, r, c, cc, d})) > 2 or \ + self.cuebed[bed].issubset({g, r, c, cc, d}): # if self.cuebed[bed] <= set([s,g,c]): for bam in self.cuebam.keys(): @@ -208,156 +210,22 @@ def annot_ind(bednames, tags): if self.cuebam[bam] <= {g, r, c, cc, d}: i = self.bednames.index(bed) j = self.readsnames.index(bam) - # print(bed + "." + bam) - - # if len(self.processed_beds[i]) == 0: - # try: - # data[s][g][c][d].append(numpy.empty(1, dtype=object)) - # except: - # data[s][g][c][d] = [numpy.empty(1, dtype=object)] - # continue + inputs = [bed, bam, self.processed_beds[i], self.processed_bedsF[i], + g, r, c, cc, d, self.reads[j], + self.rs, self.bs, self.ss, self.center, heatmap, logt, + self.sense, self.strand, self.flipnegative, + self.outside, self.extend] + ######################################################################### if mp > 0: # Multiple processing - mp_input.append([self.processed_beds[i], self.reads[j], - self.rs, self.bs, self.ss, self.center, heatmap, logt, - g, r, c, cc, d]) + mp_input.append(inputs) data[g][r][c][cc][d] = None ######################################################################### else: # Single thread - ts = time.time() - cov = CoverageSet(bed + "." + bam, self.processed_beds[i]) - - # print(len(self.processed_beds[i])) - if "Conservation" in [g, r, c, cc, d]: - cov.phastCons46way_score(stepsize=self.ss) - - elif ".bigwig" in self.reads[j].lower() or ".bw" in self.reads[ - j].lower(): - cov.coverage_from_bigwig(bigwig_file=self.reads[j], - stepsize=self.ss) - else: - if not self.sense and not self.strand: - cov.coverage_from_bam(bam_file=self.reads[j], - extension_size=self.rs, binsize=self.bs, - stepsize=self.ss) - if normRPM: cov.normRPM() - else: # Sense specific - cov.coverage_from_bam(bam_file=self.reads[j], - extension_size=self.rs, binsize=self.bs, - stepsize=self.ss, - get_sense_info=self.sense, - get_strand_info=self.strand, - paired_reads=True) - cov.array_transpose() - if normRPM: cov.normRPM() - - if self.center == "midpoint" and self.flipnegative: - for k, re in enumerate(self.processed_beds[i]): - if re.orientation == "-": - # print(k) - # print(cov.coverage[k]) - cov.coverage[k] = cov.coverage[k][::-1] - - # When bothends, consider the fliping end - if self.center == 'bothends' or self.center == 'upstream' or self.center == 'downstream': - if "Conservation" in [g, r, c, cc, d]: - flap = CoverageSet("for flap", self.processed_bedsF[i]) - flap.phastCons46way_score(stepsize=self.ss) - ffcoverage = numpy.fliplr(flap.coverage) - cov.coverage = numpy.concatenate((cov.coverage, ffcoverage), - axis=0) - elif ".bigwig" in self.reads[j].lower() or ".bw" in self.reads[ - j].lower(): - flap = CoverageSet("for flap", self.processed_bedsF[i]) - flap.coverage_from_bigwig(bigwig_file=self.reads[j], - stepsize=self.ss) - ffcoverage = numpy.fliplr(flap.coverage) - cov.coverage = numpy.concatenate((cov.coverage, ffcoverage), - axis=0) - else: - flap = CoverageSet("for flap", self.processed_bedsF[i]) - if not self.sense: - flap.coverage_from_bam(self.reads[j], - extension_size=self.rs, - binsize=self.bs, stepsize=self.ss) - if normRPM: flap.normRPM() - else: # Sense specific - flap.coverage_from_bam(bam_file=self.reads[j], - extension_size=self.rs, - binsize=self.bs, - stepsize=self.ss, - get_sense_info=True, - paired_reads=True) - flap.array_transpose(flip=True) - if normRPM: flap.normRPM() - ffcoverage = numpy.fliplr(flap.coverage) - try: - cov.coverage = numpy.concatenate((cov.coverage, ffcoverage), - axis=0) - except: - pass - - if self.sense: - cov.transpose_cov1 = numpy.concatenate((cov.transpose_cov1, - flap.transpose_cov1), - axis=0) - cov.transpose_cov2 = numpy.concatenate((cov.transpose_cov2, - flap.transpose_cov2), - axis=0) - - # Averaging the coverage of all regions of each bed file - if heatmap: - if logt: - data[g][r][c][cc][d] = numpy.log10(numpy.vstack( - cov.coverage) + 1) # Store the array into data list - else: - data[g][r][c][cc][d] = numpy.vstack( - cov.coverage) # Store the array into data list - else: - if len(cov.coverage) == 0: - data[g][r][c][cc][d] = None - print("** Warning: Cannot open " + self.reads[j]) - continue - else: - for i, car in enumerate(cov.coverage): - if i == 0: - avearr = numpy.array(car, ndmin=2) - else: - # avearr = numpy.vstack((avearr, np.array(car, ndmin=2))) - try: - avearr = numpy.vstack( - (avearr, numpy.array(car, ndmin=2))) - except: - print(bed + "." + bam + "." + str(i)) - if log: - avearr = numpy.log10(avearr + 1) - - avearr = numpy.average(avearr, axis=0) - if self.sense or self.strand: - if log: - sense_1 = numpy.average( - numpy.log2(cov.transpose_cov1 + 1), axis=0) - sense_2 = numpy.average( - numpy.log2(cov.transpose_cov2 + 1), axis=0) - else: - sense_1 = numpy.average(cov.transpose_cov1, axis=0) - sense_2 = numpy.average(cov.transpose_cov2, axis=0) - cut_end = int(self.bs / self.ss) - avearr = avearr[cut_end:-cut_end] - data[g][r][c][cc][d]["all"].append(avearr) - - if self.sense or self.strand: - sense_1 = sense_1[cut_end:-cut_end] - sense_2 = sense_2[cut_end:-cut_end] - data[g][r][c][cc][d]["sense_1"].append(sense_1) - data[g][r][c][cc][d]["sense_2"].append(sense_2) - + cov_res = compute_coverage(inputs) + data[g][r][c][cc][d] = cov_res[-1] bi += 1 - te = time.time() - print2(self.parameter, - "\t" + str(bi) + "\t" + "{0:30}\t--{1:<5.1f}\tsec".format( - bed + "." + bam, ts - te)) if mp > 0: pool = MyPool(mp) @@ -370,17 +238,12 @@ def annot_ind(bednames, tags): for cc in data[g][r][c].keys(): for d in data[g][r][c][cc].keys(): for out in mp_output: - if out[0] == g and out[1] == r and out[2] == c and out[3] == cc and out[3] == d: - if self.df: - try: - data[g][r][c][cc][d][-1].append(out[4]) - except: - data[g][r][c][cc][d] = [[out[4]]] + if out[0:5] == [g, r, c, cc, d]: + if data[g][r][c][cc][d]: + data[g][r][c][cc][d]["all"].append(out[-1]["all"][0]) else: - try: - data[g][r][c][cc][d].append(out[4]) - except: - data[g][r][c][cc][d] = [out[4]] + data[g][r][c][cc][d] = out[-1] + if average: for g in data.keys(): for r in data[g].keys(): @@ -391,18 +254,15 @@ def annot_ind(bednames, tags): data[g][r][c][cc][d]["all"]) > 1: a = numpy.array(data[g][r][c][cc][d]["all"]) averaged_array = numpy.array(numpy.average(a, axis=0)) - # print(averaged_array) - # sys.exit(1) data[g][r][c][cc][d]["all"] = [averaged_array] - # print(len(data[s][g][c][d]["all"])) if self.df: for g in data.keys(): for r in data[g].keys(): for c in data[g][r].keys(): for cc in data[g][r][c].keys(): for d in data[g][r][c][cc].keys(): - if isinstance(data[g][r][c][cc][d]["all"], list) and len( - data[g][r][c][cc][d]["all"]) > 1: + if isinstance(data[g][r][c][cc][d]["all"], list) and \ + len(data[g][r][c][cc][d]["all"]) > 1: diff = numpy.subtract(data[g][r][c][cc][d]["all"][0], data[g][r][c][cc][d]["all"][1]) data[g][r][c][cc][d]["df"].append(diff.tolist()) @@ -427,8 +287,8 @@ def plot(self, output, printtable=False, scol=False, srow=False, w=2, h=2, ylog= ticklabelsize = w * 1.5 else: ticklabelsize = w * 3 - tw = len(self.data.values()[0].keys()) * w - th = len(self.data.keys()) * (h * 0.8) + tw = len(self.data.values()[0].values()[0].keys()) * w + th = len(self.data.values()[0].keys()) * (h * 0.8) for g in self.group_tags: @@ -477,17 +337,26 @@ def plot(self, output, printtable=False, scol=False, srow=False, w=2, h=2, ylog= pt = self.data[g][r][c][cc][d]["df"] else: pt = self.data[g][r][c][cc][d]["all"] + # print(pt) for l, y in enumerate(pt): - # print(y) yaxmax[ic] = max(numpy.amax(y), yaxmax[ic]) sx_ymax[ir] = max(numpy.amax(y), sx_ymax[ir]) if self.df: yaxmin[ic] = min(numpy.amin(y), yaxmin[ic]) sx_ymin[ir] = min(numpy.amin(y), sx_ymin[ir]) - x = numpy.linspace(-self.extend, self.extend, len(y)) - ax.plot(x, y, color=self.colors[cc], lw=linewidth, label=cc) + if not self.outside: + x = numpy.linspace(-self.extend, self.extend, len(y)) + else: + x = numpy.linspace(-int(self.extend*1.5), int(self.extend*1.5), len(y)) + + ## smoothening line + xnew = numpy.linspace(x[0], x[-1], 500) + ynew = spline(x, y, xnew) + + # ax.plot(x, y, color=self.colors[cc], lw=linewidth, label=cc) + ax.plot(xnew, ynew, color=self.colors[cc], lw=linewidth, label=cc) if ir < nit - 1: ax.set_xticklabels([]) # Processing for future output @@ -533,18 +402,26 @@ def plot(self, output, printtable=False, scol=False, srow=False, w=2, h=2, ylog= ym = 1.2 * max(max(yaxmax), max(sx_ymax)) ax.set_ylim([-ym, ym]) - # ax.get_yaxis().set_label_coords(-0.1, 0.5) + # ax.get_yaxis().set_label_coords(-2, 0.5) ax.set_xlim([-self.extend, self.extend]) plt.setp(ax.get_xticklabels(), fontsize=ticklabelsize, rotation=rot, ha='right') plt.setp(ax.get_yticklabels(), fontsize=ticklabelsize) ax.locator_params(axis='x', nbins=4) ax.locator_params(axis='y', nbins=3) + if self.outside: + + xlim_value = int(self.extend * 1.5) + ax.set_xlim([-xlim_value, xlim_value]) + ax.set_xticks([-xlim_value, -int(self.extend*0.5), int(self.extend*0.5), xlim_value]) + ax.set_xticklabels([str(-self.extend), "Start", "End", str(self.extend)]) + if printtable: output_array(pArr, directory=output, folder=self.title, filename="plot_table_" + g + ".txt") handles = [] labels = [] + ylabel_x = 0.1 for ir, r in enumerate(self.data[g].keys()): if ylog: nr = r + " (log10)" @@ -552,14 +429,14 @@ def plot(self, output, printtable=False, scol=False, srow=False, w=2, h=2, ylog= nr = r try: axs[ir, 0].set_ylabel("{}".format(nr), fontsize=ticklabelsize + 1) - axs[ir, 0].get_yaxis().set_label_coords(-0.1, 0.5) + axs[ir, 0].get_yaxis().set_label_coords(-ylabel_x, 0.5) except: try: axs[ir].set_ylabel("{}".format(nr), fontsize=ticklabelsize + 1) - axs[ir].get_yaxis().set_label_coords(-0.1, 0.5) + axs[ir].get_yaxis().set_label_coords(-ylabel_x, 0.5) except: axs.set_ylabel("{}".format(nr), fontsize=ticklabelsize + 1) - axs.get_yaxis().set_label_coords(-0.1, 0.5) + axs.get_yaxis().set_label_coords(-ylabel_x, 0.5) for ic, c in enumerate(self.data[g][r].keys()): try: @@ -746,8 +623,8 @@ def heatmap(self, logt): # axs[bi, bj].locator_params(axis = 'y', nbins = 4) for spine in ['top', 'right', 'left', 'bottom']: axs[bi, bj].spines[spine].set_visible(False) - axs[bi, bj].tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='on') - axs[bi, bj].tick_params(axis='y', which='both', left='off', right='off', labelleft='off') + axs[bi, bj].tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=True) + axs[bi, bj].tick_params(axis='y', which='both', left=False, right=False, labelleft=False) # if bj > 0: # plt.setp(axs[bi, bj].get_yticklabels(),visible=False) diff --git a/rgt/viz/projection_test.py b/rgt/viz/projection_test.py index e9e3b6a65..3b618989e 100644 --- a/rgt/viz/projection_test.py +++ b/rgt/viz/projection_test.py @@ -22,17 +22,17 @@ class Projection: - def __init__(self, reference_path, query_path): + def __init__(self, reference_path, query_path, load_bed=True): # Reference self.rEM = ExperimentalMatrix() - self.rEM.read(reference_path) - self.rEM.remove_empty_regionset() + self.rEM.read(reference_path,load_bed=load_bed) + # self.rEM.remove_empty_regionset() self.references = self.rEM.get_regionsets() self.referencenames = self.rEM.get_regionsnames() # Query self.qEM = ExperimentalMatrix() self.qEM.read(query_path) - self.qEM.remove_empty_regionset() + # self.qEM.remove_empty_regionset() self.query = self.qEM.get_regionsets() self.querynames = self.qEM.get_regionsnames() self.parameter = [] @@ -69,11 +69,11 @@ def set_background(self, bed_path): self.background = OrderedDict() for ty in self.groupedreference.keys(): self.background[ty] = bg - rlist = [r.trim_by(background=bg) for r in self.groupedreference[ty]] - self.groupedreference[ty] = rlist - - qlist = [q.trim_by(background=bg) for q in self.groupedquery[ty]] - self.groupedquery[ty] = qlist + # rlist = [r.trim_by(background=bg) for r in self.groupedreference[ty]] + # self.groupedreference[ty] = rlist + # + # qlist = [q.trim_by(background=bg) for q in self.groupedquery[ty]] + # self.groupedquery[ty] = qlist def projection_test(self, organism): self.bglist = OrderedDict() @@ -104,11 +104,14 @@ def projection_test(self, organism): self.interq_list[ty][r.name] = OrderedDict() self.lenlist[r.name] = len(r) for j, q in enumerate(self.groupedquery[ty]): - # print(r.name, q.name, sep="\t") + # print([ty, r.name, q.name]) if r.name == q.name: continue else: - bg, ratio, p, interq = r.projection_test(q, organism, extra=True, background=bgset) + rr = self.rEM.get_regionset(name=r.name) + # print([len(rr), len(q)]) + print(".", end="") + bg, ratio, p, interq = rr.projection_test(q, organism, extra=True, background=bgset) self.bglist[ty][r.name][q.name] = bg self.qlist[ty][r.name][q.name] = ratio self.plist[ty][r.name][q.name] = p @@ -190,7 +193,7 @@ def plot(self, logt=None, pw=3, ph=3): ax[ind_ty].set_ylabel('Percentage of intersected regions', fontsize=8) ax[ind_ty].set_xticks([i + 0.5 - 0.5 * width for i in range(len(r_label))]) ax[ind_ty].set_xticklabels(r_label, rotation=30, ha="right", fontsize=8) - ax[ind_ty].tick_params(axis='x', which='both', top='off', bottom='off', labelbottom='on') + ax[ind_ty].tick_params(axis='x', which='both', top=False, bottom=False, labelbottom=True) handles, labels = ax[ind_ty].get_legend_handles_labels() # uniq_labels = unique(labels) @@ -287,7 +290,6 @@ def gen_html(self, directory, title, args, align=50): data_table.append( [str(ind_ty), r, q, rlen, qlen, propor, backv, value2str(pv), value2str(pvn)]) statistic_table.append([r, q, rlen, qlen, propor, backv, value2str(pv), value2str(pvn)]) - html.add_zebra_table(header_list, col_size_list, type_list, data_table, align=align, sortable=True) output_array(statistic_table, directory=directory, folder=title, filename="statistics" + ty + ".txt") @@ -410,7 +412,7 @@ def to_percentage(x, pos=0): ax.minorticks_off() ax.set_yticks([x + 0.5 for x in range(len(self.chrom_list))]) ax.set_yticklabels(self.chrom_list, rotation=0, ha="right") - ax.tick_params(axis='y', which='both', top='off', bottom='off', labelbottom='on') + ax.tick_params(axis='y', which='both', top=False, bottom=False, labelbottom=True) ax.legend(self.disperDict[ty].keys(), loc='center left', handlelength=1, handletextpad=1, columnspacing=2, borderaxespad=0., prop={'size': 10}, bbox_to_anchor=(1.05, 0.5)) diff --git a/rgt/viz/shared_function.py b/rgt/viz/shared_function.py index 9e22604d1..43e66929f 100644 --- a/rgt/viz/shared_function.py +++ b/rgt/viz/shared_function.py @@ -8,6 +8,7 @@ import pickle import re import time +from collections import defaultdict from shutil import copyfile @@ -390,48 +391,140 @@ def multiple_correction(dic): def compute_coverage(inputs): """ - bed, bam, rs, bs, ss, center, heatmap, logt, s, g, c, d + [1] bedname, bamname, processed_beds, processed_bedsF, + [5] g, r, c, cc, d, read_file, + [11] rs, bs, ss, center, heatmap, logt, + [17] sense, strand, flipnegative, center, outside, extend] """ ts = time.time() - cov = CoverageSet(inputs[0].name + ".", inputs[0]) - if ".bigWig" in inputs[1] or ".bw" in inputs[1]: - cov.coverage_from_bigwig(bigwig_file=inputs[1], stepsize=inputs[4]) + normRPM = True + + [ bedname, bamname, processed_beds, processed_bedsF, g, r, c, cc, d, read_file, + rs, bs, ss, center_end, heatmap, logt, sense, strand, flipnegative, outside, extend ] = inputs + + res = defaultdict(list) + + if len(processed_beds) == 0: + res["all"].append(numpy.zeros(5)) + return [g, r, c, cc, d, res] else: - cov.coverage_from_bam(bam_file=inputs[1], extension_size=inputs[2], binsize=inputs[3], stepsize=inputs[4]) - cov.normRPM() - # When bothends, consider the fliping end - if inputs[5] == 'bothends': - flap = CoverageSet("for flap", inputs[0]) - flap.coverage_from_bam(inputs[1], extension_size=inputs[2], binsize=inputs[3], stepsize=inputs[4]) - ffcoverage = numpy.fliplr(flap.coverage) - cov.coverage = numpy.concatenate((cov.coverage, ffcoverage), axis=0) - # Averaging the coverage of all regions of each bed file - if inputs[6]: - if inputs[7]: - result = numpy.log10(numpy.vstack(cov.coverage)) # Store the array into data list + cov = CoverageSet(bedname + "." + bamname, processed_beds) + + if "Conservation" in [g, r, c, cc, d]: + cov.phastCons46way_score(stepsize=ss) + elif ".bigwig" in read_file.lower() or ".bw" in read_file.lower(): + cov.coverage_from_bigwig(bigwig_file=read_file, stepsize=ss) else: - result = numpy.vstack(cov.coverage) # Store the array into data list - else: - # print(cov.coverage) - for i, car in enumerate(cov.coverage): - car = numpy.delete(car, [0, 1]) - if i == 0: - avearr = np.array(car) - lenr = car.shape[0] - elif car.shape[0] == lenr: - avearr = numpy.vstack((avearr, car)) + if not sense and not strand: + cov.coverage_from_bam(bam_file=read_file, extension_size=rs, binsize=bs, stepsize=ss) + if normRPM: cov.normRPM() + else: # Sense specific + cov.coverage_from_bam(bam_file=read_file, extension_size=rs, binsize=bs, stepsize=ss, + get_sense_info=sense, get_strand_info=strand, paired_reads=True) + cov.array_transpose() + if normRPM: cov.normRPM() + + if center_end == "midpoint" and flipnegative: + for k, re in enumerate(processed_beds): + if re.orientation == "-": + cov.coverage[k] = cov.coverage[k][::-1] + + # When bothends, consider the fliping end + # if center_end == 'bothends' or center_end == 'upstream' or center_end == 'downstream': + if center_end == 'bothends': + if "Conservation" in [g, r, c, cc, d]: + flap = CoverageSet("for flap", processed_bedsF) + flap.phastCons46way_score(stepsize=ss) + ffcoverage = numpy.fliplr(flap.coverage) + cov.coverage = numpy.concatenate((cov.coverage, ffcoverage), + axis=0) + elif ".bigwig" in read_file.lower() or ".bw" in read_file.lower(): + flap = CoverageSet("for flap", processed_bedsF) + flap.coverage_from_bigwig(bigwig_file=read_file, stepsize=ss) + ffcoverage = numpy.fliplr(flap.coverage) + cov.coverage = numpy.concatenate((cov.coverage, ffcoverage), axis=0) + else: + flap = CoverageSet("for flap", processed_bedsF) + if not sense: + flap.coverage_from_bam(read_file, extension_size=rs, binsize=bs, stepsize=ss) + if normRPM: flap.normRPM() + else: # Sense specific + flap.coverage_from_bam(bam_file=read_file, extension_size=rs, binsize=bs, + stepsize=ss, get_sense_info=True, paired_reads=True) + flap.array_transpose(flip=True) + if normRPM: flap.normRPM() + ffcoverage = numpy.fliplr(flap.coverage) + try: + cov.coverage = numpy.concatenate((cov.coverage, ffcoverage), axis=0) + except: + pass + + if sense: + cov.transpose_cov1 = numpy.concatenate((cov.transpose_cov1, flap.transpose_cov1), axis=0) + cov.transpose_cov2 = numpy.concatenate((cov.transpose_cov2, flap.transpose_cov2), axis=0) + # Extend outside + if outside: + new_arrays = [] + for ar in cov.coverage: + ss_side = int(extend / ss) - 1 + # if len(ar) < 2*ss_side + left_ar = ar[0:ss_side] + right_ar = ar[-ss_side:] + rest = ar[ss_side:-ss_side] + + # print([len[ar], len(left_ar), len(rest), len(right_ar)]) + try: + xp = numpy.linspace(0, ss_side, len(rest)) + rest = numpy.interp(range(ss_side), xp=xp, fp=rest) + nar = numpy.concatenate((left_ar, rest)) + nar = numpy.concatenate((nar, right_ar)) + new_arrays.append(nar) + except: + print([ss_side, extend, ss, len(ar), rest]) + cov.coverage = new_arrays + + # Averaging the coverage of all regions of each bed file + if heatmap: + if logt: + res = numpy.log10(numpy.vstack(cov.coverage) + 1) # Store the array into data list + else: + res = numpy.vstack(cov.coverage) # Store the array into data list + else: + if len(cov.coverage) == 0: + res = None + print("** Warning: Cannot open " + read_file) else: - pass - # avearr = numpy.array(cov.coverage) - # print(avearr) - # print(avearr.shape) - avearr = numpy.average(avearr, axis=0) - # numpy.transpose(avearr) - result = [inputs[8], inputs[9], inputs[10], inputs[11], avearr] # Store the array into data list - te = time.time() - print("\tComputing " + os.path.basename(inputs[1]) + " . " + inputs[0].name + "\t\t" + str( - datetime.timedelta(seconds=round(te - ts)))) - return result + for i, car in enumerate(cov.coverage): + if i == 0: + avearr = numpy.array(car, ndmin=2) + else: + avearr = numpy.vstack((avearr, numpy.array(car, ndmin=2))) + if logt: + avearr = numpy.log10(avearr + 1) + + avearr = numpy.average(avearr, axis=0) + if sense or strand: + if logt: + sense_1 = numpy.average(numpy.log2(cov.transpose_cov1 + 1), axis=0) + sense_2 = numpy.average(numpy.log2(cov.transpose_cov2 + 1), axis=0) + else: + sense_1 = numpy.average(cov.transpose_cov1, axis=0) + sense_2 = numpy.average(cov.transpose_cov2, axis=0) + cut_end = int(bs / ss) + avearr = avearr[cut_end:-cut_end] + res["all"].append(avearr) + + if sense or strand: + sense_1 = sense_1[cut_end:-cut_end] + sense_2 = sense_2[cut_end:-cut_end] + res["sense_1"].append(sense_1) + res["sense_2"].append(sense_2) + + result = [g, r, c, cc, d, res] # Store the array into data list + te = time.time() + print("\tComputing " + bedname + " . " + bamname + "\t\t" + + str(datetime.timedelta(seconds=round(te - ts)))) + return result def mp_count_intersets(inps): diff --git a/setup.py b/setup.py index 4e46063a3..22245d81a 100644 --- a/setup.py +++ b/setup.py @@ -76,7 +76,7 @@ def find_version(*file_paths): common_deps = ["cython", "numpy>=1.4.0", - "scipy>=0.7.0", + "scipy>=1.0.0", "pysam>=0.12.0", "pyBigWig", "PyVCF"] @@ -89,9 +89,6 @@ def find_version(*file_paths): bin_dir = "linux" libRGT = "librgt_linux.so" triplexes_file = "lib/libtriplexator.so" - # ngslib doesn't support Python 3 (28/08/2017) - if not p3_supported: - common_deps.append("ngslib") if not p3_supported: # needed to be able to do "import configparser", which is the new Python 3 name for ConfigParser @@ -305,7 +302,7 @@ def recursive_chown_chmod(path_to_walk, uid, gid, file_permission, path_permissi data_config_file.write("[MotifData]\n") data_config_file.write("pwm_dataset: motifs\n") data_config_file.write("logo_dataset: logos\n") -data_config_file.write("repositories: hocomoco\n\n") +data_config_file.write("repositories: jaspar_vertebrates\n\n") data_config_file.write("[HmmData]\n") data_config_file.write("default_hmm_dnase: fp_hmms/dnase.hmm\n") data_config_file.write("default_hmm_dnase_bc: fp_hmms/dnase_bc.hmm\n") @@ -372,8 +369,9 @@ def recursive_chown_chmod(path_to_walk, uid, gid, file_permission, path_permissi "atac_bias_table_F.txt", "atac_bias_table_R.txt", "atac_histone.hmm", "atac_histone_bc.hmm", "double_hit_bias_table_F.txt", "double_hit_bias_table_R.txt", "H3K4me3_proximal.hmm", "LearnDependencyModel.jar", "SlimDimontPredictor.jar", "test.fa"], - "motifs": ["jaspar_vertebrates", "uniprobe_primary", "uniprobe_secondary", "hocomoco", "hocomoco.fpr", - "jaspar_vertebrates.fpr", "uniprobe_primary.fpr", "uniprobe_secondary.fpr"], + "motifs": ["jaspar_vertebrates", "uniprobe_primary", "uniprobe_secondary", "hocomoco", + "jaspar_vertebrates.fpr", "uniprobe_primary.fpr", "uniprobe_secondary.fpr", "hocomoco.fpr", + "jaspar_vertebrates.mtf", "uniprobe_primary.mtf", "uniprobe_secondary.mtf", "hocomoco.mtf"], "fig": ["rgt_logo.gif", "style.css", "default_motif_logo.png", "jquery-1.11.1.js", "jquery.tablesorter.min.js", "tdf_logo.png", "viz_logo.png"], } diff --git a/test/hint.sh b/test/hint.sh index aa234b631..a08c42553 100755 --- a/test/hint.sh +++ b/test/hint.sh @@ -5,36 +5,38 @@ set -e RGTTEST=${RGTTEST:-"$HOME/rgt_test"} DIR="${RGTTEST}/HINT" -mkdir -p $DIR -cd ${DIR} +mkdir -p ${DIR} echo "**********************************************" echo "Testing HINT" +cd ${DIR} echo "Running HINT using only DNase-seq data.." url="http://134.130.18.8/open_data/hint/tutorial/HINT_DNaseTest.tar.gz" echo "Downloading test data." wget -qO- -O HINT_DNaseTest.tar.gz $url && tar xvfz HINT_DNaseTest.tar.gz && rm HINT_DNaseTest.tar.gz -cd ${DIR}/HINT_DNaseTest +cd HINT_DNaseTest rgt-hint footprinting --dnase-seq DNase.bam DNasePeaks.bed echo "Running HINT-BC using only DNase-seq data.." rgt-hint footprinting --dnase-seq --bias-correction DNase.bam DNasePeaks.bed +cd ${DIR} echo "Running HINT using only ATAC-seq data.." url="http://134.130.18.8/open_data/hint/tutorial/HINT_ATACTest.tar.gz" echo "Downloading test data." wget -qO- -O HINT_ATACTest.tar.gz $url && tar xvfz HINT_ATACTest.tar.gz && rm HINT_ATACTest.tar.gz -cd ${DIR}/HINT_ATACTest +cd HINT_ATACTest echo "Running HINT using ATAC-seq data.." rgt-hint footprinting --atac-seq ATAC.bam ATACPeaks.bed echo "Testing the paired-end model of HINT using ATAC-seq data.." rgt-hint footprinting --atac-seq --paired-end --output-prefix=fp_paired ATAC.bam ATACPeaks.bed +cd ${DIR} echo "Running HINT using only histone modification data.." url="http://134.130.18.8/open_data/hint/tutorial/HINT_HistoneTest.tar.gz" echo "Downloading test data." wget -qO- -O HINT_HistoneTest.tar.gz $url && tar xvfz HINT_HistoneTest.tar.gz && rm HINT_HistoneTest.tar.gz -cd ${DIR}/HINT_HistoneTest +cd HINT_HistoneTest rgt-hint footprinting --histone histone.bam histonePeaks.bed echo "********* HINT test completed ****************" diff --git a/test/motif.sh b/test/motif.sh index ca80bdb25..1121f9313 100755 --- a/test/motif.sh +++ b/test/motif.sh @@ -57,7 +57,7 @@ cd $DEST echo "Running matching.." rgt-motifanalysis matching --organism hg19 --target-genes input/genes.txt --input-files input/background.bed echo "Running enrichment.." -rgt-motifanalysis enrichment --organism hg19 input/background.bed match/target_regions.bed +rgt-motifanalysis enrichment --organism hg19 --logo-copy input/background.bed match/target_regions.bed echo @@ -82,7 +82,7 @@ cd $DEST echo "Running matching.." rgt-motifanalysis matching --organism hg19 --input-matrix input_matrix.txt --rand-proportion 10 echo "Running enrichment.." -rgt-motifanalysis enrichment --organism hg19 --input-matrix input_matrix.txt match/random_regions.bed +rgt-motifanalysis enrichment --organism hg19 --logo-embed --input-matrix input_matrix.txt match/random_regions.bed echo diff --git a/test/thor.sh b/test/thor.sh index 9b92289c4..35cc7ec06 100755 --- a/test/thor.sh +++ b/test/thor.sh @@ -25,5 +25,6 @@ fi cd THOR_example_data/ rm -rf report_* sample-* rgt-THOR -n sample --report THOR.config +rgt-THOR -n sample_no --report THOR.config --no-merge-bin echo "********* THOR test completed ****************" \ No newline at end of file diff --git a/tools/deprecated/coverageFromGenomicSets.py b/tools/deprecated/coverageFromGenomicSets.py deleted file mode 100644 index ca706886a..000000000 --- a/tools/deprecated/coverageFromGenomicSets.py +++ /dev/null @@ -1,55 +0,0 @@ -""" -A intersectGenomicSets perform intersection statistics for a set of bed files. - -Authors: Ivan G. Costa, Manuel Allhoff - -It recieves as input a experimental matrix with a list of bed files and outputs simple overlap statistics. - -""" - - -import sys -from rgt.ExperimentalMatrix import * -from rgt.GenomicRegionSet import * -from rgt.CoverageSet import * -import numpy - -def bedCoverage(bed,reads): - c=[] - for r in reads: - cov=CoverageSet(r,bed) - cov.coverage_from_genomicset(r) - #cov.normRPM() - c.append(cov.coverage) - return numpy.transpose(c) - - -def printTable(namesCol,namesLines,table,fileName): - f=open(fileName,"w") - f.write("\t"+("\t".join(namesCol))+"\n") - for i,line in enumerate(table): - f.write(namesLines[i]+"\t"+("\t".join([str(j) for j in line]))+"\n") - - -out="" -experimentalFile = sys.argv[1] -exps=ExperimentalMatrix() -exps.read(experimentalFile) -beds = exps.get_regionsets() -reads = exps.get_readsfiles() -readsnames = exps.get_readsnames() -outputDir = sys.argv[2] -if len(sys.argv) > 3: - experimentalFile2 = sys.argv[3] - exps2=ExperimentalMatrix() - exps2.read(experimentalFile2) - reads = exps2.get_readsfiles() - readsnames = exps2.get_readsnames() - out=outputDir - -for bed in beds: - bednames=[r.chrom+":"+str(r.initial)+"-"+str(r.final) for r in bed] - c=bedCoverage(bed,reads) - printTable(readsnames,bednames,c,outputDir+"/"+bed.name+".txt") - - diff --git a/tools/deprecated/geneAssociation.py b/tools/deprecated/geneAssociation.py deleted file mode 100644 index 5bb0cb897..000000000 --- a/tools/deprecated/geneAssociation.py +++ /dev/null @@ -1,83 +0,0 @@ -import sys -import os.path -from rgt.GenomicRegionSet import * -from rgt.ExperimentalMatrix import * -from fisher import pvalue - -back=False -designFile = sys.argv[1] -anotationPath = sys.argv[2] -genomeFile=anotationPath+"chrom.sizes" -geneFile=anotationPath+"association_file.bed" - -exps=ExperimentalMatrix() -exps.read(designFile) - -beds=[] -geneLists=[] - -#this should be improved -bedGenes = GenomicRegionSet(geneFile) -bedGenes.read(geneFile) -allgenes=[] -for r in bedGenes: - allgenes.append(r.name) -allgenes=list(set(allgenes)) - -genesets=exps.get_genesets() - -if len(sys.argv) > 3: - back=True - backGroundPeaks = sys.argv[3] - backBed=GenomicRegionSet("BACK") - backBed.read(backGroundPeaks) - - -backBed=GenomicRegionSet("BACK") -backBed.read(backGroundPeaks) -backUP=GenomicRegionSet("BACKUP") -[back_de_genes,back_de_peak_genes, back_mappedGenes, back_totalPeaks] = backUP.filter_by_gene_association(backGroundPeaks,genesets[0],geneFile,genomeFile) -prop_back=back_mappedGenes/float(len(allgenes)) - -for g in genesets: - for region in exps.get_regionsets(): - bed = GenomicRegionSet("") - [degenes,de_peak_genes, mappedGenes, totalPeaks] = bed.filter_by_gene_association(region.fileName,g,geneFile,genomeFile) - #print degenes - #print bed.genes - a=de_peak_genes - b=degenes-de_peak_genes - c=back_mappedGenes-de_peak_genes - d=len(allgenes)-b-c-a - prop_de=de_peak_genes/float(degenes) - p= pvalue(a,b,c,d) - print region.name,g.name,a,b,c,d,degenes,mappedGenes,len(allgenes),prop_de,prop_back,prop_de/prop_back,p.right_tail,p.left_tail - - -'''for g in genesets: - if back: - backBed=GenomicRegionSet("BACK") - backBed.read(backGroundPeaks) - backUP=GenomicRegionSet("BACKUP") - [back_de_genes,back_de_peak_genes, back_mappedGenes, back_totalPeaks] = backUP.filter_by_gene_association(backGroundPeaks,g,geneFile,genomeFile) - prop_back=len(backUP)/float(len(backBed)) - for region in exps.get_regionsets(): - bed = GenomicRegionSet("") - [degenes,de_peak_genes, mappedGenes, totalPeaks] = bed.filter_by_gene_association(region.fileName,g,geneFile,genomeFile) - if back: - a=len(bed) - b=len(region)-a - c=len(backUP)-a - d=len(backBed)-a-b-c - prop_de=len(bed)/float(len(region)) - else: - a=de_peak_genes - b=degenes-de_peak_genes - c=mappedGenes-de_peak_genes - d=len(allgenes)-b-c-a - prop_de=de_peak_genes/float(degenes) - prop_back=mappedGenes/float(len(allgenes)) - p= pvalue(a,b,c,d) - print region.name,g.name,a,b,c,d,degenes,mappedGenes,len(allgenes),prop_de,prop_back,prop_de/prop_back,p.right_tail,p.left_tail''' - - diff --git a/tools/deprecated/plotProfiles2.py b/tools/deprecated/plotProfiles2.py deleted file mode 100644 index 7deff4bc9..000000000 --- a/tools/deprecated/plotProfiles2.py +++ /dev/null @@ -1,78 +0,0 @@ -import sys -from os.path import basename -from rgt.CoverageSet import * - - - -# A_P4 B_P4 C_P4 A_P13 B_P13 C_P13 -#1.1982482 1.0613452 2.1236085 0.5630403 0.6752216 1.0295918 - -factors=[0.5630403,1.1982482,0.6752216,1.0613452,1.0295918,2.1236085] - - -bedFile= sys.argv[1] -bamFile= sys.argv[2] -bamFile2= sys.argv[3] -bamFile3= sys.argv[4] -bamFile4= sys.argv[5] -bamFile5=sys.argv[6] -bamFile6= sys.argv[7] - -bedname=basename(bedFile)[:-4] - - -bed= SetGenomicRegions(bedname) -bed.readBed(bedFile) -bed.extend(19500,19500) - -cov=CoverageSet(bed.name+"_"+basename(bamFile)[:-4],bed) -cov.coverageFromBam(bamFile,step=50,window=50) -#cov.normFactor(factors[0]) -cov.normFPKM() -cov.plot(log=False) - -cov2=CoverageSet(bed.name+"_"+basename(bamFile2)[:-4],bed) -cov2.coverageFromBam(bamFile2,step=50,window=50) -#cov2.normFactor(factors[1]) -cov2.normFPKM() -cov2.plot(log=False) - -cov.diff(cov2) -cov.plot(log=False,name=bedname+"_Diff_A_Late_Early") -#cov.abs() -#cov.plot(log=False,name=bedname+"_Abs_Diff_A_Late_Early") - -cov=CoverageSet(bed.name+"_"+basename(bamFile3)[:-4],bed) -cov.coverageFromBam(bamFile3,step=50,window=50) -cov.normFPKM() -##cov.normFactor(factors[2]) -cov.plot(log=False) -# -cov2=CoverageSet(bed.name+"_"+basename(bamFile4)[:-4],bed) -cov2.coverageFromBam(bamFile4,step=50,window=50) -#cov2.normFactor(factors[3]) -cov2.normFPKM() -cov2.plot(log=False) -# -cov.diff(cov2) -cov.plot(log=False,name=bedname+"_Diff_B_Late_Early") -##cov.abs() -##cov.plot(log=True,name=bedname+"_Abs_Diff_B_Late_Early") -# -# -cov=CoverageSet(bed.name+"_"+basename(bamFile5)[:-4],bed) -cov.coverageFromBam(bamFile5,step=50,window=50) -cov.normFPKM() -#cov.normFactor(factors[4]) -cov.plot(log=False) -# -cov2=CoverageSet(bed.name+"_"+basename(bamFile6)[:-4],bed) -cov2.coverageFromBam(bamFile6,step=50,window=50) -#cov2.normFactor(factors[5]) -cov2.normFPKM() -cov2.plot(log=False) -# -cov.diff(cov2) -cov.plot(log=False,name=bedname+"_Diff_C_Late_Early") -##cov.abs() -##cov.plot(log=True,name=bedname+"_Abs_Diff_C_Late_Early") diff --git a/tools/mapExpressionMotif.py b/tools/mapExpressionMotif.py index 0e1c48c5c..5332648d0 100644 --- a/tools/mapExpressionMotif.py +++ b/tools/mapExpressionMotif.py @@ -1,53 +1,38 @@ -from rgt.MotifSet import MotifSet -from rgt.GeneSet import GeneSet -import sys +from __future__ import print_function -jaspar='/home/ivan/projects/reg-gen/data/motifs/jaspar_vertebrates.mtf' -uniprobe='/home/ivan/projects/reg-gen/data/motifs/uniprobe_primary.mtf' -internal='/home/ivan/projects/reg-gen/data/motifs/internal.mtf' - -motif_set = MotifSet() -motif_set.read_file([jaspar,uniprobe,internal]) +import sys -motifs=[(l.strip("\n")).split("\t")[0] for l in open(sys.argv[1])] +from rgt.GeneSet import GeneSet +from rgt.MotifSet import MotifSet -geneset_file=sys.argv[2] +motifs = [(l.strip("\n")).split("\t")[0] for l in open(sys.argv[1])] +geneset_file = sys.argv[2] +search_mode = sys.argv[3] -search_mode=sys.argv[3] +# preload all available motifs from the repositories +motif_set = MotifSet(preload_motifs=True) -genes=GeneSet("DC Genes") +genes = GeneSet("DC Genes") genes.read_expression(geneset_file) - -filtered=motif_set.filter_by_motifs(motifs) - -[filtered_genes,g_m,m_g]=filtered.filter_by_genes(genes,search=search_mode) - -genes_found=[] -not_found=[] -print "\t\t"+("\t".join(genes.cond)) -for m in motifs: - try: - sel_genes=m_g[m] - for g in sel_genes: - print m+"\t"+g+"\t"+("\t".join([str(v) for v in genes.values[g]])) - genes_found.append(g) - except: - not_found.append(m) - -print not_found - -import sets - -print sets.Set(genes.genes).difference(genes_found) - +# take only a subset of the motifs (using their exact names) +motif_set, _, _ = motif_set.filter(motifs, key_type="name") -#print filtered_genes +# of these new motif set, take the subset of those matching these gene names +# (we only care about the motif2gene mapping) +_, m_g, _ = motif_set.filter(genes.genes, key_type="gene_names", search=search_mode) -#print filtered.genes_map - -#print filtered_genes.motifs_map - -#print g_m - -#print m_g +genes_found = [] +not_found = [] +print("\t\t" + ("\t".join(genes.cond))) +for m in motifs: + try: + sel_genes = m_g[m] + for g in sel_genes: + print(m + "\t" + g + "\t" + ("\t".join([str(v) for v in genes.values[g]]))) + genes_found.append(g) + except: + not_found.append(m) + +print(not_found) +print(set(genes.genes).difference(genes_found)) diff --git a/tools/mapGeneNetwork.py b/tools/mapGeneNetwork.py index 9b94f1856..6b3775d12 100644 --- a/tools/mapGeneNetwork.py +++ b/tools/mapGeneNetwork.py @@ -1,55 +1,41 @@ -from rgt.MotifSet import MotifSet -from rgt.GeneSet import GeneSet import sys -import glob -import os.path -# motif databases +from rgt.GeneSet import GeneSet +from rgt.MotifSet import MotifSet -jaspar='/home/ivan/projects/reg-gen/data/motifs/jaspar_vertebrates.mtf' -uniprobe='/home/ivan/projects/reg-gen/data/motifs/uniprobe_primary.mtf' -internal='/home/ivan/projects/reg-gen/data/motifs/internal.mtf' +# motif databases - # files with p-values -enrichment_files=sys.argv[1] +enrichment_files = sys.argv[1] # tfs to include in the network -factor_file=sys.argv[2] +factor_file = sys.argv[2] # search mode to map factors to motifs (exact or inexact) -search_mode=sys.argv[3] -# pvalue cuttoff for definition of active factors -pvalue=float(sys.argv[4]) +search_mode = sys.argv[3] +# pvalue cutoff for definition of active factors +pvalue = float(sys.argv[4]) # output file -out=sys.argv[5] +out = sys.argv[5] # genes to be used as potential targets -filter_targets=[] -targets=None +targets = None if len(sys.argv) > 6: - targets_file=sys.argv[6] - # reading targets - targets=GeneSet("genes") - targets.read(targets_file) - + targets_file = sys.argv[6] + # reading targets + targets = GeneSet("genes") + targets.read(targets_file) # starting motif databases -motif_set = MotifSet() if len(sys.argv) > 7: - motif_set.read_file([sys.argv[7]]) + motif_set = MotifSet(preload_motifs=False) + motif_set.read_mtf([sys.argv[7]]) else: - motif_set.read_file([jaspar,uniprobe,internal]) + motif_set = MotifSet(preload_motifs=True) # reading genes -factors=GeneSet("genes") +factors = GeneSet("genes") factors.read(factor_file) -# reading networks -#for f in glob.glob(enrichment_files): -# # use last dir name as name for condition -# condition=os.path.dirname(f) -# condition=condition.split("/")[-1] -motif_set.read_motif_targets_enrichment(enrichment_files,pvalue) -#motif_set.write_enrichment_table(pvalue,out+"/pvalue_table_"+str(pvalue*100)+".txt") - -##print motif_set.motifs_map -motif_set.write_cytoscape_network(factors,search_mode,out,targets,pvalue) +# we only want a subset of the motif set +motif_set = motif_set.filter(factors.genes, key_type="gene_names", search=search_mode) +motif_set.read_enrichment(enrichment_files, pvalue) +motif_set.write_network(targets, out, pvalue) diff --git a/tools/rgt-tools.py b/tools/rgt-tools.py index 876e42c23..95b3c5960 100755 --- a/tools/rgt-tools.py +++ b/tools/rgt-tools.py @@ -121,7 +121,7 @@ def get_sequence(sequence, ch, ss, es, reverse=False, complement=False, rna=Fals ############### BED merge ############################################ # python rgt-tools.py - parser_bedmerge = subparsers.add_parser('bed_merge', help="[BED] Merge regions by name") + parser_bedmerge = subparsers.add_parser('bed_merge', help="[BED] Merge regions") parser_bedmerge.add_argument('-i', metavar='input', type=str, help="Input BED file") parser_bedmerge.add_argument('-o', metavar='output', type=str, help="Output BED file") parser_bedmerge.add_argument('-s', action="store_true", help="Strand specific") @@ -229,6 +229,8 @@ def get_sequence(sequence, ch, ss, es, reverse=False, complement=False, rna=Fals help="Define minimum length of gene to filter out the small genes (default:0)") parser_bedupstream.add_argument('-r', '--reverse', action="store_true", default=False, help="Reverse the strand.") + parser_bedupstream.add_argument('-ds', '--downstream', action="store_true", default=False, + help="Find downstream regions instead of upstream.") ############### BED to FASTA ############################################# parser_bed2fasta = subparsers.add_parser('bed_to_fasta', @@ -315,6 +317,12 @@ def get_sequence(sequence, ch, ss, es, reverse=False, complement=False, rna=Fals help="[BED] Standardize the chromosomes.") parser_bedschrom.add_argument('-i', metavar='input', type=str, help="Input BED file") + ############### BED seperate strand ################################ + parser_bedstrand = subparsers.add_parser('bed_strand', + help="[BED] Seperate the BED files by strandness") + parser_bedstrand.add_argument('-i', metavar='input', type=str, help="Input BED file") + parser_bedstrand.add_argument('-o', metavar='output', type=str, help="Input directory") + ############### BED add overlapping region name ################################ parser_adddata = subparsers.add_parser('bed_add_data', help="[BED] Add overlapping region name") @@ -378,12 +386,13 @@ def get_sequence(sequence, ch, ss, es, reverse=False, complement=False, rna=Fals help="[THOR] Split and filter the differential peaks from rgt-THOR") parser_thorsf.add_argument('-i', metavar='input', type=str, help="Input BED file") parser_thorsf.add_argument('-o', metavar='output', default=None, type=str, help="Output directory.") - parser_thorsf.add_argument('-p', metavar='p-value', type=int, help="Define the cut-off of p-value (-log10) for filtering.") - parser_thorsf.add_argument('-fc', metavar='fold-change', type=int,default=0, + parser_thorsf.add_argument('-p', metavar='p-value', type=float, help="Define the cut-off of p-value (-log10) for filtering.") + parser_thorsf.add_argument('-fc', metavar='fold-change', type=float,default=0, help="Define the cut-off of foldchange for filtering.") parser_thorsf.add_argument('-rn', '--rename', action="store_true", help="Rename the peak names by associated genes.") parser_thorsf.add_argument('-g', metavar='genome', type=str, help="Define the genome") + parser_thorsf.add_argument('-m', metavar='merge', type=int, default=0, help="Define the maximum distance for merging the nearby regions") parser_thorsf.add_argument('-b', metavar='bin', type=int, help="Define the bin size") parser_thorsf.add_argument('-s', metavar='step', type=int, help="Define the step size") @@ -449,12 +458,19 @@ def get_sequence(sequence, ch, ss, es, reverse=False, complement=False, rna=Fals parser_sliceFASTA.add_argument('-p', metavar='position', type=int, help="The start position") parser_sliceFASTA.add_argument('-r', '--reverse', default=False, action="store_true", help="Reverse the sequence") - ############### TXP to BED ############################################# + ############### FASTA convert ############################################# + parser_FASTAconvert = subparsers.add_parser('fasta_convert', + help="[FASTA] Convert the sequence by critiria") + parser_FASTAconvert.add_argument('-i', metavar='input', type=str, help="Input FASTA file") + parser_FASTAconvert.add_argument('-o', metavar='output', type=str, help="Output FASTA file") + parser_FASTAconvert.add_argument('-c', '--complement', default=False, action="store_true", help="Get the complement of the sequence") + parser_FASTAconvert.add_argument('-r', '--reverse', default=False, action="store_true", help="Reverse the sequence") + ############### TPX to BED ############################################# # python rgt-tools.py txp2bed -i -o - parser_txp2bed = subparsers.add_parser('txp2bed', + parser_tpx2bed = subparsers.add_parser('tpx2bed', help="[BED] Convert TXP file into BED format") - parser_txp2bed.add_argument('-i', metavar='input', type=str, help="Input TXP file") - parser_txp2bed.add_argument('-o', metavar='output', type=str, help="Output BED file") + parser_tpx2bed.add_argument('-i', metavar='input', type=str, help="Input TPX file") + parser_tpx2bed.add_argument('-o', metavar='output', type=str, help="Output BED file") ############### ENCODE Download ############################################# # python rgt-tools.py encode -i -o @@ -810,19 +826,32 @@ def get_sequence(sequence, ch, ss, es, reverse=False, complement=False, rna=Fals target = GenomicRegionSet("target") # if args.min == 0: cut = float("inf") # elif args.min > 0: cut = args.min - - for s in gene: - if s.orientation == "+" and len(s) > args.min: - s.initial, s.final = max(s.initial-args.d-args.l, 0), max(s.initial-args.d, 0) - if s.initial > s.final: s.initial, s.final = s.final, s.initial - if args.reverse: s.orientation = "-" - target.add(s) - - elif s.orientation == "-" and len(s) > args.min: - s.initial, s.final = s.final+args.d, s.final+args.d+args.l - if s.initial > s.final: s.initial, s.final = s.final, s.initial - if args.reverse: s.orientation = "+" - target.add(s) + if not args.downstream: + for s in gene: + if s.orientation == "+" and len(s) > args.min: + s.initial, s.final = max(s.initial-args.d-args.l, 0), max(s.initial-args.d, 0) + if s.initial > s.final: s.initial, s.final = s.final, s.initial + if args.reverse: s.orientation = "-" + target.add(s) + + elif s.orientation == "-" and len(s) > args.min: + s.initial, s.final = s.final + args.d, s.final + args.d + args.l + if s.initial > s.final: s.initial, s.final = s.final, s.initial + if args.reverse: s.orientation = "+" + target.add(s) + else: + for s in gene: + if s.orientation == "+" and len(s) > args.min: + s.initial, s.final = s.final + args.d, s.final + args.d + args.l + if s.initial > s.final: s.initial, s.final = s.final, s.initial + if args.reverse: s.orientation = "-" + target.add(s) + + elif s.orientation == "-" and len(s) > args.min: + s.initial, s.final = max(s.initial-args.d-args.l, 0), max(s.initial-args.d, 0) + if s.initial > s.final: s.initial, s.final = s.final, s.initial + if args.reverse: s.orientation = "+" + target.add(s) print(len(target)) target.write(args.o) @@ -852,7 +881,7 @@ def get_sequence(sequence, ch, ss, es, reverse=False, complement=False, rna=Fals else: l = line.strip().split() ranking.append([l[0],int(l[1]),int(l[2])]) - + # print(len(regions)) for i, r in enumerate(regions): if args.order: for j, reg in enumerate(ranking): @@ -881,7 +910,7 @@ def get_sequence(sequence, ch, ss, es, reverse=False, complement=False, rna=Fals ss=exon.initial, es=exon.final, strand=exon.orientation, reverse=True, complement=True) - ss = [s[i:i+70] for i in range(0, len(s), 70)] + ss = [s[k:k+70] for k in range(0, len(s), 70)] writelines += ss with open(os.path.join(args.o, name + ".fa"), "w") as f: @@ -891,9 +920,10 @@ def get_sequence(sequence, ch, ss, es, reverse=False, complement=False, rna=Fals s = get_sequence(sequence=args.genome, ch=r.chrom, ss=r.initial, es=r.final, strand=r.orientation) - ss = [s[i:i+70] for i in range(0, len(s), 70)] + ss = [s[k:k+70] for k in range(0, len(s), 70)] if ".fa" not in args.o: + # print(i) with open(os.path.join(args.o, name + ".fa"), "w") as f: if not r.orientation: r.orientation = "." if not args.score: @@ -1291,6 +1321,23 @@ def count_polyA_on_bam(bed, bam): nbed.write(args.i) + ############### BED seperate strand ########################### + # + elif args.mode == "bed_strand": + print(tag + ": [BED] Seperate by strandness") + bed = GenomicRegionSet(args.i) + bed.read(args.i) + fwd = GenomicRegionSet("FWD") + rev = GenomicRegionSet("REV") + for r in bed: + if r.orientation == "+": + fwd.add(r) + elif r.orientation == "-": + rev.add(r) + name = os.path.basename(args.i).rpartition(".")[0] + fwd.write(os.path.join(args.o, name+"_FWD.bed")) + rev.write(os.path.join(args.o, name+"_REV.bed")) + ############### BED add overlapping region name ########################### # elif args.mode == "bed_add_data": @@ -1503,87 +1550,161 @@ def count_polyA_on_bam(bed, bam): ############### THOR split ############################################# elif args.mode == "thor_split": - print(tag + ": [THOR] Split the differential peaks") - if not args.o: - args.o = os.path.dirname(args.i) - - name = os.path.basename(args.i).split(".")[0] - if args.fc == 0: tag = "_p" + str(args.p) - else: tag = "_p"+str(args.p)+"_fc"+str(args.fc) - bed = GenomicRegionSet("input") - bed.read(args.i) - print("Number of input peaks:\t"+str(len(bed))) - - if args.rename and args.g: - bed2 = bed.gene_association(organism=args.g, strand_specific=False) - else: - bed2 = bed - - for region in bed2: - data = region.data.split() - # print(data) - # sys.exit(1) - if ";" in data[4]: - stat = data[4].split(";") + def merging_peaks(regions, distance): + bed4 = GenomicRegionSet(name="bed4") + if distance > 0: + current_chrom = "" + current_start = 0 + current_end = 0 + current_strand = "" + list_data = [] + for i, region in enumerate(regions): + d = abs(current_end - region.initial) + if current_chrom == region.chrom and current_strand == region.orientation and d < distance: + list_data.append(region.data) + current_end = region.final + else: + if len(list_data) > 1: + count1 = 0 + count2 = 0 + list_p = [] + for d in list_data: + l = d.split() + if ";" in l[4]: + stat = l[4].split(";") + else: + stat = l[5].split(";") + list_p.append(float(stat[2])) + c1 = [1 + float(x) for x in stat[0].split(":")] + c1 = sum(c1) / len(c1) + count1 = count1 + c1 + c2 = [1 + float(x) for x in stat[1].split(":")] + c2 = sum(c2) / len(c2) + count2 = count2 + c2 + # print(stat[1].split(":")) + # print(list_data) + # count1 = count1 / len(list_data) + # count2 = count2 / len(list_data) + # print([count1, count2]) + fc = count2 / count1 + p = max(list_p) + + new_d = "\t".join([str(fc), str(current_start), str(current_end), + "0,0,0", "0", ";".join([str(int(count1)), str(int(count2)), str(p)])]) + new_region = GenomicRegion(chrom=current_chrom, + orientation=current_strand, + initial=current_start, + final=current_end, + data=new_d) + bed4.add(new_region) + else: + bed4.add(region) + # sys.exit() + + current_chrom = region.chrom + current_start = region.initial + current_end = region.final + current_strand = region.orientation + list_data = [region.data] else: - stat = data[5].split(";") - s1 = [float(x) + 1 for x in stat[0].split(":")] - s2 = [float(x) + 1 for x in stat[1].split(":")] - fc = math.log((sum(s2) / len(s2)) / (sum(s1) / len(s1)), 2) - region.data = "\t".join([str(fc)] + data[1:]) + bed4 = regions - gain_peaks = GenomicRegionSet("gain_peaks") - lose_peaks = GenomicRegionSet("lost_peaks") - gain_table = GenomicRegionSet("gain_table") - lose_table = GenomicRegionSet("lost_table") - - for region in bed2: - l = region.data.split() - # print(l) - # sys.exit(1) - if ";" in l[4]: - s = l[4].split(";") - else: - s = l[5].split(";") - if abs(float(l[0])) > args.fc and float(s[2]) > args.p: + return bed4 + + def output_table(regions, args): + tabel_peaks = GenomicRegionSet("table") + for region in regions: + l = region.data.split() + if ";" in l[4]: + s = l[4].split(";") + else: + s = l[5].split(";") + # if abs(float(l[0])) > args.fc and float(s[2]) > args.p: s1 = sum([int(x) for x in s[0].split(":")]) / len(s[0].split(":")) s2 = sum([int(x) for x in s[1].split(":")]) / len(s[1].split(":")) # print([len(region), args.s]) if args.s: - nbins = int(len(region)/args.s) + nbins = int(len(region) / args.s) + if nbins == 0: + nbins = 1 ns1 = float(s1) / nbins ns2 = float(s2) / nbins data = "\t".join([l[0], str(s1), str(s2), str(len(region)), str(ns1), str(ns2), str(abs(ns1 + ns2)), str(abs(ns1 - ns2)), s[2]]) else: + # print(region) data = "\t".join([l[0], str(s1), str(s2), str(len(region)), s[2]]) # Chromosome Start End Name FC Strand Ave. Count 1 Ave. Count 2 # Length Norm count 1 Norm count 2 Sum norm count Diff norm count P-value - if float(l[0]) > 0: - gain_table.add(GenomicRegion(chrom=region.chrom, initial=region.initial, final=region.final, - orientation=region.orientation, data=data, name=region.name)) + tabel_peaks.add(GenomicRegion(chrom=region.chrom, initial=region.initial, final=region.final, + orientation=region.orientation, data=data, name=region.name)) + + return(tabel_peaks) + + # Naming + print(tag + ": [THOR] Split the differential peaks") + if not args.o: + args.o = os.path.dirname(args.i) + + name = os.path.basename(args.i).split(".")[0] + if args.fc == 0: tag = "_p" + str(args.p) + else: tag = "_p"+str(args.p)+"_fc"+str(args.fc) + + bed = GenomicRegionSet("input") + bed.read(args.i) + print("Number of input peaks:\t"+str(len(bed))) + + # Filtering by FC and p value, then split the peaks + gain_peaks = GenomicRegionSet("gain_peaks") + lose_peaks = GenomicRegionSet("lost_peaks") + for region in bed: + data = region.data.split() + if ";" in data[4]: stat = data[4].split(";") + else: stat = data[5].split(";") + s1 = [float(x) + 1 for x in stat[0].split(":")] + s2 = [float(x) + 1 for x in stat[1].split(":")] + fc = math.log((sum(s2) / len(s2)) / (sum(s1) / len(s1)), 2) + region.data = "\t".join([str(fc)] + data[1:]) + if abs(fc) > args.fc and float(stat[2]) > args.p: + if fc > 0: gain_peaks.add(region) - elif float(l[0]) < 0: - lose_table.add(GenomicRegion(chrom=region.chrom, initial=region.initial, final=region.final, - orientation=region.orientation, data=data, name=region.name)) + elif fc < 0: lose_peaks.add(region) + gain_peaks.sort() + lose_peaks.sort() + print("Number of gain peaks:\t" + str(len(gain_peaks))) + print("Number of lost peaks:\t" + str(len(lose_peaks))) + # Merging the close peaks + m_gain_peaks = merging_peaks(gain_peaks, args.m) + m_lost_peaks = merging_peaks(lose_peaks, args.m) + # m_gain_peaks = gain_peaks + # m_lost_peaks = lose_peaks + # Gene Association + if args.rename and args.g: + m_gain_peaks = m_gain_peaks.gene_association(organism=args.g, strand_specific=False) + m_lost_peaks = m_lost_peaks.gene_association(organism=args.g, strand_specific=False) + + print("Number of gain peaks:\t" + str(len(m_gain_peaks))) + print("Number of lost peaks:\t" + str(len(m_lost_peaks))) + # + gain_table = output_table(m_gain_peaks, args) + lose_table = output_table(m_lost_peaks, args) # sort table gain_table.sort(key=lambda x: float(x.data.split("\t")[-2]), reverse=True) gain_table.sort(key=lambda x: float(x.data.split("\t")[-1]), reverse=True) lose_table.sort(key=lambda x: float(x.data.split("\t")[-2]), reverse=True) lose_table.sort(key=lambda x: float(x.data.split("\t")[-1]), reverse=True) - gain_peaks.write(os.path.join(args.o, name + tag + "_gain.bed")) - lose_peaks.write(os.path.join(args.o, name + tag + "_lost.bed")) + m_gain_peaks.write(os.path.join(args.o, name + tag + "_gain.bed")) + m_lost_peaks.write(os.path.join(args.o, name + tag + "_lost.bed")) gain_table.write(os.path.join(args.o, name + tag + "_gain.table")) lose_table.write(os.path.join(args.o, name + tag + "_lost.table")) - - print("Number of gain peaks:\t" + str(len(gain_peaks))) - print("Number of lost peaks:\t" + str(len(lose_peaks))) + # + # ############### getseq ############################################# @@ -1808,15 +1929,28 @@ def fasta2bp(filename): else: print("5' - "+ seq.sequences[0].seq[start:end]+ " - 3'") + ############### FASTA_convert ####################################### + elif args.mode == "fasta_convert": + from rgt.SequenceSet import SequenceSet + seq = SequenceSet(name=args.i, seq_type="RNA") + seq.read_fasta(fasta_file=args.i) - ############### FASTA slicing ####################################### - elif args.mode == "txp2bed": + for s in seq: + if args.reverse: + s.seq.sequences = s.seq[::-1] + if args.complement: + s.seq = s.complement() + + seq.write_fasta(filename=args.o) + + ############### TPX to BED ####################################### + elif args.mode == "tpx2bed": from rgt.tdf.RNADNABindingSet import RNADNABindingSet txp = RNADNABindingSet("txp") - txp.read_txp(filename=args.i, dna_fine_posi=True) + txp.read_tpx(filename=args.i, dna_fine_posi=True) tmp = os.path.join(os.path.dirname(args.o), "temp.bed") - txp.write(filename=tmp) + txp.write_bed(filename=tmp) os.system("sort -k1,1V -k2,2n " + tmp + " > " + args.o) os.remove(tmp) @@ -1825,8 +1959,8 @@ def fasta2bp(filename): ############### ENCODE download ####################################### elif args.mode == "encode": args.i = os.path.join(os.getcwd(), args.i) - cmd = "xargs -n 1 curl -O -L < " + args.i - os.system(cmd) + # cmd = "xargs -n 1 curl -O -L < " + args.i + # os.system(cmd) name_dict = {} with open(os.path.join(args.o, "metadata.tsv")) as f: for line in f: @@ -1851,8 +1985,9 @@ def fasta2bp(filename): id = os.path.basename(file).split(".")[0] if id in name_dict.keys(): # print(file) + formatf = file.split(".")[1] if file.endswith("gz"): - formatf = file.split(".")[1] + with gzip.open(os.path.join(args.o, file), 'rb') as infile: with open(os.path.join(args.o, name_dict[id]+"."+formatf), 'wb') as outfile: for line in infile: diff --git a/unittest/test_AnnotationSet.py b/unittest/test_AnnotationSet.py index 4c36f51df..65e0a08c5 100644 --- a/unittest/test_AnnotationSet.py +++ b/unittest/test_AnnotationSet.py @@ -13,13 +13,13 @@ genome = "hg38" print("Checking " + genome) -annot = AnnotationSet(genome,filter_havana=False,protein_coding=True,known_only=False) +annot = AnnotationSet(genome, filter_havana=False, protein_coding=True, known_only=False) # annot = AnnotationSet(genome,filter_havana=True,protein_coding=True,known_only=True) print("\tloading AnnotationSet... succeeds") promoters = annot.get_promoters() -print("\tPromoters "+str(len(promoters))) +print("\tPromoters " + str(len(promoters))) gd = GenomeData(organism=genome) -print("\t"+gd.get_annotation()) +print("\t" + gd.get_annotation()) print("\tloading GenomeData... succeeds") # genome = "mm9" @@ -51,4 +51,3 @@ # gd = GenomeData(organism=genome) # print("\t"+gd.get_annotation()) # print("\tloading GenomeData... succeeds") - diff --git a/unittest/test_CoverageSet.py b/unittest/test_CoverageSet.py index c056d3d4b..6ffcc5c29 100644 --- a/unittest/test_CoverageSet.py +++ b/unittest/test_CoverageSet.py @@ -1,6 +1,7 @@ import unittest -from rgt.GenomicRegionSet import * + from rgt.CoverageSet import CoverageSet +from rgt.GenomicRegionSet import * regions = GenomicRegionSet("test") regions.add(GenomicRegion("chr1", 10000, 11000, "+")) @@ -11,8 +12,9 @@ bamfile = "/projects/lncRNA/local/cardio/total_rna/bam/d4_1.bam" bedfile = "~/rgtdata/hg38/genes_hg38.bed" -class CoverageSet_Test(unittest.TestCase): - def coverage_from_genomicset(self): + +class CoverageSetTest(unittest.TestCase): + def test_coverage_from_genomicset(self): cov.coverage_from_genomicset(bamfile) print(cov.coverage) - self.assertEqual(cov.coverage, 4) \ No newline at end of file + self.assertEqual(cov.coverage, 4) diff --git a/unittest/test_GenomicRegion.py b/unittest/test_GenomicRegion.py index 1c48b499b..b0b3ad035 100644 --- a/unittest/test_GenomicRegion.py +++ b/unittest/test_GenomicRegion.py @@ -1,114 +1,116 @@ from __future__ import print_function + import unittest + from rgt.GenomicRegion import GenomicRegion class TestGenomicRegion(unittest.TestCase): def test_overlap(self): r = GenomicRegion(chrom=1, initial=10, final=15) - - #usual cases + + # usual cases r2 = GenomicRegion(chrom=1, initial=20, final=25) self.assertFalse(r.overlap(r2)) - + r2 = GenomicRegion(chrom=1, initial=0, final=5) self.assertFalse(r.overlap(r2)) - + r2 = GenomicRegion(chrom=1, initial=7, final=12) self.assertTrue(r.overlap(r2)) - + r2 = GenomicRegion(chrom=1, initial=12, final=18) self.assertTrue(r.overlap(r2)) - + r2 = GenomicRegion(chrom=1, initial=12, final=14) self.assertTrue(r.overlap(r2)) - - #r2 within r + + # r2 within r r2 = GenomicRegion(chrom=1, initial=11, final=13) self.assertTrue(r.overlap(r2)) - - #border cases - #GenomicRegions touch, but do not overlap + + # border cases + # GenomicRegions touch, but do not overlap r2 = GenomicRegion(chrom=1, initial=5, final=10) self.assertFalse(r.overlap(r2)) - - #here, they overlap + + # here, they overlap r2 = GenomicRegion(chrom=1, initial=5, final=11) self.assertTrue(r.overlap(r2)) - - #they touch, do not overlap + + # they touch, do not overlap r2 = GenomicRegion(chrom=1, initial=15, final=20) self.assertFalse(r.overlap(r2)) - - #they overlap in 1 bp (14th) + + # they overlap in 1 bp (14th) r2 = GenomicRegion(chrom=1, initial=14, final=20) self.assertTrue(r.overlap(r2)) - - #they have zero length + + # they have zero length r = GenomicRegion(chrom=1, initial=10, final=10) r2 = GenomicRegion(chrom=1, initial=10, final=10) self.assertFalse(r.overlap(r2)) - - #they have zero length + + # they have zero length r = GenomicRegion(chrom=1, initial=10, final=10) r2 = GenomicRegion(chrom=1, initial=11, final=11) self.assertFalse(r.overlap(r2)) - - #they have zero length + + # they have zero length r = GenomicRegion(chrom=1, initial=10, final=10) r2 = GenomicRegion(chrom=1, initial=5, final=10) self.assertFalse(r.overlap(r2)) - + def test_extend(self): - #normal extend + # normal extend r = GenomicRegion(chrom=1, initial=10, final=20) - r.extend(5,15) + r.extend(5, 15) self.assertEqual(r.initial, 5) self.assertEqual(r.final, 35) - - #use negative values to extend + + # use negative values to extend r2 = GenomicRegion(chrom=1, initial=10, final=20) - r2.extend(-5,-1) + r2.extend(-5, -1) self.assertEqual(r2.initial, 15) self.assertEqual(r2.final, 19) - - #extend to under zero + + # extend to under zero r3 = GenomicRegion(chrom=1, initial=10, final=20) - r3.extend(15,0) + r3.extend(15, 0) self.assertEqual(r3.initial, 0) - - #extend so that inital and final coordinate change + + # extend so that inital and final coordinate change r4 = GenomicRegion(chrom=1, initial=10, final=20) - r4.extend(-50,-50) + r4.extend(-50, -50) self.assertEqual(r4.initial, 0) self.assertEqual(r4.final, 60) - + def test_len(self): r = GenomicRegion(chrom=1, initial=10, final=20) self.assertEqual(len(r), 10) - + def test_cmp(self): r = GenomicRegion(chrom=1, initial=10, final=20) - + r2 = GenomicRegion(chrom=1, initial=12, final=22) self.assertTrue(r < r2) - + r2 = GenomicRegion(chrom=1, initial=8, final=18) self.assertTrue(r > r2) - + r2 = GenomicRegion(chrom=1, initial=10, final=12) self.assertTrue(r > r2) - + r2 = GenomicRegion(chrom=1, initial=12, final=14) self.assertTrue(r < r2) - + r2 = GenomicRegion(chrom='X', initial=4, final=8) self.assertTrue(r < r2) - + r2 = GenomicRegion(chrom=1, initial=10, final=18) self.assertTrue(r >= r2) - -if __name__ == '__main__': + +if __name__ == '__main__': suite = unittest.TestLoader().loadTestsFromTestCase(TestGenomicRegion) unittest.TextTestRunner(verbosity=2).run(suite) diff --git a/unittest/test_GenomicRegionSet.py b/unittest/test_GenomicRegionSet.py index fc557205c..ee1d130e7 100644 --- a/unittest/test_GenomicRegionSet.py +++ b/unittest/test_GenomicRegionSet.py @@ -1,28 +1,26 @@ -from __future__ import print_function from __future__ import division -import sys +from __future__ import print_function + import unittest -from rgt.GenomicRegion import * + from rgt.GenomicRegionSet import * -import os -from rgt.Util import GenomeData from rgt.Util import OverlapType - """Unit Test""" + class TestGenomicRegionSet(unittest.TestCase): - - def region_sets(self,listA,listB): + + def region_sets(self, listA, listB): """ Setting two GenomicRegionSets as self.setA and self.setB for each case test. """ self.setA = GenomicRegionSet('for Unit Test') for i in range(len(listA)): self.setA.add(GenomicRegion(chrom=listA[i][0], initial=listA[i][1], final=listA[i][2])) - + self.setB = GenomicRegionSet('for Unit Test') for i in range(len(listB)): self.setB.add(GenomicRegion(chrom=listB[i][0], initial=listB[i][1], final=listB[i][2])) - + def test_extend(self): """ Two empty sets @@ -31,17 +29,17 @@ def test_extend(self): """ self.region_sets([], []) - self.setA.extend(100,100) + self.setA.extend(100, 100) self.assertEqual(len(self.setA.sequences), 0) """ One region A : ----- R : --------- """ - self.region_sets([['chr1',5,10]], + self.region_sets([['chr1', 5, 10]], []) result = self.setA - result.extend(4,4) + result.extend(4, 4) self.assertEqual(len(result), 1) self.assertEqual(result[0].initial, 1) self.assertEqual(result[0].final, 14) @@ -50,10 +48,10 @@ def test_extend(self): A : ----- ------ ----- ----- R : --------=--------- ------------------ """ - self.region_sets([['chr1',5,10],['chr1',15,20],['chr1',40,50],['chr1',65,75]], + self.region_sets([['chr1', 5, 10], ['chr1', 15, 20], ['chr1', 40, 50], ['chr1', 65, 75]], []) result = self.setA - result.extend(5,5) + result.extend(5, 5) self.assertEqual(len(result), 4) self.assertEqual(result[0].initial, 0) self.assertEqual(result[0].final, 15) @@ -68,10 +66,10 @@ def test_extend(self): A : ----- ------ ----- ----- R : none """ - self.region_sets([['chr1',5,10],['chr2',15,20],['chr3',40,50],['chr4',65,75]], + self.region_sets([['chr1', 5, 10], ['chr2', 15, 20], ['chr3', 40, 50], ['chr4', 65, 75]], []) result = self.setA - result.extend(5,5) + result.extend(5, 5) self.assertEqual(len(result), 4) self.assertEqual(result[0].initial, 0) self.assertEqual(result[0].final, 15) @@ -90,19 +88,19 @@ def test_extend(self): A : ----- R : --------- """ - self.region_sets([['chr1',100,200]], + self.region_sets([['chr1', 100, 200]], []) result = self.setA - result.extend(10,10,percentage=True) + result.extend(10, 10, percentage=True) self.assertEqual(len(result), 1) self.assertEqual(result[0].initial, 90) self.assertEqual(result[0].final, 210) - + def test_sort(self): - self.region_sets([['chr1',15,20],['chr1',40,50],['chr1',65,75],['chr1',5,10]], + self.region_sets([['chr1', 15, 20], ['chr1', 40, 50], ['chr1', 65, 75], ['chr1', 5, 10]], []) self.setA.sort() - + def test_intersect(self): """ Two empty sets @@ -114,10 +112,10 @@ def test_intersect(self): []) result = self.setA.intersect(self.setB) self.assertEqual(len(result), 0) - + result = self.setA.intersect(self.setB, mode=OverlapType.ORIGINAL) self.assertEqual(len(result), 0) - + result = self.setA.intersect(self.setB, mode=OverlapType.COMP_INCL) self.assertEqual(len(result), 0) """ @@ -126,14 +124,14 @@ def test_intersect(self): B : none R : none """ - self.region_sets([['chr1',5,10]], + self.region_sets([['chr1', 5, 10]], []) result = self.setA.intersect(self.setB) self.assertEqual(len(result), 0) - + result = self.setA.intersect(self.setB, mode=OverlapType.ORIGINAL) self.assertEqual(len(result), 0) - + result = self.setA.intersect(self.setB, mode=OverlapType.COMP_INCL) self.assertEqual(len(result), 0) """ @@ -142,13 +140,13 @@ def test_intersect(self): R : none """ self.region_sets([], - [['chr1',5,10]]) + [['chr1', 5, 10]]) result = self.setA.intersect(self.setB) self.assertEqual(len(result), 0) - + result = self.setA.intersect(self.setB, mode=OverlapType.ORIGINAL) self.assertEqual(len(result), 0) - + result = self.setA.intersect(self.setB, mode=OverlapType.COMP_INCL) self.assertEqual(len(result), 0) """ @@ -157,14 +155,14 @@ def test_intersect(self): B : ---- ------ ------ R : none """ - self.region_sets([['chr1',1,5],['chr1',11,20],['chr1',33,38]], - [['chr1',7,9],['chr1',20,25],['chr1',26,31]]) + self.region_sets([['chr1', 1, 5], ['chr1', 11, 20], ['chr1', 33, 38]], + [['chr1', 7, 9], ['chr1', 20, 25], ['chr1', 26, 31]]) result = self.setA.intersect(self.setB) self.assertEqual(len(result), 0) - + result = self.setA.intersect(self.setB, mode=OverlapType.ORIGINAL) self.assertEqual(len(result), 0) - + result = self.setA.intersect(self.setB, mode=OverlapType.COMP_INCL) self.assertEqual(len(result), 0) """ @@ -173,11 +171,11 @@ def test_intersect(self): B : ------ R : none """ - self.region_sets([['chr1',1,5],['chr1',11,20]], - [['chr1',5,11]]) + self.region_sets([['chr1', 1, 5], ['chr1', 11, 20]], + [['chr1', 5, 11]]) result = self.setA.intersect(self.setB) self.assertEqual(len(result), 0) - + result = self.setA.intersect(self.setB, mode=OverlapType.ORIGINAL) self.assertEqual(len(result), 0) @@ -189,36 +187,37 @@ def test_intersect(self): B : . . R : none """ - self.region_sets([['chr1',2,2],['chr1',20,20]], - [['chr1',5,5],['chr1',20,20]]) + self.region_sets([['chr1', 2, 2], ['chr1', 20, 20]], + [['chr1', 5, 5], ['chr1', 20, 20]]) result = self.setA.intersect(self.setB) self.assertEqual(len(result), 0) - + result = self.setA.intersect(self.setB, mode=OverlapType.ORIGINAL) self.assertEqual(len(result), 0) result = self.setA.intersect(self.setB, mode=OverlapType.COMP_INCL) self.assertEqual(len(result), 0) - + """ Perfect overlapping A : ------ B : ------ R : ------ """ - self.region_sets([['chr1',1,10],['chr1',500,550],['chr1',600,650],['chr1',700,750],['chr1',725,800]], - [['chr1',1,10],['chr1',500,550],['chr1',600,650],['chr1',700,750],['chr1',725,800]]) + self.region_sets( + [['chr1', 1, 10], ['chr1', 500, 550], ['chr1', 600, 650], ['chr1', 700, 750], ['chr1', 725, 800]], + [['chr1', 1, 10], ['chr1', 500, 550], ['chr1', 600, 650], ['chr1', 700, 750], ['chr1', 725, 800]]) result = self.setA.intersect(self.setB, mode=OverlapType.OVERLAP, rm_duplicates=True) - + self.assertEqual(len(result), 4) self.assertEqual(result[0].initial, 1) self.assertEqual(result[0].final, 10) - + result = self.setA.intersect(self.setB, mode=OverlapType.ORIGINAL) self.assertEqual(len(result), 5) self.assertEqual(result[0].initial, 1) self.assertEqual(result[0].final, 10) - + result = self.setA.intersect(self.setB, mode=OverlapType.COMP_INCL) self.assertEqual(len(result), 5) self.assertEqual(result[0].initial, 1) @@ -232,13 +231,13 @@ def test_intersect(self): R3: (comp_incl) """ - self.region_sets([['chr1',1,10]], - [['chr1',7,20]]) + self.region_sets([['chr1', 1, 10]], + [['chr1', 7, 20]]) result = self.setA.intersect(self.setB) self.assertEqual(len(result), 1) self.assertEqual(result[0].initial, 7) self.assertEqual(result[0].final, 10) - + result = self.setA.intersect(self.setB, mode=OverlapType.ORIGINAL) self.assertEqual(len(result), 1) self.assertEqual(result[0].initial, 1) @@ -254,8 +253,8 @@ def test_intersect(self): R2: ------- -------- (original) R3: (comp_incl) """ - self.region_sets([['chr1',1,10],['chr1',26,35]], - [['chr1',7,30]]) + self.region_sets([['chr1', 1, 10], ['chr1', 26, 35]], + [['chr1', 7, 30]]) result = self.setA.intersect(self.setB) self.assertEqual(len(result), 2) self.assertEqual(result[0].initial, 7) @@ -280,15 +279,15 @@ def test_intersect(self): R2: ------- -------- (original) R3: (comp_incl) """ - self.region_sets([['chr1',1,10],['chr1',26,35]], - [['chr1',7,15],['chr1',30,40]]) + self.region_sets([['chr1', 1, 10], ['chr1', 26, 35]], + [['chr1', 7, 15], ['chr1', 30, 40]]) result = self.setA.intersect(self.setB) self.assertEqual(len(result), 2) self.assertEqual(result[0].initial, 7) self.assertEqual(result[0].final, 10) self.assertEqual(result[1].initial, 30) self.assertEqual(result[1].final, 35) - + result = self.setA.intersect(self.setB, mode=OverlapType.ORIGINAL) self.assertEqual(len(result), 2) self.assertEqual(result[0].initial, 1) @@ -307,8 +306,8 @@ def test_intersect(self): R3: (comp_incl) """ - self.region_sets([['chr1',3,30],['chr1',50,60],['chr1',70,85]], - [['chr1',1,5],['chr1',10,19],['chr1',27,35],['chr1',55,75]]) + self.region_sets([['chr1', 3, 30], ['chr1', 50, 60], ['chr1', 70, 85]], + [['chr1', 1, 5], ['chr1', 10, 19], ['chr1', 27, 35], ['chr1', 55, 75]]) result = self.setA.intersect(self.setB, mode=OverlapType.OVERLAP) self.assertEqual(len(result), 5) @@ -331,7 +330,7 @@ def test_intersect(self): self.assertEqual(result[1].final, 60) self.assertEqual(result[2].initial, 70) self.assertEqual(result[2].final, 85) - + result = self.setA.intersect(self.setB, mode=OverlapType.COMP_INCL) self.assertEqual(len(result), 0) """ @@ -340,11 +339,11 @@ def test_intersect(self): B : chr2 ------- R : none """ - self.region_sets([['chr1',1,10]], - [['chr2',1,10]]) + self.region_sets([['chr1', 1, 10]], + [['chr2', 1, 10]]) result = self.setA.intersect(self.setB) self.assertEqual(len(result), 0) - + result = self.setA.intersect(self.setB, mode=OverlapType.ORIGINAL) self.assertEqual(len(result), 0) @@ -358,8 +357,8 @@ def test_intersect(self): R2: --------------------------- (original) R3: (comp_incl) """ - self.region_sets([['chr1',1,50]], - [['chr1',1,5],['chr1',10,19],['chr1',45,60]]) + self.region_sets([['chr1', 1, 50]], + [['chr1', 1, 5], ['chr1', 10, 19], ['chr1', 45, 60]]) result = self.setA.intersect(self.setB) self.assertEqual(len(result), 3) self.assertEqual(result[0].initial, 1) @@ -368,12 +367,12 @@ def test_intersect(self): self.assertEqual(result[1].final, 19) self.assertEqual(result[2].initial, 45) self.assertEqual(result[2].final, 50) - + result = self.setA.intersect(self.setB, mode=OverlapType.ORIGINAL) self.assertEqual(len(result), 1) self.assertEqual(result[0].initial, 1) self.assertEqual(result[0].final, 50) - + result = self.setA.intersect(self.setB, mode=OverlapType.COMP_INCL) self.assertEqual(len(result), 0) """ @@ -384,8 +383,8 @@ def test_intersect(self): R3: ---- ------ (comp_incl) """ - self.region_sets([['chr1',1,5],['chr1',10,19],['chr1',45,60]], - [['chr1',1,50]]) + self.region_sets([['chr1', 1, 5], ['chr1', 10, 19], ['chr1', 45, 60]], + [['chr1', 1, 50]]) result = self.setA.intersect(self.setB) self.assertEqual(len(result), 3) self.assertEqual(result[0].initial, 1) @@ -394,7 +393,7 @@ def test_intersect(self): self.assertEqual(result[1].final, 19) self.assertEqual(result[2].initial, 45) self.assertEqual(result[2].final, 50) - + result = self.setA.intersect(self.setB, mode=OverlapType.ORIGINAL) self.assertEqual(len(result), 3) self.assertEqual(result[0].initial, 1) @@ -403,8 +402,7 @@ def test_intersect(self): self.assertEqual(result[1].final, 19) self.assertEqual(result[2].initial, 45) self.assertEqual(result[2].final, 60) - - + result = self.setA.intersect(self.setB, mode=OverlapType.COMP_INCL) self.assertEqual(len(result), 2) self.assertEqual(result[0].initial, 1) @@ -422,15 +420,15 @@ def test_intersect(self): ------ R3: ------- (comp_incl) """ - self.region_sets([['chr1',1,50],['chr1',20,40],['chr1',70,80]], - [['chr1',25,45],['chr1',65,95]]) + self.region_sets([['chr1', 1, 50], ['chr1', 20, 40], ['chr1', 70, 80]], + [['chr1', 25, 45], ['chr1', 65, 95]]) result = self.setA.intersect(self.setB) self.assertEqual(len(result), 2) self.assertEqual(result[0].initial, 25) self.assertEqual(result[0].final, 45) self.assertEqual(result[1].initial, 70) self.assertEqual(result[1].final, 80) - + result = self.setA.intersect(self.setB, mode=OverlapType.ORIGINAL) self.assertEqual(len(result), 3) self.assertEqual(result[1].initial, 20) @@ -439,7 +437,7 @@ def test_intersect(self): self.assertEqual(result[0].final, 50) self.assertEqual(result[2].initial, 70) self.assertEqual(result[2].final, 80) - + result = self.setA.intersect(self.setB, mode=OverlapType.COMP_INCL) self.assertEqual(len(result), 1) self.assertEqual(result[0].initial, 70) @@ -613,13 +611,13 @@ def test_closest(self): # self.assertEqual(len(result), 1) # self.assertEqual(result[0].initial, 15) # self.assertEqual(result[0].final, 20) - + def test_remove_duplicates(self): """ A : ===== ----- R : ----- ----- """ - self.region_sets([['chr1',1,10],['chr1',1,10],['chr1',15,25]], + self.region_sets([['chr1', 1, 10], ['chr1', 1, 10], ['chr1', 15, 25]], []) self.setA.remove_duplicates() result = self.setA @@ -632,7 +630,7 @@ def test_remove_duplicates(self): A : =====--- ----- R : =====--- ----- """ - self.region_sets([['chr1',1,10],['chr1',1,15],['chr1',20,25]], + self.region_sets([['chr1', 1, 10], ['chr1', 1, 15], ['chr1', 20, 25]], []) self.setA.remove_duplicates() result = self.setA @@ -647,8 +645,9 @@ def test_remove_duplicates(self): A : ===== ----- ------ ==== R : ----- ----- ------ ---- """ - self.region_sets([['chr1',1,10],['chr1',1,10],['chr1',15,25],['chr1',30,35],['chr1',40,45],['chr1',40,45]], - []) + self.region_sets( + [['chr1', 1, 10], ['chr1', 1, 10], ['chr1', 15, 25], ['chr1', 30, 35], ['chr1', 40, 45], ['chr1', 40, 45]], + []) self.setA.remove_duplicates() result = self.setA self.assertEqual(len(result), 4) @@ -667,10 +666,10 @@ def test_window(self): B : ------[ 99 ] [ 199 ]--- window = 100 R : - only one base overlaps with extending A - """ - self.region_sets([['chr1',200,300]], - [['chr1',1,101],['chr1',499,550]]) - result = self.setA.window(self.setB,adding_length=100) + """ + self.region_sets([['chr1', 200, 300]], + [['chr1', 1, 101], ['chr1', 499, 550]]) + result = self.setA.window(self.setB, adding_length=100) self.assertEqual(len(result), 1) self.assertEqual(result[0].initial, 100) self.assertEqual(result[0].final, 101) @@ -680,10 +679,10 @@ def test_window(self): window = 200 R : ------ - left-hand side is covered, and the right-hand side is only one base overlapped - """ - self.region_sets([['chr1',200,300]], - [['chr1',1,101],['chr1',499,550]]) - result = self.setA.window(self.setB,adding_length=200) + """ + self.region_sets([['chr1', 200, 300]], + [['chr1', 1, 101], ['chr1', 499, 550]]) + result = self.setA.window(self.setB, adding_length=200) self.assertEqual(len(result), 2) self.assertEqual(result[0].initial, 1) # GenomicRegion.extend will choose 1 rather than 0 self.assertEqual(result[0].final, 101) @@ -694,9 +693,9 @@ def test_window(self): B : -------- ---- window = 1000 (default) R : ---- ---- - """ - self.region_sets([['chr1',3000,3500],['chr1',4000,4500]], - [['chr1',1500,2500],['chr1',5000,5500]]) + """ + self.region_sets([['chr1', 3000, 3500], ['chr1', 4000, 4500]], + [['chr1', 1500, 2500], ['chr1', 5000, 5500]]) result = self.setA.window(self.setB) self.assertEqual(len(result), 2) self.assertEqual(result[0].initial, 2000) @@ -711,18 +710,18 @@ def test_window(self): ---- ---- window = 100 R : none - """ - self.region_sets([['chr1',3000,3500],['chr1',4000,4500]], - [['chr1',1500,2500],['chr1',5000,5500]]) - result = self.setA.window(self.setB,adding_length=2000) + """ + self.region_sets([['chr1', 3000, 3500], ['chr1', 4000, 4500]], + [['chr1', 1500, 2500], ['chr1', 5000, 5500]]) + result = self.setA.window(self.setB, adding_length=2000) self.assertEqual(len(result), 2) self.assertEqual(result[0].initial, 1500) self.assertEqual(result[0].final, 2500) self.assertEqual(result[1].initial, 5000) self.assertEqual(result[1].final, 5500) - result = self.setA.window(self.setB,adding_length=100) + result = self.setA.window(self.setB, adding_length=100) self.assertEqual(len(result), 0) - + def test_subtract(self): """ A : none @@ -730,7 +729,7 @@ def test_subtract(self): R : none """ self.region_sets([], - [['chr1',6,15]]) + [['chr1', 6, 15]]) result = self.setA.subtract(self.setB) self.assertEqual(len(result), 0) """ @@ -738,7 +737,7 @@ def test_subtract(self): B : none R : ------ """ - self.region_sets([['chr1',6,15]], + self.region_sets([['chr1', 6, 15]], []) result = self.setA.subtract(self.setB) self.assertEqual(len(result), 1) @@ -749,8 +748,8 @@ def test_subtract(self): B : ------ R : --- """ - self.region_sets([['chr1',1,10]], - [['chr1',6,15]]) + self.region_sets([['chr1', 1, 10]], + [['chr1', 6, 15]]) result = self.setA.subtract(self.setB) self.assertEqual(len(result), 1) self.assertEqual(result[0].initial, 1) @@ -760,8 +759,8 @@ def test_subtract(self): B : ------ R : --- """ - self.region_sets([['chr1',6,15]], - [['chr1',1,10]]) + self.region_sets([['chr1', 6, 15]], + [['chr1', 1, 10]]) result = self.setA.subtract(self.setB) self.assertEqual(len(result), 1) self.assertEqual(result[0].initial, 10) @@ -771,8 +770,8 @@ def test_subtract(self): B : --------- R : none """ - self.region_sets([['chr1',6,10]], - [['chr1',1,15]]) + self.region_sets([['chr1', 6, 10]], + [['chr1', 1, 15]]) result = self.setA.subtract(self.setB) self.assertEqual(len(result), 0) """ @@ -780,8 +779,8 @@ def test_subtract(self): B : --- R : --- --- """ - self.region_sets([['chr1',1,15]], - [['chr1',6,10]]) + self.region_sets([['chr1', 1, 15]], + [['chr1', 6, 10]]) result = self.setA.subtract(self.setB) self.assertEqual(len(result), 2) self.assertEqual(result[0].initial, 1) @@ -793,8 +792,8 @@ def test_subtract(self): B : ------ R : none """ - self.region_sets([['chr1',6,15]], - [['chr1',6,15]]) + self.region_sets([['chr1', 6, 15]], + [['chr1', 6, 15]]) result = self.setA.subtract(self.setB) self.assertEqual(len(result), 0) """ @@ -802,8 +801,8 @@ def test_subtract(self): B : ---------- ---- R : ------- ------ """ - self.region_sets([['chr1',5,30],['chr1',70,85]], - [['chr1',20,50],['chr1',100,110]]) + self.region_sets([['chr1', 5, 30], ['chr1', 70, 85]], + [['chr1', 20, 50], ['chr1', 100, 110]]) result = self.setA.subtract(self.setB) self.assertEqual(len(result), 2) self.assertEqual(result[0].initial, 5) @@ -815,8 +814,8 @@ def test_subtract(self): B : ------ R : ---- ----- """ - self.region_sets([['chr1',20,30],['chr1',35,55]], - [['chr1',10,23],['chr1',100,110]]) + self.region_sets([['chr1', 20, 30], ['chr1', 35, 55]], + [['chr1', 10, 23], ['chr1', 100, 110]]) result = self.setA.subtract(self.setB) self.assertEqual(len(result), 2) self.assertEqual(result[0].initial, 23) @@ -831,8 +830,8 @@ def test_subtract(self): R : ch1 -------- ------- ch2 ------------------- """ - self.region_sets([['chr1',0,30000],['chr2',0,35000]], - [['chr1',20000,23000],['chr2',31000,35000]]) + self.region_sets([['chr1', 0, 30000], ['chr2', 0, 35000]], + [['chr1', 20000, 23000], ['chr2', 31000, 35000]]) result = self.setA.subtract(self.setB) self.assertEqual(len(result), 3) self.assertEqual(result[0].initial, 0) @@ -846,11 +845,11 @@ def test_subtract(self): B : --- --------- ---- ---- R : - ---- --------- ----------- -------------- """ - self.region_sets([['chr1',5,1000]], - [['chr1',10,15],['chr1',30,70],['chr1',120,140],['chr1',200,240]]) + self.region_sets([['chr1', 5, 1000]], + [['chr1', 10, 15], ['chr1', 30, 70], ['chr1', 120, 140], ['chr1', 200, 240]]) result = self.setA.subtract(self.setB) self.assertEqual(len(result), 5) - + """ A : ----------------------- ------ ----- ----- ----------- @@ -858,10 +857,10 @@ def test_subtract(self): R : - ---- ------ ---- --- --- ---- --- """ - self.region_sets([['chr1',5,100],['chr1',20,40],['chr1',60,80],['chr1',95,150],['chr1',180,220]], - [['chr1',10,15],['chr1',30,70],['chr1',120,140],['chr1',200,240]]) + self.region_sets([['chr1', 5, 100], ['chr1', 20, 40], ['chr1', 60, 80], ['chr1', 95, 150], ['chr1', 180, 220]], + [['chr1', 10, 15], ['chr1', 30, 70], ['chr1', 120, 140], ['chr1', 200, 240]]) result = self.setA.subtract(self.setB) - #print(result.sequences) + # print(result.sequences) self.assertEqual(len(result), 8) self.assertEqual(result[0].initial, 5) """ @@ -869,14 +868,13 @@ def test_subtract(self): B : --- --------- ---- ---- R : - ---- --------- ----------- -------------- """ - self.region_sets([['chr1',5,1000],['chr2',5,1000],['chr4',5,1000]], - [['chr1',10,15],['chr1',30,70],['chr1',120,140],['chr1',200,240], - ['chr2',10,15],['chr2',30,70],['chr2',120,140],['chr2',200,240], - ['chr4',10,15],['chr4',30,70],['chr4',120,140],['chr4',200,240]]) + self.region_sets([['chr1', 5, 1000], ['chr2', 5, 1000], ['chr4', 5, 1000]], + [['chr1', 10, 15], ['chr1', 30, 70], ['chr1', 120, 140], ['chr1', 200, 240], + ['chr2', 10, 15], ['chr2', 30, 70], ['chr2', 120, 140], ['chr2', 200, 240], + ['chr4', 10, 15], ['chr4', 30, 70], ['chr4', 120, 140], ['chr4', 200, 240]]) result = self.setA.subtract(self.setB) self.assertEqual(len(result), 15) - - + def test_merge(self): """ A : none @@ -891,7 +889,7 @@ def test_merge(self): A : ----- ----- R : ----- ----- """ - self.region_sets([['chr1',1,10],['chr1',15,25]], + self.region_sets([['chr1', 1, 10], ['chr1', 15, 25]], []) self.setA.merge() result = self.setA @@ -905,7 +903,7 @@ def test_merge(self): A2: ----- R : ------------ ---- """ - self.region_sets([['chr1',1,30],['chr1',11,20],['chr1',40,50]], + self.region_sets([['chr1', 1, 30], ['chr1', 11, 20], ['chr1', 40, 50]], []) self.setA.merge() result = self.setA @@ -919,7 +917,7 @@ def test_merge(self): A2: --------- R : ------------ ---- """ - self.region_sets([['chr1',1,30],['chr1',20,40],['chr1',50,60]], + self.region_sets([['chr1', 1, 30], ['chr1', 20, 40], ['chr1', 50, 60]], []) self.setA.merge() result = self.setA @@ -932,14 +930,14 @@ def test_merge(self): A : ======= R : ------- """ - self.region_sets([['chr1',1,30],['chr1',1,30]], + self.region_sets([['chr1', 1, 30], ['chr1', 1, 30]], []) self.setA.merge() result = self.setA self.assertEqual(len(result), 1) self.assertEqual(result[0].initial, 1) self.assertEqual(result[0].final, 30) - + def test_cluster(self): """ Empty sets @@ -954,7 +952,7 @@ def test_cluster(self): A : ------- R : ------- """ - self.region_sets([['chr1',1,10]], + self.region_sets([['chr1', 1, 10]], []) result = self.setA.cluster(10) self.assertEqual(len(result), 1) @@ -965,7 +963,7 @@ def test_cluster(self): ------ R : ----------- """ - self.region_sets([['chr1',1,10],['chr1',10,20]], + self.region_sets([['chr1', 1, 10], ['chr1', 10, 20]], []) result = self.setA.cluster(10) self.assertEqual(len(result), 1) @@ -976,7 +974,7 @@ def test_cluster(self): R1: ----- ----- R2: ------------ """ - self.region_sets([['chr1',1,10],['chr1',15,25]], + self.region_sets([['chr1', 1, 10], ['chr1', 15, 25]], []) result = self.setA.cluster(1) self.assertEqual(len(result), 2) @@ -1002,8 +1000,8 @@ def test_cluster(self): R4: ------------------------------ R5: ------------------------------ """ - self.region_sets([['chr1',1,10],['chr1',15,25],['chr1',35,45], - ['chr1',60,70],['chr1',90,100]], + self.region_sets([['chr1', 1, 10], ['chr1', 15, 25], ['chr1', 35, 45], + ['chr1', 60, 70], ['chr1', 90, 100]], []) result = self.setA.cluster(6) self.assertEqual(len(result), 4) @@ -1015,13 +1013,13 @@ def test_cluster(self): self.assertEqual(len(result), 1) result = self.setA.cluster(26) self.assertEqual(len(result), 1) - + def test_flank(self): """ A : ----- R1: --- --- """ - self.region_sets([['chr1',60,75]], + self.region_sets([['chr1', 60, 75]], []) result = self.setA.flank(10) self.assertEqual(len(result), 2) @@ -1033,7 +1031,7 @@ def test_flank(self): A : ----- ---- R1: ----- ===== ---- """ - self.region_sets([['chr1',60,75],['chr1',90,100]], + self.region_sets([['chr1', 60, 75], ['chr1', 90, 100]], []) result = self.setA.flank(15) self.assertEqual(len(result), 4) @@ -1045,7 +1043,7 @@ def test_flank(self): self.assertEqual(result[2].final, 90) self.assertEqual(result[3].initial, 100) self.assertEqual(result[3].final, 115) - + def test_jaccard(self): """ self --8-- ---10--- -4- @@ -1054,11 +1052,11 @@ def test_jaccard(self): similarity: ( 5 + 4 )/[(8 + 10 + 4) + (10 +10) - (5 + 4 )] = 9/33 """ - self.region_sets([['chr1',50,58],['chr1',70,80],['chr1',90,94]], - [['chr1',45,55],['chr1',76,86]]) + self.region_sets([['chr1', 50, 58], ['chr1', 70, 80], ['chr1', 90, 94]], + [['chr1', 45, 55], ['chr1', 76, 86]]) result = self.setA.jaccard(self.setB) - self.assertEqual(result, 9/33) - + self.assertEqual(result, 9 / 33) + def test_get_genome_data(self): """hg19""" result = GenomicRegionSet("hg19") @@ -1066,90 +1064,90 @@ def test_get_genome_data(self): self.assertEqual(len(result), 23) """hg19, with Mitochondria chromosome""" result = GenomicRegionSet("hg19") - result.get_genome_data(organism="hg19",chrom_M=True) + result.get_genome_data(organism="hg19", chrom_M=True) self.assertEqual(len(result), 24) - + def test_random_regions(self): - - self.region_sets([['chr1',0,10000],['chr2',0,20000],['chrX',0,30000]], + + self.region_sets([['chr1', 0, 10000], ['chr2', 0, 20000], ['chrX', 0, 30000]], []) - result = self.setA.random_regions(organism="mm9", - total_size=100, - overlap_result=False, + result = self.setA.random_regions(organism="mm9", + total_size=100, + overlap_result=False, overlap_input=False) result.sort() - #print("-"*80) - #print("The result random regions are: ") - #for s in result.sequences: + # print("-"*80) + # print("The result random regions are: ") + # for s in result.sequences: # print("\t%s\t%10d\t%10d%10d" % (s.chrom,s.initial,s.final,s.__len__())) - #print("Overlaps within result: ",result.within_overlap()) - - - self.region_sets([['chr1',0,10000],['chr2',0,20000],['chrX',0,30000]], + # print("Overlaps within result: ",result.within_overlap()) + + self.region_sets([['chr1', 0, 10000], ['chr2', 0, 20000], ['chrX', 0, 30000]], []) - result = self.setA.random_regions(organism="mm9", - total_size=100, - overlap_result=True, + result = self.setA.random_regions(organism="mm9", + total_size=100, + overlap_result=True, overlap_input=False) result.sort() - #print("-"*80) - #print("The result random regions are: ") - #for s in result.sequences: + # print("-"*80) + # print("The result random regions are: ") + # for s in result.sequences: # print("\t%s\t%10d\t%10d%10d" % (s.chrom,s.initial,s.final,s.__len__())) - #print("Overlaps within result: ",result.within_overlap()) - - self.region_sets([['chr1',0,10000],['chr2',0,20000],['chrX',0,30000]], + # print("Overlaps within result: ",result.within_overlap()) + + self.region_sets([['chr1', 0, 10000], ['chr2', 0, 20000], ['chrX', 0, 30000]], []) - result = self.setA.random_regions(organism="mm9", - total_size=100, - overlap_result=False, + result = self.setA.random_regions(organism="mm9", + total_size=100, + overlap_result=False, overlap_input=True) result.sort() - #print("-"*80) - #print("The result random regions are: ") - #for s in result.sequences: + # print("-"*80) + # print("The result random regions are: ") + # for s in result.sequences: # print("\t%s\t%10d\t%10d%10d" % (s.chrom,s.initial,s.final,s.__len__())) - #print("Overlaps within result: ",result.within_overlap()) - - self.region_sets([['chr1',0,10000],['chr2',0,20000],['chrX',0,30000]], + # print("Overlaps within result: ",result.within_overlap()) + + self.region_sets([['chr1', 0, 10000], ['chr2', 0, 20000], ['chrX', 0, 30000]], []) - result = self.setA.random_regions(organism="mm9", - total_size=100, - overlap_result=True, + result = self.setA.random_regions(organism="mm9", + total_size=100, + overlap_result=True, overlap_input=True) result.sort() - #print("-"*80) - #print("The result random regions are: ") - #for s in result.sequences: + # print("-"*80) + # print("The result random regions are: ") + # for s in result.sequences: # print("\t%s\t%10d\t%10d%10d" % (s.chrom,s.initial,s.final,s.__len__())) - #print("Overlaps within result: ",result.within_overlap()) - - self.region_sets([['chr1',0,1000],['chr2',0,2000],['chrX',0,3000]], + # print("Overlaps within result: ",result.within_overlap()) + + self.region_sets([['chr1', 0, 1000], ['chr2', 0, 2000], ['chrX', 0, 3000]], []) - result = self.setA.random_regions(organism="mm9", - multiply_factor=100, - overlap_result=False, + result = self.setA.random_regions(organism="mm9", + multiply_factor=100, + overlap_result=False, overlap_input=False) result.sort() - #print("-"*80) - #print("The result random regions are: ") - #for s in result.sequences: + # print("-"*80) + # print("The result random regions are: ") + # for s in result.sequences: # print("\t%s\t%10d\t%10d%10d" % (s.chrom,s.initial,s.final,s.__len__())) - #print("Overlaps within result: ",result.within_overlap()) - - self.region_sets([['chr1',0,1000],['chr2',0,2000],['chrX',0,3000]], + # print("Overlaps within result: ",result.within_overlap()) + + self.region_sets([['chr1', 0, 1000], ['chr2', 0, 2000], ['chrX', 0, 3000]], []) - result = self.setA.random_regions(organism="mm9", - multiply_factor=100, - overlap_result=False, + result = self.setA.random_regions(organism="mm9", + multiply_factor=100, + overlap_result=False, overlap_input=False, chrom_M=True) result.sort() - #print("-"*80) - #print("The result random regions are: ") - #for s in result.sequences: + # print("-"*80) + # print("The result random regions are: ") + # for s in result.sequences: # print("\t%s\t%10d\t%10d%10d" % (s.chrom,s.initial,s.final,s.__len__())) - #print("Overlaps within result: ",result.within_overlap()) + # print("Overlaps within result: ",result.within_overlap()) + """ @@ -1160,9 +1158,8 @@ def test_projection_test(self): result = self.setA.projection_test(self.setB) #print(result) #self.assertEqual(result, 11/31) -""" - -if __name__ == "__main__": +""" +if __name__ == "__main__": suite = unittest.TestLoader().loadTestsFromTestCase(TestGenomicRegionSet) unittest.TextTestRunner(verbosity=3).run(suite) diff --git a/unittest/test_MotifSet.py b/unittest/test_MotifSet.py new file mode 100644 index 000000000..c2c7faa64 --- /dev/null +++ b/unittest/test_MotifSet.py @@ -0,0 +1,240 @@ + +# Python 3 compatibility +from __future__ import print_function + +# Python +import unittest + +# Internal +from rgt.MotifSet import MotifSet + + +# NB: test based on hocomoco +# TODO: must enforce it by only loading hocomoco mtf +class MotifSetTest(unittest.TestCase): + def test_create_default(self): + ms = MotifSet() + self.assertEqual(len(ms.motifs_map), 0, msg="motif dictionary must be empty") + + def test_create_empty(self): + ms = MotifSet(preload_motifs=False) + self.assertEqual(len(ms.motifs_map), 0, msg="motif dictionary must be empty") + + def test_create_non_empty(self): + ms = MotifSet(preload_motifs=True) + self.assertGreater(len(ms.motifs_map), 0, msg="motif dictionary must be non empty") + + def test_filter_keys_not_list(self): + ms = MotifSet() + + with self.assertRaises(ValueError): + ms.filter("test") + + def test_filter_wrong_key_type(self): + ms = MotifSet() + + with self.assertRaises(ValueError): + ms.filter([], key_type="test") + + def test_filter_names(self): + ms = MotifSet(preload_motifs=True) + + ms2 = ms.filter(["ALX1_HUMAN.H11MO.0.B"], key_type="name", search="exact") + self.assertEqual(len(ms2.motifs_map), 1) + + ms2 = ms.filter(["ALX1"], key_type="name", search="exact") + self.assertEqual(len(ms2.motifs_map), 0) + + ms2 = ms.filter(["ALX1_HUMAN.H11MO.0.B"], key_type="name", search="inexact") + self.assertEqual(len(ms2.motifs_map), 1) + + ms2 = ms.filter(["ALX1"], key_type="name", search="inexact") + self.assertEqual(len(ms2.motifs_map), 1) + + ms2 = ms.filter(["ALX"], key_type="name", search="inexact") + self.assertEqual(len(ms2.motifs_map), 3) + + ms2 = ms.filter(["ALX1_HUMAN.H11MO.0.B"], key_type="name", search="regex") + self.assertEqual(len(ms2.motifs_map), 1) + + ms2 = ms.filter(["ALX1.*"], key_type="name", search="regex") + self.assertEqual(len(ms2.motifs_map), 1) + + ms2 = ms.filter(["ALX[134]_.*"], key_type="name", search="regex") + self.assertEqual(len(ms2.motifs_map), 3) + + def test_filter_genes(self): + ms = MotifSet(preload_motifs=True) + + ms2 = ms.filter(["ALX1_HUMAN.H11MO.0.B"], key_type="gene_names", search="exact") + self.assertEqual(len(ms2.motifs_map), 0) + m2k, k2m = ms2.get_mappings(key_type="gene_names") + self.assertEqual(len(m2k), 0) + self.assertEqual(len(k2m), 0) + + ms2 = ms.filter(["ALX1"], key_type="gene_names", search="exact") + self.assertEqual(len(ms2.motifs_map), 1) + m2k, k2m = ms2.get_mappings(key_type="gene_names") + self.assertEqual(len(m2k), 1) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter(["ALX"], key_type="gene_names", search="exact") + self.assertEqual(len(ms2.motifs_map), 0) + m2k, k2m = ms2.get_mappings(key_type="gene_names") + self.assertEqual(len(m2k), 0) + self.assertEqual(len(k2m), 0) + + ms2 = ms.filter(["ALX1"], key_type="gene_names", search="inexact") + self.assertEqual(len(ms2.motifs_map), 1) + m2k, k2m = ms2.get_mappings(key_type="gene_names") + self.assertEqual(len(m2k), 1) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter(["ALX"], key_type="gene_names", search="inexact") + self.assertEqual(len(ms2.motifs_map), 3) + m2k, k2m = ms2.get_mappings(key_type="gene_names") + self.assertEqual(len(m2k), 3) + self.assertEqual(len(k2m), 3) + + ms2 = ms.filter(["ALX1.*"], key_type="gene_names", search="regex") + self.assertEqual(len(ms2.motifs_map), 1) + m2k, k2m = ms2.get_mappings(key_type="gene_names") + self.assertEqual(len(m2k), 1) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter(["ALX[134]"], key_type="gene_names", search="regex") + self.assertEqual(len(ms2.motifs_map), 3) + m2k, k2m = ms2.get_mappings(key_type="gene_names") + self.assertEqual(len(m2k), 3) + self.assertEqual(len(k2m), 3) + + def test_filter_family(self): + ms = MotifSet(preload_motifs=True) + + ms2 = ms.filter(["Paired-related HD factors"], key_type="family", search="exact") + self.assertEqual(len(ms2.motifs_map), 35) + m2k, k2m = ms2.get_mappings(key_type="family") + self.assertEqual(len(m2k), 35) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter(["factors"], key_type="family", search="exact") + self.assertEqual(len(ms2.motifs_map), 0) + m2k, k2m = ms2.get_mappings(key_type="family") + self.assertEqual(len(m2k), 0) + self.assertEqual(len(k2m), 0) + + ms2 = ms.filter(["Paired-related HD factors"], key_type="family", search="inexact") + self.assertEqual(len(ms2.motifs_map), 35) + m2k, k2m = ms2.get_mappings(key_type="family") + self.assertEqual(len(m2k), 35) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter(["Paired-related HD"], key_type="family", search="inexact") + self.assertEqual(len(ms2.motifs_map), 35) + m2k, k2m = ms2.get_mappings(key_type="family") + self.assertEqual(len(m2k), 35) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter(["factors"], key_type="family", search="inexact") + self.assertEqual(len(ms2.motifs_map), 676) + m2k, k2m = ms2.get_mappings(key_type="family") + self.assertEqual(len(m2k), 676) + self.assertEqual(len(k2m), 59) + + ms2 = ms.filter(["Paired.*factors"], key_type="family", search="regex") + self.assertEqual(len(ms2.motifs_map), 35) + m2k, k2m = ms2.get_mappings(key_type="family") + self.assertEqual(len(m2k), 35) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter(["Paired-related.*"], key_type="family", search="regex") + self.assertEqual(len(ms2.motifs_map), 35) + m2k, k2m = ms2.get_mappings(key_type="family") + self.assertEqual(len(m2k), 35) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter([".*factors"], key_type="family", search="regex") + self.assertEqual(len(ms2.motifs_map), 676) + m2k, k2m = ms2.get_mappings(key_type="family") + self.assertEqual(len(m2k), 676) + self.assertEqual(len(k2m), 59) + + def test_filter_uniprot(self): + ms = MotifSet(preload_motifs=True) + + ms2 = ms.filter(["Q9H3D4"], key_type="uniprot_ids", search="exact") + self.assertEqual(len(ms2.motifs_map), 2) + m2k, k2m = ms2.get_mappings(key_type="uniprot_ids") + self.assertEqual(len(m2k), 2) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter(["Q9H"], key_type="uniprot_ids", search="exact") + self.assertEqual(len(ms2.motifs_map), 0) + m2k, k2m = ms2.get_mappings(key_type="uniprot_ids") + self.assertEqual(len(m2k), 0) + self.assertEqual(len(k2m), 0) + + ms2 = ms.filter(["Q9H3D4"], key_type="uniprot_ids", search="inexact") + self.assertEqual(len(ms2.motifs_map), 2) + m2k, k2m = ms2.get_mappings(key_type="uniprot_ids") + self.assertEqual(len(m2k), 2) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter(["Q9H"], key_type="uniprot_ids", search="inexact") + self.assertEqual(len(ms2.motifs_map), 20) + m2k, k2m = ms2.get_mappings(key_type="uniprot_ids") + self.assertEqual(len(m2k), 20) + self.assertEqual(len(k2m), 16) + + ms2 = ms.filter(["Q9H3D4"], key_type="uniprot_ids", search="regex") + self.assertEqual(len(ms2.motifs_map), 2) + m2k, k2m = ms2.get_mappings(key_type="uniprot_ids") + self.assertEqual(len(m2k), 2) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter(["Q9H.*"], key_type="uniprot_ids", search="regex") + self.assertEqual(len(ms2.motifs_map), 20) + m2k, k2m = ms2.get_mappings(key_type="uniprot_ids") + self.assertEqual(len(m2k), 20) + self.assertEqual(len(k2m), 16) + + def test_filter_data_source(self): + ms = MotifSet(preload_motifs=True) + + # implicitly, we are also testing the case insensitiveness of the string matching of all three types + + ms2 = ms.filter(["chip-seq"], key_type="data_source", search="exact") + self.assertEqual(len(ms2.motifs_map), 433) + m2k, k2m = ms2.get_mappings(key_type="data_source") + self.assertEqual(len(m2k), 433) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter(["chip"], key_type="data_source", search="exact") + self.assertEqual(len(ms2.motifs_map), 0) + m2k, k2m = ms2.get_mappings(key_type="data_source") + self.assertEqual(len(m2k), 0) + self.assertEqual(len(k2m), 0) + + ms2 = ms.filter(["chip-seq"], key_type="data_source", search="inexact") + self.assertEqual(len(ms2.motifs_map), 433) + m2k, k2m = ms2.get_mappings(key_type="data_source") + self.assertEqual(len(m2k), 433) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter(["chip"], key_type="data_source", search="inexact") + self.assertEqual(len(ms2.motifs_map), 433) + m2k, k2m = ms2.get_mappings(key_type="data_source") + self.assertEqual(len(m2k), 433) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter(["chip-seq"], key_type="data_source", search="regex") + self.assertEqual(len(ms2.motifs_map), 433) + m2k, k2m = ms2.get_mappings(key_type="data_source") + self.assertEqual(len(m2k), 433) + self.assertEqual(len(k2m), 1) + + ms2 = ms.filter(["(chip|selex)"], key_type="data_source", search="regex") + self.assertEqual(len(ms2.motifs_map), 591) + m2k, k2m = ms2.get_mappings(key_type="data_source") + self.assertEqual(len(m2k), 591) + self.assertEqual(len(k2m), 2)