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CancerVar.py
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##################################################################################
# Author: Li Quan ([email protected])
# Created Time: 2017-01-13 20:38:23 Friday
# File Name: CancerVar.py File type: python
# Last Change:.
# Description: python script for Interpretation of Pathogenetic of Cancer Variants
##################################################################################
#!/usr/bin/env python
import string,copy,logging,os,io,re,time,sys,platform,optparse,gzip,csv,glob
prog="CancerVar"
version = """%prog 1.1.1
release 20200119
Copyright (C) 2020 Wang Genomic Lab
CancerVar is free for non-commercial use without warranty.
Please contact the authors for commercial use.
Written by Quan LI,[email protected].
============================================================================
........................................................................
..%%%%....%%%%...%%..%%...%%%%...%%%%%%..%%%%%...%%..%%...%%%%...%%%%%..
.%%..%%..%%..%%..%%%.%%..%%..%%..%%......%%..%%..%%..%%..%%..%%..%%..%%.
.%%......%%%%%%..%%.%%%..%%......%%%%....%%%%%...%%..%%..%%%%%%..%%%%%..
.%%..%%..%%..%%..%%..%%..%%..%%..%%......%%..%%...%%%%...%%..%%..%%..%%.
..%%%%...%%..%%..%%..%%...%%%%...%%%%%%..%%..%%....%%....%%..%%..%%..%%.
........................................................................
New datasets downloading:
https://cancervar.wglab.org/databases/
"""
usage = """Usage: %prog [OPTION] -i INPUT -o OUTPUT ...
%prog --config=config.ini ...
"""
description = """=============================================================================
CancerVar
Interpretation of Pathogenic/Benign for cancer variants (v.1.1)
=============================================================================
........................................................................
..%%%%....%%%%...%%..%%...%%%%...%%%%%%..%%%%%...%%..%%...%%%%...%%%%%..
.%%..%%..%%..%%..%%%.%%..%%..%%..%%......%%..%%..%%..%%..%%..%%..%%..%%.
.%%......%%%%%%..%%.%%%..%%......%%%%....%%%%%...%%..%%..%%%%%%..%%%%%..
.%%..%%..%%..%%..%%..%%..%%..%%..%%......%%..%%...%%%%...%%..%%..%%..%%.
..%%%%...%%..%%..%%..%%...%%%%...%%%%%%..%%..%%....%%....%%..%%..%%..%%.
........................................................................
New datasets downloading:
https://cancervar.wglab.org/databases/
"""
end = """=============================================================================
........................................................................
..%%%%....%%%%...%%..%%...%%%%...%%%%%%..%%%%%...%%..%%...%%%%...%%%%%..
.%%..%%..%%..%%..%%%.%%..%%..%%..%%......%%..%%..%%..%%..%%..%%..%%..%%.
.%%......%%%%%%..%%.%%%..%%......%%%%....%%%%%...%%..%%..%%%%%%..%%%%%..
.%%..%%..%%..%%..%%..%%..%%..%%..%%......%%..%%...%%%%...%%..%%..%%..%%.
..%%%%...%%..%%..%%..%%...%%%%...%%%%%%..%%..%%....%%....%%..%%..%%..%%.
........................................................................
New datasets downloading:
https://cancervar.wglab.org/databases/
Thanks for using CancerVar!
Report bugs to [email protected];
Need OPAI(Oncogenic Prioritization by Artificial Intelligence)?
Please check https://github.com/WGLab/CancerVar
CancerVar homepage: <https://CancerVar.wglab.org>
=============================================================================
"""
if platform.python_version()< '3.0.0' :
import ConfigParser
else:
import configparser
paras = {}
def ConfigSectionMap(config,section):
global paras
options = config.options(section)
for option in options:
try:
paras[option] = config.get(section, option)
if paras[option] == -1:
DebugPrint("skip: %s" % option)
except:
print("exception on %s!" % option)
paras[option] = None
return
user_evidence_dict={}
class myGzipFile(gzip.GzipFile):
def __enter__(self):
if self.fileobj is None:
raise ValueError("I/O operation on closed GzipFile object")
return self
def __exit__(self, *args):
self.close()
#begin read some important datsets/list firstly;
lof_genes_dict={}
mim2gene_dict={}
mim2gene_dict2={}
exclude_snps_dict={}
mim_pheno_dict={}
mim_orpha_dict={}
orpha_dict={}
knownGeneCanonical_dict={}
knownGeneCanonical_st_dict={}
knownGeneCanonical_ed_dict={}
cancervar_markers_dict={}
cancer_pathway_dict={}
cancers_gene_dict={}
cancers_types_dict={}
cancervar_d=[]
def flip_ACGT(acgt):
nt="";
if acgt=="A":
nt="T"
if acgt=="T":
nt="A"
if acgt=="C":
nt="G"
if acgt=="G":
nt="C"
if acgt=="N":
nt="N"
if acgt=="X":
nt="X"
return(nt)
def read_datasets():
#0. read the user specified evidence file
if os.path.isfile(paras['evidence_file']):
try:
fh=open(paras['evidence_file'], "r")
strs = fh.read()
for line2 in strs.split('\n'):
cls2=line2.split('\t')
if len(cls2)>1:
keys=cls2[0]+"_"+cls2[1]+"_"+cls2[2]+"_"+cls2[3]+"_"+cls2[4]
keys=re.sub("[Cc][Hh][Rr]","",keys)
user_evidence_dict[keys]=cls2[5].upper()
print("%s %s\n" %(keys,user_evidence_dict[keys]))
except IOError:
print("Error: can\'t read the user specified evidence file %s" % paras['evidence_file'])
else:
fh.close()
#1.LOF gene list
try:
fh = open(paras['lof_genes'], "r")
strs = fh.read()
for line2 in strs.split('\n'):
cls2=line2.split('\t')
if len(cls2[0])>1:
lof_genes_dict[cls2[0]]='1'
except IOError:
print("Error: can\'t read the LOF genes file %s" % paras['lof_genes'])
print("Error: Please download it from the source website")
sys.exit()
return
else:
fh.close()
#1. cancervar_markers
global cancervar_d
try:
with open(paras['cancervar_markers']) as fh:
reader = csv.reader(fh, delimiter="\t")
cancervar_d = list(reader)
except IOError:
print("Error: can\'t read the cancervar_markers file %s" % paras['cancervar_markers'])
print("Error: Please download it from the source website")
sys.exit()
return
else:
fh.close()
#print len(cancervar_d)
for ii in range(0,len(cancervar_d)):
gene=cancervar_d[ii][0]
mut_type=cancervar_d[ii][1]
mut=cancervar_d[ii][2]
mut_types=mut_type.split(';'); # BIA CNA EXPR FUS MUT
list_mut_size=len(mut_types)
mut_alts=mut.split(';');
for jj in range(0,list_mut_size):
mut_list=mut_alts[jj].split(':')
if(len(mut_list)>1):
gene1=mut_list[0]
mutb=mut_list[1]
else:
gene1=gene
mutb=mut_alts[jj]
if(mut_types[jj]=="FUS"):
mutt="fus_"+re.sub('__','-',mut_alts[jj])
key=mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mut_types[jj]=="EXP" or mut_types[jj]=="EXPR"):
mutt=gene1+"_"+"expr_"+mutb
key=mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mut_types[jj]=="CNV" or mut_types[jj]=="CNA"):
mutt=gene1+"_"+"cna_"+mutb
key=mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mut_types[jj]=="BIA"):
mutt=gene1+"_"+"bia_"+mutb
key=mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mut_types[jj]=="OTH"):
mutt=gene1+"_"+"oth_"+mutb
key=mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
#start from mutb
elif(mut_types[jj]=="MUT"):
if(mutb=="any mutation"):
mutt="mut_any"
key=gene+'_'+mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mutb=="any insertion"):
mutt="insertion_any"
key=gene+'_'+mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mutb=="any deletion"):
mutt="deletion_any"
key=gene+'_'+mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mutb=="any frameshift" or mutb=="frameshift"):
mutt="frameshift_any"
key=gene+'_'+mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mutb=="any indel"):
mutt="indel_any"
key=gene+'_'+mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mutb=="any missense"):
mutt="missense_any"
key=gene+'_'+mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mutb=="mutant"):
mutt="mut_any"
key=gene+'_'+mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mutb=="mutation" or mutb=="ALTERATION"):
mutt="mut_any"
key=gene+'_'+mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mutb=="MUTATION"):
mutt="mut_any"
key=gene+'_'+mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mutb=="oncogenic mutation"):
mutt="mut_any"
key=gene+'_'+mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mutb=="Oncogenic Mutations"):
mutt="mut_any"
key=gene+'_'+mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
elif(mutb=="any nonsense"):
mutt="nonsense_any"
key=gene+'_'+mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
# format as " exon(s) 10, 20, 21 any"
elif( re.findall('exon\(s\) ', mutb, flags=re.IGNORECASE)):
str1,str2=mutb.split('exon(s) ',1);
if( not re.findall(',', mutb, flags=re.IGNORECASE)): # for single position
poss,mutc=str2.split(' ',1);
#mut0=re.sub('"','',mutb)
poss_t=int(poss)
muts="exon_"+str(poss_t)+"_"+mutc
key=gene+'_'+muts
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
else: # for multiple positions
list_pos=str2.split(',');
list_pos_size=len(list_pos)
list_tt=list_pos[list_pos_size-1].split(' ')
list_tt_size=len(list_tt)
mutc=list_tt[list_tt_size-1]
pos_end=str(int(list_tt[list_tt_size-2]))
if(mutc=="mutation"): mutc="any"
muts="exon_"+pos_end+"_"+mutc
key=gene+'_'+muts
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
for jj in range(0,list_pos_size-1):
pos_be=str(int(list_pos[jj]))
if(mutc=="mutation"): mutc="any"
muts="exon_"+pos_be+"_"+mutc
key=gene+'_'+muts
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
#print key,ii,cancervar_markers_dict[key]
# " codon(s) 289, 596, 598 any" codon(s) 132 any
elif( re.findall('codon\(s\) ', mutb, flags=re.IGNORECASE)):
str1,str2=mutb.split('codon(s) ',1);
if( not re.findall(',', mutb, flags=re.IGNORECASE)): # for single position
poss,mutc=str2.split(' ',1);
#mut0=re.sub('"','',mutb)
poss_t=int(poss)
muts="codon_"+str(poss_t)+"_"+mutc
key=gene+'_'+muts
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
else: # for multiple positions
list_pos=str2.split(',');
list_pos_size=len(list_pos)
list_tt=list_pos[list_pos_size-1].split(' ')
list_tt_size=len(list_tt)
mutc=list_tt[list_tt_size-1]
pos_end=str(int(list_tt[list_tt_size-2]))
muts="codon_"+pos_end+"_"+mutc
key=gene+'_'+muts
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
for jj in range(0,list_pos_size-1):
pos_be=str(int(list_pos[jj]))
muts="codon_"+pos_be+"_"+mutc
key=gene+'_'+muts
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
else:
mutt=gene1+"_"+mutb
key=mutt
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
else:
key=gene1+"_"+mutb
default_s=''
cancervar_markers_dict[key]=str(ii)+","+cancervar_markers_dict.get(key,default_s)
# print cancervar_markers_dict
# end deal with cancervar_markers
#4. OMIM mim2gene.txt file
try:
fh = open(paras['mim2gene'], "r")
strs = fh.read()
for line2 in strs.split('\n'):
cls2=line2.split('\t')
if len(cls2)>1:
cls0=cls2[4].split(',')
keys=cls0[0]
mim2gene_dict[keys]=cls2[0]
keys1=cls2[3]
keys=keys1.upper()
mim2gene_dict2[keys]=cls2[0]
except IOError:
print("Error: can\'t read the OMIM file %s" % paras['mim2gene'])
print("Error: Please download it from http://www.omim.org/downloads")
sys.exit()
else:
fh.close()
#5. read the user specified SNP list, the variants will pass the frequency check.
if os.path.isfile(paras['exclude_snps']):
try:
fh=open(paras['exclude_snps'], "r")
strs = fh.read()
for line2 in strs.split('\n'):
cls2=line2.split('\t')
if len(cls2)>1:
keys=cls2[0]+"_"+cls2[1]+"_"+cls2[2]+"_"+cls2[3]
keys=re.sub("[Cc][Hh][Rr]","",keys)
exclude_snps_dict[keys]="1"
except IOError:
print("Error: can\'t read the user specified SNP list file %s" % paras['exclude_snps'])
else:
fh.close()
#6. knownGeneCanonical exon file # caution the build ver, now it is hg19
try:
fh = open(paras['knowngenecanonical'], "r")
strs = fh.read()
for line2 in strs.split('\n'):
cls2=line2.split(' ')
if len(cls2)>1:
keys=cls2[0]
knownGeneCanonical_dict[keys]=cls2[1]
knownGeneCanonical_st_dict[keys]=cls2[2]
knownGeneCanonical_ed_dict[keys]=cls2[3]
except IOError:
print("Error: can\'t read the knownGeneCanonical file %s" % paras['knowngenecanonical'])
print("Error: Please download it from the source website")
sys.exit()
else:
fh.close()
#7. OMIM mim_pheno.txt file
try:
fh = open(paras['mim_pheno'], "r")
strs = fh.read()
for line2 in strs.split('\n'):
cls2=line2.split(' ')
if len(cls2)>1:
keys=cls2[0]
mim_pheno_dict[keys]=cls2[1]
except IOError:
print("Error: can\'t read the MIM file %s" % paras['mim_pheno'])
print("Error: Please download it from CancerVar source website")
sys.exit()
else:
fh.close()
#8. OMIM mim_orpha.txt file
try:
fh = open(paras['mim_orpha'], "r")
strs = fh.read()
for line2 in strs.split('\n'):
cls2=line2.split(' ')
if len(cls2)>1:
keys=cls2[0]
mim_orpha_dict[keys]=cls2[1]
except IOError:
print("Error: can\'t read the MIM file %s" % paras['mim_orpha'])
print("Error: Please download it from CancerVar source website")
sys.exit()
else:
fh.close()
#9. orpha.txt file
try:
fh = open(paras['orpha'], "r")
strs = fh.read()
for line2 in strs.split('\n'):
cls2=line2.split('\t')
if len(cls2)>1:
keys=cls2[0]
orpha_dict[keys]=cls2[1]
except IOError:
print("Error: can\'t read the Orpha file %s" % paras['orpha'])
print("Error: Please download it from CancerVar source website")
sys.exit()
else:
fh.close()
#10 cancer_pathway=%(database_cancervar)s/cancer_pathway.list
global cancer_pathway_dict
try:
fh = open(paras['cancer_pathway'], "r")
strs = fh.read()
for line2 in strs.split('\n'):
cls2=line2.split('\t')
if len(cls2[0])>1:
#cancer_pathway_dict[cls2[0]]='1'
cancer_pathway_dict[cls2[0]]=cls2[1]
except IOError:
print("Error: can\'t read the cancer_pathway genes file %s" % paras['cancer_pathway'])
print("Error: Please download it from the source website")
sys.exit()
return
else:
fh.close()
#11 cancers_genes=%(database_cancervar)s/cancers_genes.list
global cancers_gene_dict
try:
fh = open(paras['cancers_genes'], "r")
strs = fh.read()
for line2 in strs.split('\n'):
cls2=line2.split('\t')
if len(cls2[0])>1:
cancers_gene_dict[cls2[0]]='1'
except IOError:
print("Error: can\'t read the cancers diseases genes file %s" % paras['cancer_pathway'])
print("Error: Please download it from the source website")
sys.exit()
return
else:
fh.close()
#12 cancers_types=%(database_cancervar)s/cancervar.cancer.types
global cancers_types_dict
try:
fh = open(paras['cancers_types'], "r")
strs = fh.read()
for line2 in strs.split('\n'):
cls2=line2.split('/')
if len(cls2)>1:
cancers_types_dict[cls2[1]]=cls2[0];
except IOError:
print("Error: can\'t read the cancers types file %s" % paras['cancers_types'])
print("Error: Please download it from the source website")
sys.exit()
return
else:
fh.close()
#end read datasets
return
def check_downdb():
path=paras['database_locat']
path=path.strip()
path=path.rstrip("\/")
isExists=os.path.exists(path)
if not isExists:
os.makedirs(path)
print("Notice: the folder of %s is created!" % path)
else:
print("Warning: the folder of %s is already created!" % path)
ds=paras['database_names']
ds.expandtabs(1);
# database_names = refGene 1000g2014oct esp6500siv2_all avsnp147 ljb26_all clinvar_20150629 exac03 hg19_dbscsnv11 dbnsfp31a_interpro rmsk ensGene
for dbs in ds.split():
# os.path.isfile(options.table_annovar)
file_name=dbs
if dbs=="1000g2015aug":
file_name="ALL.sites.2015_08"
dataset_file=paras['database_locat']+"/"+paras['buildver']+"_"+file_name+".txt"
if dbs != 'rmsk':
cmd="perl "+paras['annotate_variation']+" -buildver "+paras['buildver']+" -downdb -webfrom annovar "+file_name+" "+paras['database_locat']
if dbs == 'rmsk':
cmd="perl "+paras['annotate_variation']+" -buildver "+paras['buildver']+" -downdb "+file_name+" "+paras['database_locat']
if not os.path.isfile(dataset_file):
if dbs=="1000g2015aug":
file_name="1000g2015aug"
dataset_file=paras['database_locat']+"/"+paras['buildver']+"_"+file_name+".txt"
cmd="perl "+paras['annotate_variation']+" -buildver "+paras['buildver']+" -downdb -webfrom annovar "+file_name+" "+paras['database_locat']
print("Warning: The Annovar dataset file of %s is not in %s,begin to download this %s ..." %(dbs,paras['database_locat'],dataset_file))
print("%s" %cmd)
os.system(cmd)
return
def check_input():
inputft= paras['inputfile_type']
if inputft.lower() == 'avinput' :
return
if inputft.lower() == 'vcf':
if os.path.isfile(paras['convert2annovar']):
#convert2annovar.pl -format vcf4 variantfile > variant.avinput
cmd="perl "+paras['convert2annovar']+" -format vcf4 "+ paras['inputfile']+"> "+paras['inputfile']+".avinput"
print("Warning: Begin to convert your vcf file of %s to AVinput of Annovar ..." % paras['inputfile'])
print("%s" %cmd)
os.system(cmd)
else:
print("Error: The Annovar file [ %s ] is not here,please download ANNOVAR firstly: http://www.openbioinformatics.org/annovar"
% paras['convert2annovar'])
sys.exit()
if inputft.lower() == 'vcf_m':
if os.path.isfile(paras['convert2annovar']):
#convert2annovar.pl -format vcf4 variantfile > variant.avinput
cmd="perl "+paras['convert2annovar']+" -format vcf4 "+ paras['inputfile']+" --allsample --outfile "+ paras['outfile']
print("Warning: Begin to convert your vcf file with multiple samples of %s to AVinput of Annovar with All.raw.highqc.vcf.<samplename>.avinput..." % paras['inputfile'])
print("Warning: Please attention that the sample names in VCF file should contain letters/numners only, otherwise the converting may be failure!")
print("%s" %cmd)
os.system(cmd)
else:
print("Error: The Annovar file [ %s ] is not here,please download ANNOVAR firstly: http://www.openbioinformatics.org/annovar"
% paras['convert2annovar'])
sys.exit()
return
def check_annovar_result():
# table_annovar.pl example/ex1.avinput humandb/ -buildver hg19 -out myanno -remove -protocol refGene,esp6500siv2_all,1000g2015aug_all,avsnp144,ljb26_all,CLNSIG,exac03 -operation g,f,f,f,f,f,f -nastring . -csvout
inputft= paras['inputfile_type']
annovar_options=" --otherinfo "
#if re.findall('true',paras['otherinfo'], flags=re.IGNORECASE) :
# annovar_options=annovar_options+"--otherinfo "
if re.findall('true',paras['onetranscript'], flags=re.IGNORECASE) :
annovar_options=annovar_options+"--onetranscript "
if not os.path.isfile(paras['table_annovar']):
print("Error: The Annovar file [ %s ] is not here,please download ANNOVAR firstly: http://www.openbioinformatics.org/annovar"
% paras['table_annovar'])
sys.exit()
if inputft.lower() == 'avinput' :
#cmd="perl "+paras['table_annovar']+" "+paras['inputfile']+" "+paras['database_locat']+" -buildver "+paras['buildver']+" -remove -out "+ paras['outfile']+" -protocol refGene,esp6500siv2_all,1000g2015aug_all,avsnp147,dbnsfp30a,clinvar_20160302,exac03,dbscsnv11,dbnsfp31a_interpro,rmsk,ensGene,knownGene -operation g,f,f,f,f,f,f,f,f,r,g,g -nastring ."+annovar_options
cmd="perl "+paras['table_annovar']+" "+paras['inputfile']+" "+paras['database_locat']+" -buildver "+paras['buildver']+" -remove -out "+ paras['outfile']+" -protocol refGene,ensGene,knownGene,esp6500siv2_all,1000g2015aug_all,exac03,avsnp147,dbnsfp30a,dbscsnv11,dbnsfp31a_interpro,clinvar_20190305,cosmic91,icgc28,gnomad_genome -operation g,g,g,f,f,f,f,f,f,f,f,f,f,f -nastring ."+annovar_options
print("%s" %cmd)
os.system(cmd)
if inputft.lower() == 'vcf' :
cmd="perl "+paras['table_annovar']+" "+paras['inputfile']+".avinput "+paras['database_locat']+" -buildver "+paras['buildver']+" -remove -out "+ paras['outfile']+" -protocol refGene,ensGene,knownGene,esp6500siv2_all,1000g2015aug_all,exac03,avsnp147,dbnsfp30a,dbscsnv11,dbnsfp31a_interpro,clinvar_20190305,cosmic91,icgc28,gnomad_genome -operation g,g,g,f,f,f,f,f,f,f,f,f,f,f -nastring ."+annovar_options
print("%s" %cmd)
os.system(cmd)
if inputft.lower() == 'vcf_m' :
for f in glob.iglob(paras['outfile']+"*.avinput"):
print("INFO: Begin to annotate sample file of %s ...." %(f))
new_outfile=re.sub(".avinput","",f)
cmd="perl "+paras['table_annovar']+" "+f+" "+paras['database_locat']+" -buildver "+paras['buildver']+" -remove -out "+ new_outfile +" -protocol refGene,ensGene,knownGene,esp6500siv2_all,1000g2015aug_all,exac03,avsnp147,dbnsfp30a,dbscsnv11,dbnsfp31a_interpro,clinvar_20161128,cosmic91,icgc28 -operation g,g,g,f,f,f,f,f,f,f,f,f,f -nastring ."+annovar_options
print("%s" %cmd)
os.system(cmd)
return
def check_genes(anvfile):
#check with multiple genes, so one gene by one gene to annote
newoutfile=anvfile+".grl_p"
try:
fh = open(anvfile, "r")
fw = open(newoutfile, "w")
strs = fh.read()
sum=0
otherinf_pos=1
for line in strs.split('\n'):
cls=line.split('\t')
if len(cls)>1:
if sum==0 and re.findall('true',paras['otherinfo'], flags=re.IGNORECASE) :
for ii in range(0,len(cls)):
if re.findall('otherinfo',cls[ii], flags=re.IGNORECASE) :
otherinf_pos=ii
gene_name=cls[6]
if cls[6] == 'Gene.refGene':
gene_name='Gene'
#some with multiple genes, so one gene by one gene to annote
sum=sum+1
for gg in gene_name.split(','):
if not re.findall('true',paras['otherinfo'], flags=re.IGNORECASE) :
line_out=line+"\t"+gg
else:
line_out=cls[0]
for ii in range(1,len(cls)):
if ii != otherinf_pos :
line_out=line_out+"\t"+cls[ii]
if ii == otherinf_pos :
line_out=line_out+"\t"+gg+"\t"+cls[ii]
if sum >1: line_out=re.sub("^[Cc][Hh][Rr]","",line_out)
fw.write("%s\t\n" % line_out)
except IOError:
print("Error: can\'t read/write the annovar output file %s %s" % (anvfile,newoutfile))
sys.exit()
return
else:
pass
fh.close()
fw.close()
return(sum)
def sum_of_list(list):
sum=0
for i in list:
sum=sum+i
return(sum)
def classfy(CBP,Allels_flgs,cls):
#BPS=["Pathogenic","Likely_pathogenic","Benign/Likely_benign","Uncertain_significance"]
BPS=["Tier_I_strong","Tier_II_potential","Tier_IV_benign","Tier_III_Uncertain"]
PAS_out=-1
BES_out=-1
BPS_out=3 # BPS=[3]:Uncertain significance
CBP_sum=0;
# CBP[0]:Therapeutic(2100) CBP[1]:Diagno CBP[2]:Progno CBP[3]:Mutation(1100) CBP[4]:Variant_freq CBP[5]:Potential_germ
# CBP[6]: Populatio(1100) CBP[7]:Germline dat(2201) CBP[8]:Somatic dat(2100) CBP[9]:Predict_dama(2201)
# CBP[10]: Path(2100) CBP[11] : Pubs
#begin process the user's flexible grade to get the final interpretation
if os.path.isfile(paras['evidence_file']):
keys=cls[Allels_flgs['Chr']]+"_"+cls[Allels_flgs['Start']]+"_"+cls[Allels_flgs['End']]+"_"+cls[Allels_flgs['Ref']]+"_"+cls[Allels_flgs['Alt']]
keys=re.sub("[Cc][Hh][Rr]","",keys)
try:
evds=user_evidence_dict[keys] #PS1=1;PM1=1;BA1=1;PVS1 PP BS BP
for evd in evds.split(';'):
evd_t=evd.split('=')
if(len(evd_t)>1 and re.findall('grade', evd_t[0], flags=re.IGNORECASE) ):
#10 104353782 G A PVS1=1;PP1=1;PM3=1;grade_PP1=2;
if int(evd_t[1])<=3:
#print ("%s %s %s " %(keys,evd_t[1],evd_t[0]))
if(evd_t[0].find('CBP')!=-1):
t=evd_t[0].find('CBP');
tt=evd_t[0];
tt3=int(tt[t+3:t+4])
if(t<len(evd_t[0])-2 and tt3<=12 ): CBP[tt3-1]=int(evd_t[1])
except KeyError:
pass
else:
pass
# end process the user's flexible grade
#this is old scoring, removed
# if CBP[0]==2 and CBP[3]==1 and CBP[6]==1 and CBP[7]==2 and CBP[8]==2 and CBP[9]==2 and CBP[10]==2:
# BPS_out=0
# if CBP[0]==1 and CBP[3]==1 and CBP[6]==1 and CBP[7]==2 and CBP[8]>=1 and CBP[9]==2 and CBP[10]>=1:
# BPS_out=1
# if CBP[0]==0 and CBP[3]==0 and CBP[6]==0 and CBP[7]==0 and CBP[8]==0 and CBP[9]==0 and CBP[10]==0:
# BPS_out=3
# if CBP[0]==0 and CBP[3]==0 and CBP[6]==0 and CBP[7]>=0 and CBP[8]==0 and CBP[9]<=1 and CBP[10]==0:
# BPS_out=2
# EVS=[1, None, None, 1, None, None, 1, 2, 2, 2, 2, None]
# [1, None, None, 1, None, None, 1, 0, 1, 2, 2, None]
#begin new scoring.
for i in range(0,12): CBP_sum=CBP_sum+CBP[i]
if(CBP_sum<=2): BPS_out=2 # benign
if(CBP_sum>=3 and CBP_sum<8): BPS_out=3 # VUS
if(CBP_sum>=8 and CBP_sum<=10): BPS_out=1 # potential path
if(CBP_sum>=11): BPS_out=0 # strong path
CBPout=str(CBP_sum)+"#"+BPS[BPS_out]
return(CBPout)
def check_Thera(line,Funcanno_flgs,Allels_flgs,lof_genes_dict):
'''Therapeutic:
2 FDA approved or investigational with strong evidence
1 FDA approved for different tumor type; investigational therapies with some evidence
0 Cancer genes: none
0 None
# the function of check_Thera check_Diagno check_Progno are similar, should combine as one function,
but in future for different purpose and criteria, leave three functions.
'''
Thera=0;
global cancervar_d;
level=0 # ABCD
cls=line.split('\t')
clstt=cls[Funcanno_flgs['Otherinfo']].split(';')
cancer_type="Cancer"
out_list=""
#if (len(clstt[0])>0):
# cancer_type=clstt[0]
try:
cancer_type=paras['cancer_type']
except KeyError:
if (len(clstt[0])>0): cancer_type=clstt[0]
else:
pass
gene_tr=cls[Funcanno_flgs['Gene']]
func=cls[Funcanno_flgs['Func.refGene']]
exonfunc=cls[Funcanno_flgs['Func.refGene']]
# ABL1:NM_005157:exon6:c.C944T:p.T315I,ABL1:NM_007313:exon6:c.C1001T:p.T334I
# AAChange.refGene only check variant with AAchange information
line_tmp=cls[Funcanno_flgs['Gene']]+" "+cls[Funcanno_flgs['Func.refGene']]+" "+cls[Funcanno_flgs['ExonicFunc.refGene']]+" "+cancer_type
line_tmp2=cls[Funcanno_flgs['AAChange.refGene']]
#line_tmp2_sp=line_tmp2.split(',')
line_tmp2_sp=re.split(';|,',line_tmp2)
exon_func=cls[Funcanno_flgs['ExonicFunc.refGene']]
marker_key0=gene_tr+"_"+"mut_any"
out_list=""
for cls0 in line_tmp2_sp:
cls0_1=cls0.split(':')
if(len(cls0)>1 and len(line_tmp2_sp)>0 and len(cls0_1)>=4 ):
cls0_1=cls0.split(':')
gene=cls0_1[0]
transc=cls0_1[1]
exon=re.sub('exon','',cls0_1[2])
cdna_change=re.sub('c.','',cls0_1[3])
amino_change="NA";
if(len(cls0_1)==5):amino_change=re.sub('p.','',cls0_1[4])
ltt=len(amino_change)
codon_num="NA";
if(len(cls0_1)==5):codon_num=amino_change[1:ltt-1]
#print gene, transc,exon,cdna_change,amino_change,cancer_type
marker_key=gene+"_"+amino_change
marker_key0=gene+"_"+"mut_any"
marker_key1=gene+"_"+"exon_"+exon+"_any"
marker_key2=gene+"_"+"codon_"+codon_num+"_any"
marker_key00=""
marker_key11=""
marker_key22=""
if(cls[Funcanno_flgs['ExonicFunc.refGene']]=="nonsynonymous SNV"):
marker_key00=gene+"_"+"missense_any"
marker_key11=gene+"_"+"exon_"+exon+"_missense"
marker_key22=gene+"_"+"codon_"+codon_num+"_missense"
if exon_func.find("frameshift")>=0 and exon_func.find("nonframe")<0 :
marker_key00=gene+"_"+"frameshift_any"
marker_key11=gene+"_"+"exon_"+exon+"_frameshift"
marker_key22=gene+"_"+"codon_"+codon_num+"_frameshift"
if exon_func.find("stopgain")>=0 or exon_func.find("stoploss")>=0:
marker_key00=gene+"_"+"nonsense_any"
marker_key11=gene+"_"+"exon_"+exon+"_nonsense"
marker_key22=gene+"_"+"codon_"+codon_num+"_nonsense"
if exon_func.find("deletion")>=0 :
marker_key00=gene+"_"+"deletion_any"
marker_key11=gene+"_"+"exon_"+exon+"_deletion"
marker_key22=gene+"_"+"codon_"+codon_num+"_deletion"
if exon_func.find("insertion")>=0 :
marker_key00=gene+"_"+"insertion_any"
marker_key11=gene+"_"+"exon_"+exon+"_insertion"
marker_key22=gene+"_"+"codon_"+codon_num+"_insertion"
# caution: frameshift insertion or deletion
#deletion frameshift indel missense nonsense_any
# nonsynonymous SNV;stopgain/stoploss(nonsense);frameshift insertion/deletion/substitution; nonframeshift insertion/deletion/substitution;
default_s=""
add_list=cancervar_markers_dict.get(marker_key,default_s)+cancervar_markers_dict.get(marker_key0,default_s)+cancervar_markers_dict.get(marker_key00,default_s)+cancervar_markers_dict.get(marker_key1,default_s)+cancervar_markers_dict.get(marker_key11,default_s)+cancervar_markers_dict.get(marker_key2,default_s)+cancervar_markers_dict.get(marker_key22,default_s)
cgi_list=add_list
#out_list="";
level_AB="0"
level_CD="0"
level=0 # ABCD
#out_list=""
for i in cgi_list.split(","):
#print i
if(len(i)>0):
pos=int(i)
if(cancervar_d[pos][9]=="Therapeutic"):
out_list=str(pos)+","+out_list
t_level=cancervar_d[pos][10]
#print gene,pos,t_level,cancervar_d[pos][10],cancervar_d[pos][9]
if(t_level=='A' and level < 2 ): level=2;
if(t_level=='B' and level < 2 ): level=2;
if(t_level=='C' and level < 1 ): level=1;
if(t_level=='D' and level < 1): level=1;
#print cancer_type,cancervar_d[pos][8]
if( not re.findall(cancer_type, cancervar_d[pos][8], flags=re.IGNORECASE) ):
if(t_level=='A' or t_level=='B'): # did not find the specific type, decrease level
level=1;
#print gene,level,"Therapeutic"
Thera=level
if(out_list==""): out_list="."
out_list_t=list(set(out_list.split(",")))
str_out_t = ",".join(out_list_t)
out_list=str_out_t;
return(Thera,out_list)
#print line_tmp
def check_Diagno(line,Funcanno_flgs,Allels_flgs,lof_genes_dict):
'''Diagnostic:
In PG or reported evidence with consensus
not in PG but with convincing published data
Cancer genes: none
None
'''
Diagno=0
global cancervar_d;
level=0 # ABCD
cls=line.split('\t')
clstt=cls[Funcanno_flgs['Otherinfo']].split(';')
cancer_type="Cancer"
out_list=""
#if (len(clstt[0])>0):
# cancer_type=clstt[0]
try:
cancer_type=paras['cancer_type']
except KeyError:
if (len(clstt[0])>0): cancer_type=clstt[0]
else:
pass
gene_tr=cls[Funcanno_flgs['Gene']]
func=cls[Funcanno_flgs['Func.refGene']]
exonfunc=cls[Funcanno_flgs['Func.refGene']]
# ABL1:NM_005157:exon6:c.C944T:p.T315I,ABL1:NM_007313:exon6:c.C1001T:p.T334I
# AAChange.refGene only check missense with AAchange
line_tmp=cls[Funcanno_flgs['Gene']]+" "+cls[Funcanno_flgs['Func.refGene']]+" "+cls[Funcanno_flgs['ExonicFunc.refGene']]+" "+cancer_type
line_tmp2=cls[Funcanno_flgs['AAChange.refGene']]
#line_tmp2_sp=line_tmp2.split(',')
line_tmp2_sp=re.split(';|,',line_tmp2)
exon_func=cls[Funcanno_flgs['ExonicFunc.refGene']]
marker_key0=gene_tr+"_"+"mut_any"
out_list=""
for cls0 in line_tmp2_sp:
cls0_1=cls0.split(':')
if(len(cls0)>1 and len(line_tmp2_sp)>0 and len(cls0_1)>=4 ):
cls0_1=cls0.split(':')
gene=cls0_1[0]
transc=cls0_1[1]
exon=re.sub('exon','',cls0_1[2])
cdna_change=re.sub('c.','',cls0_1[3])
amino_change="NA";
if(len(cls0_1)==5):amino_change=re.sub('p.','',cls0_1[4])
ltt=len(amino_change)
codon_num="NA";
if(len(cls0_1)==5):codon_num=amino_change[1:ltt-1]
#print gene, transc,exon,cdna_change,amino_change,cancer_type
marker_key=gene+"_"+amino_change
marker_key0=gene+"_"+"mut_any"
marker_key1=gene+"_"+"exon_"+exon+"_any"
marker_key2=gene+"_"+"codon_"+codon_num+"_any"
marker_key00=""
marker_key11=""
marker_key22=""
if(cls[Funcanno_flgs['ExonicFunc.refGene']]=="nonsynonymous SNV"):
marker_key00=gene+"_"+"missense_any"
marker_key11=gene+"_"+"exon_"+exon+"_missense"
marker_key22=gene+"_"+"codon_"+codon_num+"_missense"
if exon_func.find("frameshift")>=0 and exon_func.find("nonframe")<0 :
marker_key00=gene+"_"+"frameshift_any"
marker_key11=gene+"_"+"exon_"+exon+"_frameshift"
marker_key22=gene+"_"+"codon_"+codon_num+"_frameshift"
if exon_func.find("stopgain")>=0 or exon_func.find("stoploss")>=0:
marker_key00=gene+"_"+"nonsense_any"
marker_key11=gene+"_"+"exon_"+exon+"_nonsense"
marker_key22=gene+"_"+"codon_"+codon_num+"_nonsense"
if exon_func.find("deletion")>=0 :
marker_key00=gene+"_"+"deletion_any"
marker_key11=gene+"_"+"exon_"+exon+"_deletion"
marker_key22=gene+"_"+"codon_"+codon_num+"_deletion"
if exon_func.find("insertion")>=0 :
marker_key00=gene+"_"+"insertion_any"
marker_key11=gene+"_"+"exon_"+exon+"_insertion"
marker_key22=gene+"_"+"codon_"+codon_num+"_insertion"
#deletion frameshift indel missense nonsense_any
# nonsynonymous SNV;stopgain/stoploss(nonsense);frameshift insertion/deletion/substitution; nonframeshift insertion/deletion/substitution;
default_s=""
add_list=cancervar_markers_dict.get(marker_key,default_s)+cancervar_markers_dict.get(marker_key0,default_s)+cancervar_markers_dict.get(marker_key00,default_s)+cancervar_markers_dict.get(marker_key1,default_s)+cancervar_markers_dict.get(marker_key11,default_s)+cancervar_markers_dict.get(marker_key2,default_s)+cancervar_markers_dict.get(marker_key22,default_s)
cgi_list=add_list
#print gene,add_list
level_AB="0"
level_CD="0"
level=0 # ABCD
#out_list=""
for i in cgi_list.split(","):
#print i
if(len(i)>0):
pos=int(i)
if(cancervar_d[pos][9]=="Diagnostic"):
t_level=cancervar_d[pos][10]
out_list=str(pos)+","+out_list
#print gene,pos,t_level,cancervar_d[pos][10],cancervar_d[pos][9]
if(t_level=='A' and level < 2 ): level=2;
if(t_level=='B' and level < 2 ): level=2;
if(t_level=='C' and level < 1 ): level=1;
if(t_level=='D' and level < 1 ): level=1;
if( not re.findall(cancer_type, cancervar_d[pos][8], flags=re.IGNORECASE) ):
if(t_level=='A' or t_level=='B'): # did not find the specific type, decrease level
level=1;
#print gene,level
Diagno=level;
if(out_list==""): out_list="."
out_list_t=list(set(out_list.split(",")))
str_out_t = ",".join(out_list_t)
out_list=str_out_t;
return(Diagno,out_list)
def check_Progno(line,Funcanno_flgs,Allels_flgs,lof_genes_dict):
'''Prognostic:
In PG or reported evidence with consensus