A tool to parse the results of BLAST/DIAMOND similarity searches made against the KEGG GENES prokaryotes database.
Distributed under the terms of the GNU AGPL v3 <https://www.gnu.org/licenses/agpl.html>.
Version: 0.9.1
Author: Igor S. Pessi
E-mail: [email protected]
library("devtools")
install_github("igorspp/keggR")
library("tidyverse")
library("keggR")
The first step is to run formatKEGG() to format the KEGG auxiliary files.
[COMING UP, NOT YET IMPLEMENTED].
Then we load the formatted auxiliary files.
For this, you need to run loadKEGG() giving the path to where the formatted files are located:
loadKEGG("~/KEGG")
## Reading PROKARYOTES.DAT
## Reading KO00000
## Reading KO00001
## Reading KO00002
The primary input for keggR is a BLAST/DIAMOND output table in the tabular format (outfmt 6).
The BLAST table can be filtered based on an evalue threshold by passing the EVALUE argument (default EVALUE = 0, i.e. no filtering is done).
blast <- readBlast("examples/input_data.txt")
blast
## # A tibble: 1,000 x 3
## sequence target evalue
## <chr> <chr> <chr>
## 1 read_1 gsu:GSU2105 1.7e-13
## 2 read_2 pla:Plav_0247 5.1e-18
## 3 read_5 mbry:B1812_20795 2.4e-15
## 4 read_6 aey:CDG81_18260 1.2e-11
## 5 read_7 amv:ACMV_04450 8.4e-21
## 6 read_11 pcu:pc0030 1.6e-19
## 7 read_14 slp:Slip_0435 1.2e-11
## 8 read_16 pht:BLM14_09585 5.3e-28
## 9 read_17 bic:LMTR13_36750 8.1e-24
## 10 read_18 vgo:GJW-30_1_03888 2.1e-11
## # … with 990 more rows
Now we use assignKEGG() to assign KO identifiers and gene names to the sequences.
KOtable <- blast %>%
assignKEGG
KOtable
## # A tibble: 559 x 4
## sequence evalue KO gene
## <chr> <chr> <chr> <chr>
## 1 read_1 1.7e-13 K01338 lon; ATP-dependent Lon protease
## 2 read_561 6.6e-18 K01338 lon; ATP-dependent Lon protease
## 3 read_2 5.1e-18 K13894 yejB; microcin C transport system permease protein
## 4 read_11 1.6e-19 K04077 groEL, HSPD1; chaperonin GroEL
## 5 read_222 6e-11 K04077 groEL, HSPD1; chaperonin GroEL
## 6 read_14 1.2e-11 K06442 tlyA; 23S rRNA (cytidine1920-2'-O)/16S rRNA (cytidine1409-2'-O)-methyltransferase
## 7 read_16 5.3e-28 K03046 rpoC; DNA-directed RNA polymerase subunit beta'
## 8 read_651 2.6e-14 K03046 rpoC; DNA-directed RNA polymerase subunit beta'
## 9 read_1562 4.4e-22 K03046 rpoC; DNA-directed RNA polymerase subunit beta'
## 10 read_1602 5.3e-15 K03046 rpoC; DNA-directed RNA polymerase subunit beta'
## # … with 549 more rows
And now we can summarise the pathways and modules found with summariseKEGG.
Here, if a sequence has more than one hit, the one with the lowest evalue will be used.
It is a good idea to use MinPath (https://omics.informatics.indiana.edu/MinPath) to remove spurious pathways.
For this you need to pass the directory where MinPath is installed.
summary <- KOtable %>%
summariseKEGG("~/bin/MinPath")
The output of summariseKEGG is a list. To access the summaries, for example for pathways summarised at the level 3, we do:
summary$pathways$level3
## # A tibble: 71 x 4
## level1 level2 level3 count
## <chr> <chr> <chr> <int>
## 1 Cellular Processes Cell growth and death 04112 Cell cycle - Caulobacter 9
## 2 Cellular Processes Cellular community - prokaryotes 02024 Quorum sensing 27
## 3 Cellular Processes Cellular community - prokaryotes 02026 Biofilm formation - Escherichia coli 3
## 4 Cellular Processes Cellular community - prokaryotes 05111 Biofilm formation - Vibrio cholerae 5
## 5 Cellular Processes Transport and catabolism 04146 Peroxisome 7
## 6 Environmental Information Processing Membrane transport 02010 ABC transporters 23
## 7 Environmental Information Processing Membrane transport 02060 Phosphotransferase system (PTS) 2
## 8 Environmental Information Processing Membrane transport 03070 Bacterial secretion system 4
## 9 Environmental Information Processing Signal transduction 02020 Two-component system 24
## 10 Genetic Information Processing Folding, sorting and degradation 03018 RNA degradation 9
## # … with 61 more rows
Or for modules summarised at the level 4:
summary$modules$level4
## # A tibble: 102 x 5
## level1 level2 level3 level4 count
## <chr> <chr> <chr> <chr> <int>
## 1 Functional set Environmental information processing Drug efflux transporter/pump M00646 Multidrug resistance, efflux pump AcrAD-TolC 2
## 2 Functional set Environmental information processing Drug efflux transporter/pump M00648 Multidrug resistance, efflux pump MdtABC 1
## 3 Functional set Environmental information processing Drug efflux transporter/pump M00701 Multidrug resistance, efflux pump EmrAB 2
## 4 Functional set Environmental information processing Drug resistance M00628 beta-Lactam resistance, AmpC system 1
## 5 Functional set Environmental information processing Drug resistance M00742 Aminoglycoside resistance, protease FtsH 3
## 6 Functional set Environmental information processing Two-component regulatory system M00452 CusS-CusR (copper tolerance) two-component regulatory system 1
## 7 Functional set Environmental information processing Two-component regulatory system M00454 KdpD-KdpE (potassium transport) two-component regulatory system 1
## 8 Functional set Environmental information processing Two-component regulatory system M00506 CheA-CheYBV (chemotaxis) two-component regulatory system 1
## 9 Functional set Environmental information processing Two-component regulatory system M00512 CckA-CtrA/CpdR (cell cycle control) two-component regulatory system 2
## 10 Functional set Metabolism Aminoacyl tRNA M00359 Aminoacyl-tRNA biosynthesis, eukaryotes 16
## # … with 92 more rows
summary$modules$level4 %>%
ggplot(aes(x = level4, y = count)) +
geom_bar(stat="identity") +
facet_grid("level2", scales = "free", space = "free") +
theme(strip.text = element_blank()) +
coord_flip()
