Question

How do we get genes involved in all immune system pathways?

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Recep ▴ 20

@7c8fd686

Last seen 2.2 years ago

Germany

Hi guys,

im new in bioinformatics. Im researching the interaction between microbiota and immunity.

I want to get all the genes, which play role a role in the immune system.

Here you can see all pathways related to immune system

Manually i can find genes in each pathway, but there are lots of pathways. So i want to make it short.

If you have any advice, i would be grateful.

Best

keggorthology KEGG • 1.1k views

ADD COMMENT • link updated 2.3 years ago by sandmann.t ▴ 70 • written 2.4 years ago by Recep ▴ 20

score 0 · Answer 1 · 2022-07-27

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James W. MacDonald 67k

@james-w-macdonald-5106

Last seen 12 hours ago

United States

There's probably a more sophisticated way to do this, but here's a semi-brute force way, using KEGG's REST API.

> gnmap <- read.table("https://rest.kegg.jp/link/hsa/pathway/", sep = "\t")
> names(gnmap) <- c("Path","GeneID")
## Woo brute force
> whatIwant <- c(paste0("path:hsa046", c(40,10,11,13,20,24,21,22,23,25,60,12,60,58,59,57,62,64,66,70,72)), "path:hsa04062")
> gnmap2 <- subset(gnmap, Path %in% whatIwant)
> library(org.Hs.eg.db)
> gnmap2$Symbol <- mapIds(org.Hs.eg.db, gsub("hsa:", "", gnmap2$GeneID), "SYMBOL", "ENTREZID")
'select()' returned 1:1 mapping between keys and columns
> head(gnmap2)
              Path    GeneID  Symbol
8506 path:hsa04062 hsa:10000    AKT3
8507 path:hsa04062 hsa:10235 RASGRP2
8508 path:hsa04062 hsa:10344   CCL26
8509 path:hsa04062 hsa:10451    VAV3
8510 path:hsa04062 hsa:10563  CXCL13
8511 path:hsa04062 hsa:10663   CXCR6

## Or maybe clean that up a bit.

> gnmap2$Path <- gsub("path:", "", gnmap2$Path)
> gnmap2$GeneID <-  gsub("hsa:", "", gnmap2$GeneID)
> head(gnmap2)
         Path GeneID  Symbol
8506 hsa04062  10000    AKT3
8507 hsa04062  10235 RASGRP2
8508 hsa04062  10344   CCL26
8509 hsa04062  10451    VAV3
8510 hsa04062  10563  CXCL13
8511 hsa04062  10663   CXCR6

ADD COMMENT • link 2.4 years ago James W. MacDonald 67k

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James' answer is great. Just for completeness, the limma package has two helper functions that might be helpful:

library(limma)

# get gene identifiers (= Entrez ids) & pathway ids 
genes <- getGeneKEGGLinks(species.KEGG = "hsa", convert = TRUE)

# get description for each pathway
pathways <- getKEGGPathwayNames(species.KEGG = "hsa", remove.qualifier = TRUE)

head(genes)
  GeneID     PathwayID
1  10327 path:hsa00010
2    124 path:hsa00010
3    125 path:hsa00010
4    126 path:hsa00010
5    127 path:hsa00010
6    128 path:hsa00010

> head(pathways)
      PathwayID                              Description
1 path:hsa00010             Glycolysis / Gluconeogenesis
2 path:hsa00020                Citrate cycle (TCA cycle)
3 path:hsa00030                Pentose phosphate pathway
4 path:hsa00040 Pentose and glucuronate interconversions
5 path:hsa00051          Fructose and mannose metabolism
6 path:hsa00052                     Galactose metabolism

pws <- split(genes$GeneID, genes$PathwayID)
names(pws) <- pathways[match(names(pws), pathways$PathwayID), "Description"]
pws[1]

> pws[1]
$`Glycolysis / Gluconeogenesis`
 [1] "10327"  "124"    "125"    "126"    "127"    "128"    "130"   
 [8] "130589" "131"    "160287" "1737"   "1738"   "2023"   "2026"  
[15] "2027"   "217"    "218"    "219"    "2203"   "221"    "222"   
[22] "223"    "224"    "226"    "229"    "230"    "2538"   "2597"  
[29] "26330"  "2645"   "2821"   "3098"   "3099"   "3101"   "387712"
[36] "3939"   "3945"   "3948"   "441531" "501"    "5105"   "5106"  
[43] "5160"   "5161"   "5162"   "5211"   "5213"   "5214"   "5223"  
[50] "5224"   "5230"   "5232"   "5236"   "5313"   "5315"   "55276" 
[57] "55902"  "57818"  "669"    "7167"   "80201"  "83440"  "84532" 
[64] "8789"   "92483"  "92579"  "9562"

ADD REPLY • link 2.3 years ago sandmann.t ▴ 70