Classify gene ontology (GO) enrichment terms
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Entering edit mode
18 months ago
yepeh72919 ▴ 10

Hello all,

Please bear with me, I have very little experience with bioinformatics and I'd really appreciate everyone's help with my questions.

I have some confusion regarding gene ontologies and the genes associated. I have some gene ontology terms enriched from a ChIP-seq experiment, and I'd like to be able to classify whether such genes belong to different categories.

While I understand that HOMER will check different libraries of gene grouping, like GO, COSMIC etc, then rank them according to p-values, I am unclear about what it means by 'Common Genes', and why each 'Term' has multiple genes, especially when row 1 and row 2 look to be related processes:

Picture from HOMER website: Homer pic here

Is it possible that I can use each of these Common Genes and categorize them accordingly (e.g. inflammatory genes, apoptosis signalling genes etc.)? If so, is there a bioinformatics tool that I use to categorize these genes, or some sort of database that I can use for inflammatory genes and match them up for all the Common Genes in each Term? Alternatively, does HOMER already 'classify' these genes for me through this gene enrichment?

I ask this since my significant Terms are from COSMIC (e.g. 'blood_vessel' or 'prostate'), and COSMIC is for somatic mutations in human cancer, which is not necessarily what I am studying.

Thanks so much!

ChIP-Seq homer gene ontology classification • 684 views
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Entering edit mode
18 months ago
tamerg ▴ 90

As a database you can check out the biobtree with its R/Python packages based on Ensembl or Uniprot GO terms. Following are some queries in R related to your cases.

1- Filter the genes with immune response

bbMapping("Malt1,Ccl2,CCL26",'filter(ensembl.genome=="homo_sapiens").map(go).filtergo.name=="immune response")',inattrs = "name")

2- Simalarly but based on Uniprot GO terms

bbMapping("Malt1,Ccl2,CCL26",'filter(ensembl.genome=="homo_sapiens").map(uniprot).map(go).filtergo.name=="defense response")',inattrs = "name")

3- All ensembl genes with inflammatory response

bbMapping("GO:0006954",'map(ensembl).filter(ensembl.genome=="homo_sapiens")',attrs = "name")

4- All ensembl genes with child GO terms of inflammatory response(GO:0006954)

bbMapping("GO:0006954",'map(gochild).map(ensembl).filter(ensembl.genome=="homo_sapiens")',attrs = "name")

Note: Due to the issue in rendering the query here "filtergo" must be filter(go

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Thanks so much for the very detailed workflow! Really appreciate the time you took to help me out; I will try this out and let you know if I have any more questions.

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