What are the advantages of GO and KEGG? What should be as next step?

Question

[Amr]

After analyzing cell lines for cancer patients we extract the GO (Gene Ontology), we discover that some genes in our data are related to each other that for example, share GO or share a biological pathway from KEGG, How that be useful in Cancer Research, I mean, what will be the next step? How that will help us to discover which set of genes when they mutated caused cancer? Thanks in advance

Answer 1

[dsull]

First step is to define your biological question: Why are you doing GO analysis in the first place? What do you hope to gain from it? Do you have a hypothesis?

Also, it's difficult for us to answer when we have no idea what you actually did. Did you do whole genome sequencing, microarray, ChIP-Seq, RNA-seq, single-cell RNA-seq, etc.?

Doing bulk RNA-seq followed by GO and finding that proliferation genes get upregulated in cancer isn't going to tell you anything new.

Try to define a question that's interesting and novel; e.g. are there genes that consistently go up in subtype A of cancer but go down in subtype B of cancer.

There are plenty of papers that demonstrate how to do good data science with cancer data (e.g. https://pubmed.ncbi.nlm.nih.gov/31287989 ).