Hi Kane,

Thanks for the question! And I see this is your first post - welcome to the ResearchGate community :)

For a large dataset of gene names, I would recommend using the R package 'goseq'. This will allow you to determine the primary GO term for each gene, as well as the most enriched GO terms amongst your gene set, as ranked by a p-value statistic. This analysis can also be done separately for the three main categories of GO terms (i.e. biological process, cellular component, and molecular function). I have attached an R script of a goseq analysis I have done previously (starts on page 9) for your reference.

Please don't hesitate to ask any questions.

Happy ontologising!

Your new friend in the community,

Alex Nagle

Hi Alex,

Thanks for such an informative reply, your goseq analysis document will surely prove incredibly useful for my current and future analysis. However, I have already acquired similar data output by goseq (principle GO term, enriched GO terms etc) and am instead more interesting in every mapped ontology term to an individual gene.

I specifically want to pull-out every gene associated with an ontology term, i.e KEGG: Glycyolsis. I have found a manual workaround for this however, perhaps I will share with you. :) Thank you anyway, have a good day

Hi Kane Foster ,

You might be interested in a simple suite of gene list enrichment analysis, such as Enrichr (https://amp.pharm.mssm.edu/Enrichr/). This tool was developed by the Ma'ayan Laboratory, Computational Systems Biology, which has several resources depending on what you are working on (https://labs.icahn.mssm.edu/maayanlab/resources/).

Here for using in R (https://cran.r-project.org/web/packages/enrichR/vignettes/enrichR.html)

Hope this help.

Greetings!

There exist various platform-specific and independent gene ontology analysis tools and packages. For example, EnrichR, ShinyGO v0.61, Metascape etc