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I've recently discovered OrthoVenn2, which I'm using to compare proteomes and extract likely clade-specific genes. I'm enjoying software, and one of the things it offers is a breakdown of protein clusters in terms of Gene Ontology (example from a mock dataset). However, I find the Gene Ontology terms a bit too general, and would appreciate more specificity for my data.

I guess something more like the 'Subsystems' figure on MG-Rast datasets? In an ideal case, I'd enjoy something interactive where I could explore different levels of annotation (e.g. if I click on 'transferase' in the pie-chart, it would be nice to see 'methyl-trasferase', 'formyl-trasferase' etc), but I understand that might not exist and I'd be happy with something that requires a bit of command line usage too.

I'm working with a relatively medium number of genes (50-400) at one time.

Thank you for your time!

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  • $\begingroup$ How are the GO terms assigned? If it's just showing you the terms that are overrepresented in your group of proteins, then those are probably the most specific terms that are overrepresented and any more specific terms would not be overrepresented and hence are not shown. GO itself can be extremely specific, but that doesn't mean your data will be. $\endgroup$
    – terdon ♦
    May 13, 2020 at 15:41
  • $\begingroup$ @terdon Thank you. It looks like the software is using Swiss-Prot to cut down on computer memory, which might leave out some terms. I guess it might help if I ran the sequences I got there through another GO-finding software? $\endgroup$
    – Laura
    May 13, 2020 at 15:49
  • $\begingroup$ What? No, swiss-prot is just the well annotated version of trEMbl, it's a high confidence set of proteins and protein annotations. Nothing wrong with using swissprot and I don't see how that's relevant. My point is that if you are looking for overrepresented terms, then you will never find the most specific terms, you will find broader ones that are shared by many proteins. I don't think this is a limitation of the tool you are using, but of your data: why do you think that more specific terms exist in your case? $\endgroup$
    – terdon ♦
    May 13, 2020 at 15:52
  • $\begingroup$ @terdon Because I ran the same sequences through BlastKOALA (KEGG-based) and the results were more in line with what I had in mind (broad terms like 'Metabolic pathways' and 'Lipid metabolism', but also the possibility to zoom in to subgroups (such as 'Glycerolipid metabolism' or 'Glycerophospholipid metabolism', and even individual enzymes). I guess I'd like something similar to that. $\endgroup$
    – Laura
    May 13, 2020 at 16:27

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