GO analysis for non-mainstream species (using Panther annotation)

[yvan.wenger]

Hello,

I am sure this question has been asked many times, but so far I couldn't find a satisfactory answer to my question, so I am trying it here...

Most of the tools available use background frequencies derived from specific organisms (i.e. it is not possible to input the background frequencies of my organism). It is a real problem because I want to input annotated data derived from a de-novo transcriptome assembly, which is quite redundant (I have many variants of the same transcript).

Here is the approach I would like to follow, if anyone has a better suggestion of did it already, I would be extremely glad if they could comment:

1. Annotation of my in-silico derived protein set using PantherDB with pantherScore2.1.pl, which will constitute my background

2. Retrieve GO terms using the PANTHER HMM annotation files (which contain GO annotation for panther (sub)families

3. Perform a GO gene set analysis using a tool that takes into account the GO hierarchy, such as the GO::TermFinder perl module. http://search.cpan.org/dist/GO-TermF.../TermFinder.pm

Alternatives:
- I know that I can perform GO analyses on the Panther website, but I would like to perform them locally (I have hundreds of groups to test), if someone did it already I will persevere.
- For #3: if there is a R package to do that, even better.
- Is there a simple way in GO::TermFinder to input background frequencies?

If you have any suggestion, feel free to comment, and as I know that we are many struggling with the same topic, I will follow up here when I get significant advances on my side.

Thanks !

Yvan

[neavemj]

Hi Yvan,

I can recommend topGO (http://bioconductor.org/packages/rel...tml/topGO.html) as an R package that will do GO enrichment for you on a non-model organism. You basically just provide the GO background for your organism, then a list of DE genes, and it will calculate the enriched terms. It has a few different options for the statistical tests.

Best,

Matt.