Functional enrichment analysis of bacteria

Question

[mickey_95]

Hello,

I have gene lists from an RNA-seq experiment from E.coli bacteria. So far, I have only worked with model organisms, which are supported by biomaRt, so conversion of gene IDs and functional enrichment analysis within R was easy. Now that I am working with a bacterial dataset for which there is no biomaRt support, I am a bit uncertain. After searching around, I decided to use the DAVID web interface. From there, I exported the results from the functional annotation clustering as .txt files (using all 3 GO term databases, i.e. BP, CC and MF). These have the following structure:

Annotation Cluster 1    Enrichment Score: 2.0813446807310627
Category    Term    Count   %   PValue  Genes   List Total  Pop Hits    Pop Total   Fold Enrichment Bonferroni  Benjamini   FDR
GOTERM_CC_DIRECT ...

Annotation Cluster 2    Enrichment Score: 0.028318244118032198
Category    Term    Count   %   PValue  Genes   List Total  Pop Hits    Pop Total   Fold Enrichment Bonferroni  Benjamini   FDR
GOTERM_BP_DIRECT ...

I would like to visualize these results within R. Is there a function for example using the clusterProfiler package that I am not aware of?

Or is this maybe not the best approach and there is a more suitable one?

I would really appreciate some help!

Answer 1

[damki]

Not sure of the best way to visualize your enrichment results from DAVID, but I have found that GSEA-pro (http://gseapro.molgenrug.nl/) to be a reliable tool for bacterial functional enrichments, as it seems to be developed to specifically use bacterial references including less commonly studied species. The tool generates tables and visualizations that can be downloaded and preserves the analysis on their webserver for some period of time, which allows the webserver results to be directly shared.

[mickey_95]

Cool, I hadn't heard of GSEA-pro, will give it a try. Thanks!!

Answer 2

[Guangchuang Yu]

if you have your own annotation, you can use the universal interface provide in clusterProfiler.

PS: for microbiome data, you can use https://github.com/YuLab-SMU/MicrobiomeProfiler.