Hello,
I have a list of enriched gene ontology (GO)- biological process terms. I am trying to avoid listing them as tables and looking for programs that summarizes the result as a nice publishable picture. So far, I find Revigo as the best program. It produces nice scatter plot as well as a tree view. Link: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0021800
I notice that some papers use programs like topGO, RamiGO and show the GO enrichment as a hierarchical tree structure. Is there any other programs that can summarize GO enrichment as a nice visually captivating picture?
I also found some nice visualization using gephi and bio4j.
http://ohnosequences.com/images/GoSlimBlog.svg
http://bio4j.com/blog/2011/11/cool-go-annotation-visualizations-with-gephi-bio4j/
Any other suggestions will be helpful.
Thanks
Diwan
At the risk of sounding negativist I will say that I find the visualization you list (as well as those in the paper) to be surprisingly uninformative. They initially look cool but then if I were to ask myself what is it that I have learned from them that I did not know before I am not sure what the answer is.
Please don't take that as a criticism, it is more of musing on so called cool visualizations. I ask the same when looking at circos plots.
Hi Istvan, Thanks for your comment.
When I have a gene set, I would like to see the major biological theme in the set. A table output from GO BP enrichment is usually lengthy and might not fit the main paper. When we list so many terms there is little take home message. If we just list the top n enriched terms also doesn't provide the complete picture. So I really like the Revigo treemap where similar terms are grouped together and their major theme is highlighted. The reader or audience can quickly get a general idea of the gene set as well as the related enriched GO terms. I am searching to see if there are more tools for similar purposes.
This is a nice and meaningful plot actually, forgot what I said, that refers more to those networks that don't seem to say much other than here is a network with lots of edges