As elaborated in my previous post, volcano plots are a great way of visualising the differentially expressed genes (DEGs) that are most affected by a particular treatment (compared to untreated control). However, a limitation is that volcano plots do not directly annotate whether the most influential genes perform similar functions. Here, I introduce Genoppi, which is an open-source computational tool (R package source code: github.com/lagelab/Genoppi) that allows plotting of interactive volcano plots with the corresponding gene functions derived from HGNC, GO or MSigDB (see example figure on top). In addition, by assigning a bait protein of interest, the tool is able to identify the interacting partners that are significant on the volcano plot. The interaction partners are compiled from InWeb_InBioMap, iRefIndex, or BioPlex which includes data from >40000 scientific articles. If interested to find out if SNPs (single-nucleotide polymorphisms) could play a role in your dataset, you may also assign the GWAS study from the NHGRI-EBI GWAS catalog to the dataset.
There are several other functions that Genoppi can do, but not elaborated in this blog post. Interested users may visit the details within the publication or in the guide provided in the website. Overall, while Genoppi is likely to be most utilised by scientists interested in proteomics and interactome research, my experience suggests that Genoppi can be potentially applied more broadly to transcriptomics analyses as well.