why is featureCounts under RNAseq analysis?

I just wondered why featureCounts is listed under RNAseq analysis, I thought it has been superseded. Maybe I’m wrong. Can someone explain? Thanks

Hi @mwoods

Tools in the left tool panel are organized into “sections” related to analysis domains, functionality, common operation on specific datatypes, and/or by “tool suites”. That is just a default organization – and there are ways to customize.

The tool featureCounts Measure gene expression in RNA-Seq experiments from SAM or BAM files is nested under the section “RNA-seq” since that is the primary analysis domain the tool is used for.

Some tips:

  • Overview in the release notes from last September related to the latest “tool panel views” updates: September 2021 Galaxy Release (v 21.09) — Galaxy Project 22.01.1.dev0 documentation

  • Try using the tool search at the top of the panel to find tools. Here you can use keywords to limit the results, and toggle the display to include the section labels or not.

  • You can also organize the list of tool by EDAM Ontologies, or your own list of favorites. Once you load up a tool, you can click on the star in the upper right corner of the tool form to “add to your own personal favorites” then restrict the tool panel view just your own favorites.

  • Try hovering over the “star” and “list” icons to see how each is labeled, then try those functions out to learn how they work and to discover if any are helpful for you. :slight_smile:

If this doesn’t address your question, would you please you explain with a bit more detail? I’m not sure what “I thought it has been superseded” refers to.


Hi @jennaj thanks so much for the explanation on how to use galaxy, I think it’s great & I’ll try to use it.

By superseded I meant that ten years ago, a paper was published that took RNA from HOXA1-depleted fibroblasts and cells treated with a pool of scrambled siRNAs & hybridized that RNA on gene expression arrays & also performed RNA sequencing with it.

Trapnell, C., Hendrickson, D., Sauvageau, M. et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol 31, 46–53 (2013). Differential analysis of gene regulation at transcript resolution with RNA-seq | Nature Biotechnology

Next they compared the fold change of a subset of genes between the micro array & RNAseq by plotting the fold change computed by RNAseq against the fold change of the same gene when analyzed by micro array.

They made this comparison using two different bioinformatics tools, one that counts reads, like feature counts & another that uses a likelihood function & mathematical model to infer the expression, a model called isoform deconvolution. They found isoform deconvolution had greater accuracy because the RNAseq fold change had greater concordance with micro array fold change.

Because differential gene expression analysis is used to guide experiments and select hypotheses to test, it is logical to choose the tool with best accuracy. There are other papers that have also made similar conclusions to trapnell et al,

Would you be able to provide a note at the introduction of feature counts that explains whilst it is a fast tool to compute gene expression, there are alternative methods with greater precision?

Thank you

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