Question about exon/intron counting in RNA-seq dataset analysis

Question

[nanoide]

Hi, So I'm currently analyzing some RNA-seq datasets and wanted to get expression at the gene level. So I got raw counts with featureCounts and then got normalized counts using DESeq2.

My question is, I have huge genes with very high introns. I'm afraid the normalization is taking into account the whole gene length, so expression in exons of genes with huge introns would be underrated. I believe DESeq-2 normalizes depending on the total on reads but I don't know if the fact that I have huge introns might be confounding in oter parts of the analysis. Maybe I can perform the analyses at the exon level and then somehow combine the counts per gene? Don't know if that's crazy

Any thoughts? Thanks for any help

Answer 1

[caggtaagtat]

I think you can use featureCount to counts reads on exons and then summarize data of all exons of one gene. So intron lengths should not be a problem.

Edit: as long as there are exons annotated in your gtf file

Each entry in the provided annotation file is taken as a feature (e.g. an exon). A meta-feature is the aggregation of a set of features (e.g. a gene). The featureCounts program uses the gene_id attribute available in the GTF format annotation (or the GeneID column in the SAF format annotation) to group features into meta-features, ie. features belonging to the same meta-feature have the same gene identifier.

[nanoide]

Thank you for your useful answer! Will take it into account. Regards

Answer 2

[swbarnes2]

DESeq knows nothing about your features except the names. How could it? You never give it that information. If the gtf you gave to FeatureCounts had proper exon and intron information, you are fine.