Hi all,

I'm dealing with an unusual case of DGE, where I'm interested in quantifying reads that come from particular regions at transcript ends instead of the whole transcript. I'm screening standard Illumina RNA-Seq reads for these regions and quantify these separately from all other reads (background reads). I'm then doing a gene-based DGE analysis, but am wondering whether this analysis might be affected by differences in isoform usage between treatment groups.

What if one treatment group uses longer isoforms than the other group? In standard DGE, transcript length matters because longer transcripts produce more reads, but I can't quite get my head around whether transcript length affects the number of reads I get at transcript ends. My intuition is that the number of reads at a transcript end depends only on the length of the nucleotide sequence I'm screening for (say, 30bp); if one treatment group now switches to longer isoforms I will see more background reads, but the same number of reads at the first 30bp. This means I don’t need length correction for my counts. Is this correct or should I be doing transcript-level expression analysis accounting for transcript length?

Thank you very much, Marius

You need no correction for end-tagged data, see the section in tximport:

https://bioconductor.org/packages/release/bioc/vignettes/tximport/inst/doc/tximport.html#3%E2%80%99_tagged_RNA-seq

If you have 3’ tagged RNA-seq data, then correcting the counts for gene length will induce a bias in your analysis, because the counts do not have length bias. Instead of using the default full-transcript-length pipeline, we recommend to use the original counts, e.g. txi$counts as a counts matrix, e.g. providing to DESeqDataSetFromMatrix or to the edgeR or limma functions without calculating an offset and without using countsFromAbundance.