Hi,

I have 149 bulk RNA-Seq samples (100 bp, paired-end, Illumina) which have come from sequencing in the form of fastq triplets, i.e. pairs of reads plus a third fastq which contains only UMIs.

My first question is - do I need to use the UMIs at all or just ignore them?

So far I've ignored them, and used this workflow (on just 10 samples to begin with):

1. FastQC on raw reads
2. Align to full ref human genome using STAR (in: fastq, out: BAMs, sortedByCoord)
3. Produce counts using featureCounts
4. MultiQC on results produced so far
5. Analyse using DESeq2

This works, but ignores the UMIs completely. Results from multiQC show, from STAR:

and from featureCounts:

(note the fairly large percentages of unassigned multimapping reads there).

Alternatively, I've tried to incorporate the UMIs with this changed workflow:

1. FastQC on raw reads
2. Align to full ref human genome using STAR (in: fastq, out: BAMs, sortedByCoord)
3. Add the UMIs from the fastq files to the BAMs produced by STAR, using fgbio’s AnnotateBamWithUmis

but I'm getting lost down a rabit-hole now, adding more and more steps to this pipeline just in order to satisfy various errors I'm getting from downstream tools, e.g.

1. fgbio SortBam
2. fgbio SetMateInformation
3. fgbio GroupReadsByUmi
4. fgbio CallMolecularConsensusReads
5. samtools rehead, to add SM tag to BAMs
6. fgbio FilterConsensusReads (results in vastly reduced BAM file sizes)

for the moment I've stopped here - maybe I can use these BAM files, but this workflow is starting to feel over-complicated and I don't have confidence it's the correct way to go.

So to summarise:

• Do I need to incorporate the UMIs at all?
• If so, could anybody suggest a workflow?

Many thanks, Steve