Hi

I have a normalisation question. I am having some inconsistencies between mRNA abundance estimation from Deseq2 and StringTie-Ballgown. I get that that there are many differences between the two, but if you consider 1 gene that has 1 transcript, and use the same bam input file, the main difference between the 2 algorithms is the normalisation - correct?

Attached is the bamcoverage of such a gene. And below are the rpkm estimated by Deseq2 (gene level) and StringTie-ballgown (transcript level) - commands used are at the end of this post :

                                    AMP (blue track)         DLM (green track)
 fpkm by Ballgown                    40.6                        5.1
 fpkm by Deseq2                      21.3                        13.1

The fold change between the 2 conditions according to stringtie is much closer to what you see on the pile up. Is that because StringTie and bamCoverage use the same kind of normalisation algorithm? And if so, which is closer to the "biological truth", Deseq2 or StringTie/read Coverage?

Thanks!

Commands used: StringTie stringtie -e -B -G ${GTF} -o transcripts.gtf -A gene_abundances.tsv input.rmdup.bam

Deseq2 (using featureCounts counts) featureCounts -T $threads -p -F GTF -t exon -g gene_id -s 2 -a ${GTF} -o out.featurecount input.rmdup.bam FPKM values calculated in Deseq2 with: fpkmNormalisedCounts <- as.data.frame(fpkm(analysisObject, robust =TRUE))

Bigwig bamCoverage -b input.rmdup.bam --ignoreDuplicates --effectiveGenomeSize 142573017 --normalizeUsing RPKM --filterRNAstrand forward -of bigwig -o output.bw

The fpkm function in DESeq2 is using whatever gene length you provide. So it's not a question of StringTie vs DESeq2, but featureCounts vs StringTie. You can import StringTie data directly into DESeq2 using tximport (has support for type="stringtie"), which would be a 1-to-1 comparison.