Hello everyone,

I am a novice in Bioinformatics and just trying to understand analyzing the RNASeq data that we have generated. To give an overview, we have a wild type and knockout strain of parasites that we wish to compare at transcript level. We have got the illumina seq done and have the results with us. We ran the files on nfcore and got the salmon merged files. Of these we used the transcript counts file and ran in on R using DESeq2. Now I have a list of differentially regulated files. I wanted to know, if this is the accepted workflow and if not what changes should we make. And if possible, please provide the explanation in simple terms/ non-bioinformatics terms. (Its my first time with Bioinformatics analysis and I am having a tough time).

Thank you in advance.

Best wishes,

Sindhuri

You need to account for the inferential uncertainty of mapping, that is because transcripts of the same gene shared most exonic content, so for many reads it is hard to tell which transcript the read is truely originating from. Salmon can give such uncertainty estimates by bootstrap or gibbs sampling. And then you need a DE tool which can use this, see Considerations behind the choice of RNAseq Differential Expression Analysis Tools and especially What type of counts data to import for performing Isoform analysis in edgeR