Hi, I would like to ask a question or opinion, or suggestion, so currently I am doing featureCounts with my bulk RNAseq samples from PBMC samples. However, the percentages that gave me of "Successfully assigned alignments" were low, I experimented with all possibilities in my code: reverse strand (in general, 30-33% successfully assigned), forward strand (3-7%), and unstranded (27-30 %).
I checked my STAR alignment, and it seemed they aligned quite well (75-85%), and then I checked my GTF file, for me it seems fine, as in alignment I used GRCh38.p14, and for feature counts I used gencode.v48.basic.annotation.gtf
I already played around with Chat-GPT, but the answer was hallucinating. So, do you think these percentages of my FeatureCounts are still acceptable since this is from PBMC
thank you for your time
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Not sure what "since this is PBMC" means. There is no reason PBMCs should give low assignments. It might be rRNA or genomic DNA contamination. The important part is how many total reads are assigne to genes and if this is sufficient for downstream analysis. Consider sqeuencing deeper if depth is an issue regardless of the percentage.
thank you for the reply ATpoint, I assumed in PBMC samples such rRNA or genomic DNA contamination are common, that is why I emphasized since this is PBMC data, and after checking and believe this is enough number of the total reads that are assigned to genes, I consider to proceed it further. Thanks!
I don't see any reason why PBMCs should enrich for this. Poor quality can generally be more common in certain samples that are rich in RNases, such as neutrophils, but PBMCs in general I would label as easy to work with since these can be directly lysed without mechanical or enzymatic dissocation, so the turnaround is much faster, hence the time for RNA degradation is shorter.