featureCounts parameters
Hi,
I was wondering whether you would recommend adjusting the following featureCounts parameters and if so, why?
--fracOverlap, --fracOverlapFeature, --minOverlap, --maxMOp
I am trying to decide which parameters to go for. I have high quality bulk RNA-seq data (75 bp or 150 bp paired-end reads) from humans and I have mapped the data with STAR. Please let me know if you require any other information.
Best wishes,
Lucy
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Are you doing a Differential Expression analysis? If so, are you interested in a Gene Differential Expression or Transcript Differential Expression?
Please use comments to reply, instead of answers. Other info:
Hi Shred,
I haven't actually seen any convincing evidence that Kallisto and Salmon are more accurate than tools like featureCounts. Please supply the references.
My read lengths are 75 bp or 150 bp paired-end (and I have done a small amount of trimming). I will run featureCounts twice, once excluding multi-mapping reads and once counting them fractionally.
Best wishes,
Lucy
Salmon is better suited for transcript-level redistribution of reads. Featurecounts was not designed to distinguish between transcripts.
Note how Salmon is not a replacement of Featurecounts but a replacement of an entire two-step process: alignment+counting
When it comes to finding coverages at the gene level, the differences between the classification methods of Salmon vs the alignments +counting are probably less pronounced. Though running Salmon is probably much faster and simpler.
The main downside of Salmon is that you are not getting a genome level alignment that you could visualize, thus I always recommend doing both types of analyses and comparing the results.
Salmon could be used to quantify .bam files aligned against the transcriptome fasta file, which, for the purpose of a DE analysis with human RNAseq data, may be enough.
Here you go https://genomebiology.biomedcentral.com/articles/10.1186/s13059-018-1419-z
Yes I will be doing gene level differential expression analysis.