Hi,

I searched the previous posts and the nanopore community forum but couldn't find a definitive answer so here I am looking for your advice. I know working with long-reads RNA-seq data is still complicated because it's hard to find a general consensus on how to analyse them but I'd be interested in having your opinion.

I'm interested in measuring the number of genes detected in various RNA-seq libraries that I have generated with various nanopore library kits (DNA ligation of RT-PCR products, direct-cDNA and direct-RNA).

So far I've been using **HTseq** with the mode `intersection-nonempty` but it seems that now the `union` mode is the most recommended one. And i've also seen posts stating that **featureCounts** is much better and there's even an option for `longreads`.

I tried to compare the output of the three 'possibilities' but I cannot say it really helped me in the end:

- HTseq + intersection-nonempty mode: 238K assigned + 0 ambiguous + 18K no_features + 24K too low quality

- HTseq + union mode: 210K assigned + 44K ambiguous + 2K no_features + 24K too low quality

- featureCounts + long-reads mode:  219K assigned + 59K ambiguous + 2K no_features + 0 too low quality

Based on those observations, would you be able to recommend me to use one versus the two others ? And if yes, why ?
I'm trying to understand what makes one more suited to my need and/or generally more suited to the analyse of long reads RNA-seq data.

Thanks a lot.
Having thinking a little more about the problem, I think you may try Salmon with its alignment-based mode of quantification. The expectation-maximization algorithm will probably do a better job at sorting ambiguous reads than HTSeq / featureCounts. Be sure to read the manual: https://salmon.readthedocs.io/en/latest/salmon.html