I've got some doubts on the hisat2 --rna-strandness option and its output for downstream analysis. Is it expected to see a difference in alignment and counting results given the default usage of --rna-strandness in hisat2 followed by htseq-count -s reverse on a strand-specific assay?
Please see below.
I understand that the --rna-strandness option produces an XS tag to indicate where a transcript is from (on the + or - strand) for downstream transcriptome assembly analysis. I have a paired-end stranded sequencing library that was aligned to the genome using hisat2 without specifying the --rna-strandness (in other words, the default unstranded was the usage). Following this, the reads were assigned to genes using htseq-count and this time -s reverse was specified given the strand-specific sequencing assay type.
Since --rna-strandness is for transcriptome assembly using the XS tags generated and htseq does not use XS tags for counting, I presume there should be no practical impact from the above. Could you also shed light on this? in case I may have been overlooking other facts of the usages of the tools.
To help verify the above, I re-aligned and counted the reads from 2 samples by switching on --rna-strandness RF in hisat2. I attach the alignment and count features info. below for assessment.
Overall alignment rate of Sample 1:
94.52% (--rna-strandness RF)
94.12% (--rna-strandness unstranded)

Overall alignment rate of Sample 2:
94.57% (--rna-strandness RF)
94.15% (--rna-strandness unstranded)

Feature counts of Sample 1 (following --rna-strandness RF + -s reverse):
__no_feature        6327294
__ambiguous     2954776
__too_low_aQual     3784481
__not_aligned       688856
__alignment_not_unique      4858182

Feature counts of Sample 1 (following --rna-strandness unstranded + -s reverse):
__no_feature        6291151
__ambiguous     2911298
__too_low_aQual     4075017
__not_aligned       754400
__alignment_not_unique      16136045

Feature counts of Sample 2 (following --rna-strandness RF + -s reverse):
__no_feature        5417882
__ambiguous     1708510
__too_low_aQual     3532352
__not_aligned       564596
__alignment_not_unique      2859501

Feature counts of Sample 2 (following --rna-strandness unstranded + -s reverse):
__no_feature        5359434
__ambiguous     1676091
__too_low_aQual     3813344
__not_aligned       623122
__alignment_not_unique      2891792

These results look comparable to me across pipelines.
Thanks
Guan

That is the correct sequence for 2019-nCov. Coronavirus is of course an RNA virus and in fact, to my knowledge, every RNA virus in Genbank is present as cDNA (AGCT, i.e. thydmine) and not RNA (AGCU, i.e. uracil).

The reason is simple, we never sequence directly from RNA because RNA is too unstable and easily degraded by RNase. Instead the genome is reverse transcribed, either by targeted reverse transcription or random amplification and thus converted to cDNA. cDNA is stable and is essentially reverse transcribed RNA.

The cDNA is either sequenced directly or further amplified by PCR and then sequenced. Hence the sequence we observe is the cDNA rather than RNA, thus we observe thymine rather than uracil and that is how it is reported. 
