Quantifying antisense transcription from strand specific data

[blanco]

Hi all,
I have sequences from strand specific libraries made with the 'dUTP second strand marking' protocol.

I was wondering what would be the best way to quantify antisense transcription of the data.

So far, my best approach has been to use RPKM_count.py tool from the RSeQC package but I am not sure if that is the right way to go about it. I thought maybe this was somehow implemented in the tophat-cufflink pipeline.

Any feedback would be appreciated and if you need any further information on the libraries, mapping, etc., I will be happy to provide.

Thanks,
blanco

[dadada4ever]

I have the same question.
My opinion was using command to seperate the tophat mapping results by the tag "XS:A:" to generate two individual bam file which contains positive strand and negative strand mapping results separately. Then use the cufflinks to generate the FPKM. But IDK if it's correct or not.
And I'm curious about how did others refine the annotations or find new antisense transcripts using RNA-Seq data. As far as I know there's only method of finding new antisense transcripts in bacteria or fungi.
Hope somebody could share some brilliant methods to handle with these questions.

[frozenlyse]

Tophat/cufflinks can be run in strand aware mode (--library-type option) so if you use it to discover new transcriptional units it should find antisense transcripts (if there are enough reads!)