Visualization of single transcripts with a-site/p-site coverage

[AlexFryd]

Hello!

First of all thank you for developing Ribo-TISH! I have found it really useful for the QC it provides and also for uORF prediction.

I have a question that I am not sure that it is relevant to Ribo-TISH itself.

In the published paper there are several snapshots of single transcripts (e.g. Fig.3a,3b and Fig.4a,4b) providing p-site (or a-site?) Ribo-seq or QTI-seq coverage. I would like to ask if Ribo-TISH has the ability to provide such plots.

If not, may I know how do you manage to end up with the .bam/.bed file containing only the counts per p-site or a-site so that can be easily visualized on a genome browser.

Thanks in advance!

Kind Regards,
Alex

[zhpn1024]

We have raw script for ploting P-site profiles in transcript, but not ready for release.
The --transprofile option in predict module export P-site counts in transcripts. You may use it to plot.

[AlexFryd]

Hello Zhang,

Ok I will give that a try! Thanks a lot!

Kind Regards,
Alex

[AlexFryd]

Hello Zhang,

I am running the following command: (regular Ribo-seq data)

ribotish predict -b path/to/bam/.bam -g path/to/gtf/.gtf -f path/to/fasta/genome.fa --transprofile PSITE_TRANSCRIPTS -o ribo_ORFs.txt
as a really basic version of the offered predict function.

Firstly, when it comes to predictions, I am getting no values for TIScounts, TISGroup, TISPvalues, FisherPvalue and TISQvalue.

Secondly, I am getting a file for the --transprofile, which I am not absolutely sure how to use to plot. Is it something I can use to a genome browser?

Thank you in advance!

Kind Regards,
Alex

[zhpn1024]

Firstly, you do not provide TISeq data by '-t' option, so there's no TIS related results or merged Fisher. In this case, Ribo-TISH can guess a TIS for each ORF, with the strategy of '--longest' or '--framebest'.

Secondly, the output of '--transprofile' provides five columns, gene_id, transcript_id, gene_symbol, TISeq_counts and RiboSeq_counts. TISeq_counts and RiboSeq_counts are in python dict format recording counts in different positions. The positions are in transcript coordinate, not genome coordinate.