Can Prodigal-GV make 'real world' difference compared to original Prodigal?

[GomathiNayagam]

I am wondering if Prodigal-GV, which incorporates alternative genetic codes tailored to viruses, could be a viable alternative to Prodigal in the Virify pipeline to enhance result accuracy. I am curious about the perspective of the Virify authors on this.

[mberacochea]

Hi @GomathiNayagam

Apologies it wook so long to answer. I think that is an interesting idea, @hoelzer what do you think?.

Cheers

[hoelzer]

Hey @GomathiNayagam and @mberacochea - also sorry for the lack of response: I'm going through my email and issues backlog.

Yes, indeed. This would be interesting; thanks for the suggestion!

https://github.com/apcamargo/prodigal-gv
https://anaconda.org/bioconda/prodigal-gv

Code is also on bioconda so it would be easy to implement and test.

However, a proper benchmark would be needed because there are also other gene annotation tools tailored for viruses/phages such as https://github.com/deprekate/PHANOTATE or Vgas (https://www.frontiersin.org/articles/10.3389/fmicb.2019.00184/full). In the Vgas paper, the authors found

When combining Vgas with GeneMarkS and Prodigal, better prediction results could be obtained than with each of the three individual programs, suggesting that collaborative prediction using several different software programs is an alternative for gene prediction.

and this is also my impression. Probably an ensemble method would be best instead of a single tool.

Here is another benchmark study: https://www.biorxiv.org/content/10.1101/2021.12.11.472104v1