Greetings @AMNiewiadomska! Thanks very much for sharing this tremendous resource, VIGOR4. I too tried it on Wuhan-Hu-1 and was delighted by its ease of use, in general — and its accurate identification of ORF3b, in particular! I’m excited to employ this in the future.
I agree with @ZacharyArdern about the language regarding expression (but, of course, I’m biased!). Taking ORF3d/-2 as an example, even beyond the ribosome profiling data of Finkel et al. which we re-analyzed in the eLife paper, I personally feel that the presence of a strong antibody response in convalescent sera documented by Hachim et al. (where it is called ORF3b) constitutes at least some degree of evidence for expression.
For similar reasons, I’d also vote for inclusion of all proposed non-canonical genes in your software. It could certainly be noted where evidence is dubious, but in my view it would be maximally beneficial to use your tool without needing to subsequently find any ORFs manually (e.g., ORF2b). The user, who should presumably have some familiarity with SARS-CoV-2 genes, could then choose which if any to exclude.
One issue I’m sure you’ve encountered is that, even with such a great resource as this, many (most?) researchers understandably still rely on the gene annotations of the most-used reference genome, Wuhan-Hu-1 (NC_045512), without further investigation of gene repertoire. As one example, I recently reviewed a study which failed to consider ORF9b, with the understandable justification that it is not annotated in the aforementioned reference genome. This then led to interpreting certain evidence relevant to ORF9b as instead pertaining to the more dubious ORF10, simply because the latter is annotated. Hopefully we can inch toward the best of both worlds, with people adopting resources like yours, but also with the primary reference genome being updated!
Thanks a ton!
Chase