mantis VS reCOGnizer

You could use https://github.com/PedroMTQ/mantis

https://github.com/PedroMTQ/mantis Mantis works on protein level but it does address the consensus matter you mentioned

Hello, I'm the developer of Mantis (https://github.com/PedroMTQ/mantis). Mantis doesn't use a database for COGs specifically but it does output some of the IDs you mentioned (e.g., KOs, COGs). If this is important for your work I could consider creating a COG centric database (or at least format it to be natively compatible with Mantis). Anyhow, please check the GitHub page and message me or post an issue and I'll try to help out.

Thanks a lot! Will definitely try it out. Another question: Does Mantis+eggNOG annotate the genes with eggNOG-ortholog-ids? More generally, what are the resulting annotation types? (pfam, GO, KO, EC and description are listed, how about KR, BiGG, brite and cazy?)

You could accomplish this by gene calling with prodigal and then annotating with a tools like Mantis https://github.com/PedroMTQ/mantis

reCOGnizer (https://github.com/iquasere/reCOGnizer) will do that for you. It also outputs the COG categories in a krona plot

What tool are you using for analyzing them? Using something like reCOGnizer (https://github.com/iquasere/reCOGnizer) gives you the bitscores and evalues in a TSV table, next to the matches

Finally, a huge help on developing is having a Continuous Integration/Deployment on the tool. CD refers to the conda suggestion above, as your tool would be readily available in a controlled environment. CI could use the GitHub Actions functionality provided by GitHub. You can see an example here

You may run MOSCA (https://github.com/iquasere/MOSCA), it performs all major steps of metagenomics analysis. It includes that functional classification you are looking for, since with UPIMAPI (https://github.com/iquasere/UPIMAPI) it annotates with UniProt DB as reference, and obtains information including taxonomy, EC numbers, and even those GOs, and reCOGnizer (https://github.com/iquasere/reCOGnizer), which annotates with CDD DB as reference, and obtains orthologous groups information (COG, Pfam, etc).

Prediction of coding sequences takes as input the contigs you obtained, and gives you the translated genes. Besides annotating with the KEGG database, you may also want to annotate with more general purpose databases (e.g. UniProt), as these provide more taxonomies and functional information. MOSCA includes UPIMAPI (https://github.com/iquasere/UPIMAPI) and reCOGnizer (https://github.com/iquasere/reCOGnizer), which annotate genes with reference to UniProt and CDD databases using two different methods, providing complementary information. This is the same methodology used by widely popular tools such as eggNOG-mapper and Prokka, but these use other databases.

Install with mamba instead of conda, no more problems. Or use UPIMAPI (https://github.com/iquasere/UPIMAPI) together with reCOGnizer (https://github.com/iquasere/reCOGnizer), since these tools obtain better results when annotating proteins

I think reCOGnizer is a nice name

reCOGnizer (https://github.com/iquasere/reCOGnizer) can annotate with COGs, and the other databases available at CDD. It obtains all information concerning COGs description and categories, and outputs krona plots and TSV tables in formats easy to analyze. There is also mantis (https://github.com/PedroMTQ/mantis), prokka (https://github.com/tseemann/prokka) and DFAST (https://github.com/nigyta/dfast_core), the latter two work on contigs and the first two are for proteins