prokka VS reCOGnizer

If you need more accurate ORF(CDS) prediction including functional annotation, I recommend using CLI tools such as prokka, bakta, or DFAST (DFAST is also available in a web version).

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

I just checked out Prokka, which uses Prodigal to predict genes. They do not seem to have any reservations for viruses. Thus, I recommend using Prokka directly, since that will conveniently add functional annotations.

If you do DNA sequencing and receive the sequencing files as fastq files (normal from sequencing) then spades to assemble the genome, then put it through PROKKA to annotate it. Here's a beginners guide, the most difficult part is downloading the programs onto your laptop.

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