AI coding assistants have made it possible for domain experts to rewrite established scientific software in days. We believe that a wave of AI-driven tool rewrites is coming to bioinformatics. We've published a set of best-practices principles to help people to approach rewrites in the right way. Along the way we fully rewrote and open-sourced the genomics QC tools for RNAseq, the most widely used genomics pipeline, yielding a >60x performance improvement.
The resource I recommend to people looking to move from wet lab to dry lab stuff is https://www.biostarhandbook.com/. From your post history it looks like you already have some programming experience, so you could skip the first few chapters which are just a linux intro. I don't think it has all the best practices, but I think it's the most comprehensive overview that starts from square 1 and fills in all the gaps no one tells you when you first start, for example the "Common data types" chapter.