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.
Really interesting direction. The validation and drop-in compatibility part is what caught my attention most. If this holds up across more datasets, this could remove a huge amount of RNA-seq QC runtime pain.
So needed. I just threw genebody_coverage and TIN out of RSeQC ;) Maybe they can come back now.
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.
Really interesting direction. The validation and drop-in compatibility part is what caught my attention most. If this holds up across more datasets, this could remove a huge amount of RNA-seq QC runtime pain.