Nature even wrote a feature article about it a couple years ago:
Why scientists are turning to Rust
https://www.nature.com/articles/d41586-020-03382-2
They mention the Rust-Bio [1] project by well known Snakemake author Johannes Köster & co, and there are some other widely used libraries like needletail [2] and noodles [3].
A cool smaller tool developed by performance wiz Ragnar Groot Koerkamp which was just published is Sassy [4] [5]. He has also been involved in developing some high performance SIMD based stuff (minimizers) [6].
[1] https://github.com/rust-bio/rust-bio
[2] https://github.com/onecodex/needletail
[3] https://github.com/zaeleus/noodles
[4] https://github.com/RagnarGrootKoerkamp/sassy
[5] https://academic.oup.com/bioinformatics/article/42/5/btag244...
Note that this doesn't have much overlap with the traditional bioinformatics workflows like the OP (Rosland), or the one you linked to seem to be focused on.
It is very tempting, though - 'just' make a nice, clean API in your favourite language (eg Haskell, Ruby, ...) and everyone will flock to use it! Maybe.
https://iwantosequencemygenomeathome.com/
(Well, the guy offers to do it for you too, delivering the data on an USB stick).
You're doing too much vibe coding and not enough checking/testing.
LinkedIn link on your website points to: https://linkedin.com/in/logannye
Website bio: https://www.logannye.io/about
Uhh... are there stochastic genomics pipelines?
Seqera Labs has a bit of a manifesto: https://rewrites.bio/
Heng Li has an overview here too: https://lh3.github.io/2026/04/17/the-ai-rewrite-dilemma
IMHO it's... OK? Bioinformatics code quality is generally poor, untrained biologists writing functioning code that is poor in scoping, but works. (Unguided) LLMs write on that level, too, so not much harm done.
In defense of a lot of these bioinformatics-specific rewrites, there are some really dodgy coding practices and bugs that exist in well used tools, so there is scope for genuine improvement. The most recent release of minimap2 fixed some bugs identified in a rewrite, for example: https://github.com/lh3/minimap2/releases/tag/v2.31
Not to say the other names mentioned aren't also deserving of similar honors
Note to the OP: specify a focus please? short, long, mega-long read and bacterial, human, small plant or large plant genome? Alignment heuristics and performance differ significantly across those axes.
Looks like total slop to me. All code in one commit, then a bunch of commits polishing the Readme.
No release, no updates in half a year.
FlowBase or I didn't have much of ideas about how to keep data structures compact, as the linked library does, and I was mostly aiming to make it really easy to build streaming pipelines.
I haven't yet got my head around how the composability story is in rosalind though, so would be interested in any pointers or examples on how this would be done using it.
