Rosalind: A genomics toolkit in Rust running whole-genome pipelines on a laptop
github.comHey guys, this is my github repo. Glad it's received some interest - I figured HN might be the culprit when it suddenly jumped ~100 stars despite not working on the code base since last year. I prototyped this out of personal curiosity last year and moved on abruptly so there's a lot of gaps I still need to close and knobs that need to be optimized. But if people genuinely find "deterministic genomics workloads on edge devices" proposal useful, I'll begin refining the code tonight and try to make it as useful as possible. If you have any particular bioinformatics tasks or use cases that you want to be feasible on edge devices, lmk and I'll work on integrating new capabilities. Always happy to be helpful
Your website bio and LinkedIn don't match at all. Is the LinkedIn link on your website wrong? Update: yes it is. This is the correct one: https://www.linkedin.com/in/logan-nye
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
oh wow lol never seen that one before
Realistically, without data from a large testset that compares this thoroughly to Samtools (and others?), I wouldn't touch this.
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.
There has been a bit of a 'trend' to rewrite common bioinformatics/comp-bio into faster languages (Rust) via LLMs, OP's repo seems to be an early example.
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.
This is interesting; thanks for sharing! I have been curious about the adoption of Rust in computational biology. I know that the folks at Saint Jude's [1] are also using Rust for their 'omics research.
There is a relatively widely adopted tool (100+ citations, >500k invocations collected via telemetry) for mass spectrometry-based proteomics written in Rust, and quite a few others in the works.
We rewrote Nextclade in Rust and are very happy. Works nicely both for CLI and client side browser with wasm.
I'm building a structural bio crate system in rust (na_seq, bio_files, bio_apis, dynamics and some more specialized). No one is using it AFAIK other than myself. I am using it to build a GUI multi-purpose structural bio GUI program called Molchanica.
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.
Thanks for the shout out!
Oh, thank you, @clmcleod! We've been following all your work closely in my team!
I'm very bullish on the long-term prospects of Rust in computational biology—as well as research computing more generally.
Those are all the tests for alignment. They don't even check the alignment is correct. Just that there are no errors. This is a joke: https://github.com/logannye/rosalind/blob/main/tests/alignme...
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.
Looking at the commenting pattern, it seems like AI unfortunately
The OP? They're not AI, they've been active on X and bsky for years.
Sorry, I meant the code in the repo
Lots of bad smells in this repo.
Do you have some examples to look at? I am curious.
Well the √t stuff looks like nonsense or way overblown, existing tools already do similar things, there’s pretty much a single commit with no follow up commits etc etc.
O(√t) looks weird but it's real. the "naive trial division" primality test for example.
bioinformaticians have been making these useless bioinformatic-toolkit-in-my-favorite-programming-language repos for years
Well, what else are we going to do while waiting for the bench scientists to finish collecting data?
Dissertationware is common in a lot of fields, honestly.
Hate to agree, but it is true. For a while, I think, the main sequencing framework was in perl (Bioperl). Not sure what was best for structures - possibly Biojava?
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.
> A deterministic genomics engine with a compact memory footprint.
Uhh... are there stochastic genomics pipelines?
A quick search gave me for example this one: https://genome.cshlp.org/content/26/1/36
i think these are more relevant examples
I would love to hear about what the sacrifices are, but this project really looks amazing.
Didn't see a publication or preprint for this - is there one?
Should have called it Raymond.
Or rather Margaret: https://en.wikipedia.org/wiki/Margaret_Oakley_Dayhoff
I'm not familiar with Margaret Oakley Dayhoff, but I am aware that Rosalind Franklin [1] was extremely important for our understanding of DNA, comparable to Watson/Crick, with whom she co-discovered the structure of DNA. So it seems "Rosalind" is at least very appropriate as a name for a genomics tool such as this.
Not to say the other names mentioned aren't also deserving of similar honors
Rosalind Franklin was the team lead of the research team that photographed DNA.
The actual team member that took the key photo[0] was Raymond Gosling.
That team didn't interpret the double helix structure of DNA that the photograph had captured - that was Watson and Crick working it out from the photograph.
It's not quite that clear-cut. Franklin was pretty clear on the helical structure in both research notes and papers, but she didn't quite nail the overall structure (2 strands with opposing winding, complementing bases).
Fundamentally, she suffered the curse of the experimental scientist - waiting for actual data before being willing to build a model. Watson & Crick postulated ahead based on partial data.
> I'm not familiar with Margaret Oakley Dayhoff
Then you’re one of today’s lucky 10,000. Any time!