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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. [1] https://github.com/stjude-rust-labs
Hey 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 ed…
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
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. [1] https://github.com/stjude-rust-labs
Thanks for the shout out!
I'm very bullish on the long-term prospects of Rust in computational biology—as well as research computing more generally.
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.
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.
> 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
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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.
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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. [0] https://en.wikipedia.org/wiki/Photo_51
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.
the type of diffraction her lab was doing only makes sense on helical structures. it being helical was already kind of? established -- linus pauling was contemporaneously working on some sort of alpha-helix inspired single helix model.
watson and crick immediately recognized the position of the diffraction spots fit the distances suggested by their chemical modeling of a, t, c, g, which franklin was not able to do since she hadn't made a structural prediction.
> postulated ahead based on partial data
not quite. if you know that a t c and g are the raw chemicals made, you can make a (possibly even literal) model and say, "this ball and stick model predicts diffractions here".
this is arguably better science than waiting for data and fitting a model to the data, falsifiability and all that.
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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.
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O(√t) looks weird but it's real. the "naive trial division" primality test for example.
It doesn’t apply to what this repo is doing. Also the 70 odd single author preprints seems to suggest the author is in some deep AI psychosis: https://www.researchgate.net/profile/Logan-Nye-2