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logannyeMD 9 hours ago [-]
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 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
croemer 9 hours ago [-]
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.
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.
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.
clmcleod 10 hours ago [-]
Thanks for the shout out!
p4ul 6 hours ago [-]
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.
mriet 5 hours ago [-]
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.
a_bonobo 6 hours ago [-]
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.
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.
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.
vatsachak 10 hours ago [-]
Looking at the commenting pattern, it seems like AI unfortunately
jghn 10 hours ago [-]
The OP? They're not AI, they've been active on X and bsky for years.
vatsachak 10 hours ago [-]
Sorry, I meant the code in the repo
semiinfinitely 11 hours ago [-]
bioinformaticians have been making these useless bioinformatic-toolkit-in-my-favorite-programming-language repos for years
maxall4 11 hours ago [-]
Well, what else are we going to do while waiting for the bench scientists to finish collecting data?
asdff 9 hours ago [-]
Dissertationware is common in a lot of fields, honestly.
gilleain 11 hours ago [-]
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.
boron1006 11 hours ago [-]
Lots of bad smells in this repo.
the__alchemist 11 hours ago [-]
Do you have some examples to look at? I am curious.
boron1006 10 hours ago [-]
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.
aeve890 5 hours ago [-]
O(√t) looks weird but it's real. the "naive trial division" primality test for example.
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.
dnautics 4 hours ago [-]
> Franklin was pretty clear on the helical structure
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.
flobosg 12 hours ago [-]
> I'm not familiar with Margaret Oakley Dayhoff
Then you’re one of today’s lucky 10,000. Any time!
bonsai_spool 11 hours ago [-]
Didn't see a publication or preprint for this - is there one?
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
[1] https://github.com/stjude-rust-labs
[1] https://github.com/lazear/sage
https://github.com/nextstrain/nextclade
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.
I'm very bullish on the long-term prospects of Rust in computational biology—as well as research computing more generally.
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.
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.
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.
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.
Uhh... are there stochastic genomics pipelines?
https://www.cs.cmu.edu/~ckingsf/software/sailfish/
https://www.nature.com/articles/nmeth.4197
Not to say the other names mentioned aren't also deserving of similar honors
[1] https://en.wikipedia.org/wiki/Rosalind_Franklin
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
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.
Then you’re one of today’s lucky 10,000. Any time!