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
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.
they weren't expecting to receive attention out of the blue today. it seems rude to attack someone's engineering skills because an online profile is out of date.
I work with genomics pipelines in my day job.
This repo does not seem quite ready for serious usage until a comparison is made with existing tools such as Bowtie 2/samtools/Strelka or similar.
For cancer genomes, it's also a bit limiting that it does not call structural variants instead of just SNVs/indels.
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.
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.
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.
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.
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.
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].
I shared this since it seems to address a somewhat similar niche that I have had hopes to one day develop, based on FlowBase [1]; A library of streaming processing components based on basic operations, that can be easily stitched together into larger pipelines in a compiled language that can run on smaller hardware too.
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.
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.
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.
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.
> 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.
> So it seems "Rosalind" is at least very appropriate as a name for a genomics tool such as this.
Indeed. The only argument against it might be that Rosalind is already a pretty well-known website for doing bioinformatics exercises and have them automatically graded:
Awesome piece of software! Quick side question... does anyone have a recommendation for a DNA genotyping service that prioritizes privacy? I'm looking for a company that provides private results and doesn't add them to any sort of database (dystopian or otherwise). I'd love to get my DNA profile, but I'm concerned about privacy issues. :\
I guess the author refers to the fact that many well-known tools have some randomness built-in. The most obvious one is differences due to the order of parallel processing. But these differences are often small and have no significant downstream effects. They are mostly inconvenient for regression testing.
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
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
they weren't expecting to receive attention out of the blue today. it seems rude to attack someone's engineering skills because an online profile is out of date.
It's not out of date, it's pointing at the wrong person.
this isn't an attack. this is a data point that they don't review the slop coming out of the LLLM
I work with genomics pipelines in my day job. This repo does not seem quite ready for serious usage until a comparison is made with existing tools such as Bowtie 2/samtools/Strelka or similar. For cancer genomes, it's also a bit limiting that it does not call structural variants instead of just SNVs/indels.
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.
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.
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
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.
We rewrote Nextclade in Rust and are very happy. Works nicely both for CLI and client side browser with wasm.
https://github.com/nextstrain/nextclade
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.
[1] https://github.com/lazear/sage
Yeah, there is actually a pretty big shift towards Rust in the comp bio / bioinformatics community.
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...
[6] https://github.com/rust-seq/simd-minimizers
I shared this since it seems to address a somewhat similar niche that I have had hopes to one day develop, based on FlowBase [1]; A library of streaming processing components based on basic operations, that can be easily stitched together into larger pipelines in a compiled language that can run on smaller hardware too.
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.
[1] https://github.com/flowbase/flowbase
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
Have you tested with other similar softwares such as Blast, which is the most common?
Rust is a great fit for genomics. Processing whole genomes locally on a laptop is a huge step up from typical Python pipelines.
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.
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
Oh wow you weren't joking.
bioinformaticians have been making these useless bioinformatic-toolkit-in-my-favorite-programming-language repos for years
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.
Why don't you mention Biopython? Bioperl is already too old and not much up-to-date with newest data.
He’s talking about the past (“For a while, …”). Up to early 2010s, I would say.
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.
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
[1] https://en.wikipedia.org/wiki/Rosalind_Franklin
> I'm not familiar with Margaret Oakley Dayhoff
Then you’re one of today’s lucky 10,000. Any time!
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.
> 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.
> So it seems "Rosalind" is at least very appropriate as a name for a genomics tool such as this.
Indeed. The only argument against it might be that Rosalind is already a pretty well-known website for doing bioinformatics exercises and have them automatically graded:
https://rosalind.info
Awesome piece of software! Quick side question... does anyone have a recommendation for a DNA genotyping service that prioritizes privacy? I'm looking for a company that provides private results and doesn't add them to any sort of database (dystopian or otherwise). I'd love to get my DNA profile, but I'm concerned about privacy issues. :\
You might try sequencing your DNA at home :)
https://iwantosequencemygenomeathome.com/
(Well, the guy offers to do it for you too, delivering the data on an USB stick).
> 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
https://www.cs.cmu.edu/~ckingsf/software/sailfish/
https://www.nature.com/articles/nmeth.4197
I guess the author refers to the fact that many well-known tools have some randomness built-in. The most obvious one is differences due to the order of parallel processing. But these differences are often small and have no significant downstream effects. They are mostly inconvenient for regression testing.
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?
blatant copyright infringement of https://rosalind.info/problems/locations/