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.
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 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
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.
How well tested would you say these libraries are? It doesn't sound promising, sadly. If there are comprehensive test suites, that would go a long way to ensuring new, faster tools arent producing subtly wrong answers. That's a pretty big deal, just because the code compiles or there is no exception thrown doesnt mean the analysis was correct.
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.
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.
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.
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. :\",
25 comments
[ 3.7 ms ] story [ 44.6 ms ] thread[1] https://github.com/stjude-rust-labs
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
Uhh... are there stochastic genomics pipelines?
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.
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.
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