π Les #soumissions pour @jobim2026.bsky.social sont ouvertes jusqu'au 15/03/26.
π Soumission de travaux originaux, articles longs (+ PCI), activitΓ©s de plateformes et de service, posters et dΓ©monstrations
π Plus dβinfos sur : jobim2026.sfbi.fr
#JOBIM2026 #bioinfo #Strasbourg
ποΈβ‘ If you use gzip/gunzip a lot in your pipelines, switch to the faster"libdeflate" versions instead! They use modern CPU capabilities to achieve a 2-3x speedup.
libdeflate is in conda, and "libdeflate-gzip" and "libdeflate-gunzip" are drop-in replacements. #unix
github.com/ebiggers/lib...
"..based on a common wavefront design that can be adapted to support a variety of dynamic programming algorithms: local, global, and semi-global alignment of genomic and protein sequences with a variety of commonly used scoring schemes" from
@martinsteinegger.bsky.social andco
Inverted colored de Bruijn Graph for practical kmer sets storage https://www.biorxiv.org/content/10.64898/2025.12.08.692073v1
The 12th edition of the 2-days workshop βData Structures in Bioinformaticsβ (DSB) will take place in Venice (Italy) on February 18-19th, 2026: dsb-meeting.github.io/DSB2026/
1/9 Just out:
k-mer indexes are the backbone of fast search in genomic data, but many degrade under small k, subsampling, or high diversity.
With OndΕej SladkΓ½ and @pavelvesely.bsky.social we asked: can we build one that works efficiently for any k-mer set?
Sorry for the first figure, got a problem of background, here it is:
Coming up with a name was pretty hard, we had a lot of good candidates. We settled on SNooPy, which is a reference to SNPs and to the fact that the tool is 100% python. We thought about metaSNooPy but this went too far π . The github: github.com/RolandFaure/SNooPy
Tests show that:
1/ SNooPy has the best recall in our tests
2/ Using genomic long-read SNP callers does not (always) work well: most tools have very low recall, but DeepVariant perform much better than other tested methods
3/ The recall of all tools is still far from 100%
We propose a new statistical framework. The idea to distinguish artefacts from SNPs is to look at several loci simultaneously: artefacts will occur on random reads, while SNPs will occur systematically on the reads that come from the same strain.
Existing long-read SNP callers (DeepVariant, longshot...) have been developed for diploid genomes. Deep-learning methods are trained on [human] genomic data. Statistical methods contain assumption that do not hold for metagenomics.
Preprint out! Check out our new long-read metagenomic SNP-caller, SNooPy π. Work with Chris Quince. Thread π§΅
π www.biorxiv.org/content/10.6...
Preprint Alert!
We present new strategies to accelerate large-scale document comparison using MinHash-like sketches.
A thread:
π§΅6/ 6
Since MSRs sketches are sequence, they are super easy to use. I think they could be useful for many other problems, e.g. SNP calling, pangenome graphs, indexing, etc.
π§΅5/6
The sketching makes assembly extremely fast: a gut metagenome sample of 138Gbp of sequencing data was assembled in less that 2h and 10G RAM on 8 threads β‘. And thanks to MSRs, *highly similar strains are not collapsed*
π§΅4/6
Two key properties that make MSRs sketches really cool:
π They are alignable sequences: you can just feed them in existing assembler
π MSR sketches can *keep all the SNPs*, i.e. two highly similar sequences are (almost) always reduced to different sketches -> useful to separate similar strains
π§΅3/ 6
MSRs have been defined by @lblassel.bsky.social @rayanchikhi.bsky.social and @pashadag.bsky.social in pmc.ncbi.nlm.nih.gov/articles/PMC....
Take a sequence, a value of k, and stream all k-mers through a function that output either a base or the empty character, and you got your sketch
π§΅2/6
Conceptually, the assembler is on the same lines as metaMDBG:
1. sketching reads
2. assembly procedure on the sketches
3. reversing to base-space to obtain the final assembly
The main difference is the sketching scheme: we introduce *Mapping-friendly Sequence Reductions (MSR) sketching*
Our preprint on our new metagenomic HiFi assembler Alice is out π₯³ Based on a *new sketching method* (π§΅1/6)
π Preprint www.biorxiv.org/content/10.1...
π Github github.com/rolandfaure/...
ππ©βπ¬ For 15+ years biology has accumulated petabytes (million gigabytes) ofπ§¬DNA sequencing data𧬠from the far reaches of our planet.π¦ ππ΅
Logan now democratizes efficient access to the worldβs most comprehensive genetics dataset. Free and open.
doi.org/10.1101/2024...
Preprint out for myloasm, our new nanopore / HiFi metagenome assembler!
Nanopore's getting accurate, but
1. Can this lead to better metagenome assemblies?
2. How, algorithmically, to leverage them?
with co-author Max Marin @mgmarin.bsky.social, supervised by Heng Li @lh3lh3.bsky.social
1 / N
Congrats! Nice results π
I am happy to share our new preprint introducing MADRe - a pipeline for Metagenomic Assembly-Driven Database Reduction, enabling accurate and computationally efficient strain-level metagenomic classification.
πhttps://www.biorxiv.org/content/10.1101/2025.05.12.653324v1
1/9
Starting #RECOMBseq with @rayanchikhi.bsky.social 's keynote. Here stressing our responsibility as scientists to enable access to a common good: genomic data
Side note: you could, speaking purely theoretically, also fit every microbe onto an SD card, which is within the weight limit for a carrier pigeon. For some distances, it would be faster than the internet for transmitting sequence libraries
7/
So glad this is finally out. The method has been instrumental in allowing us to compress the AllTheBacteria data - ~2 million bacterial genomes shrink from 3Terabytes (gzipped) to 100Gb using phylogenetic compression. Great work by @brinda.eu
Do you (like me) create a bunch of conda environments, then later forget what they're for, when they were last updated, or which tools are in them?
If so, you might this little project: github.com/rrwick/conda...
So glad to have participated in #DSB2025, what a great workshop! For some mysterious reason it was the first time I attended after 3 years of sequence research. Thanks to all participants & organizers π
Ragnar's made some incredible optimizations on the computation of minimizers, can't wait to see how these improvements will benefit bioinfo tools!
Really cool work!
