Roland Faure

Roland Faure

@rfaure.bsky.social

Sequence bioinfomatician, algorithms, methods. Postdoc in Institut Pasteur in Rayan Chikhi's lab

80 followers137 following29 posts

View full thread

Third application: imagine you have sequenced your metagenome but not deeply enough to have a good assembly. Then you can go through the Logan accessions and recruit highly similar sequencing datasets to help co-assemble your sample.

View full thread

First question we can answer with the FracMinHashes: are we exhausting the number of new DNA we sequence? Answer: no in general (as of 2023). In 2023, we were still discovering more and more new stuff every year. However, for gut microbiota, yes: the amount of novelty per sequenced bp is dropping.

Metadata in the SRA is not always precise and reliable. We investigated all 'organism' tags by checking coherence with tags from most similar accessions. We found thousands of misannotated datasets. We found even more datasets for which we specified the tag ("metagenome" -> "mouse gut metagenome")

Roland Faure reposted

When utilized in literature review, LLMs consistently 1. fail to mention female authors in female-led literatures, 2. insist that men are more influential or more heavily cited when this is contradicted by objective citation counts, and 3. attribute women’s work to hallucinated male scholars.

Glad to announce that our new long-read metagenomic SNP caller, SNooPy, is published in NAR! Existing long-read SNP callers are not designed for metagenomic data, check out our new solution 👉https://academic.oup.com/nar/article/54/10/gkag556/8700491 @narjournal.bsky.social

View full thread

We use a new statistical test, which I find beautiful in its simplicity. The idea is to exploit the length of the reads to look at multiple loci at the same time. The p-value of a *group of* variants being due only to sequencing errors is 0.1^(ab)*C(m,b)*C(n,a), C denoting binomial coefficients.

Most modern SNP callers use neural networks, but this software takes a different path—no NN at all! That leaves room for big improvements by blending both approaches.

Glad to announce that our new long-read metagenomic SNP caller, SNooPy, is published in NAR! Existing long-read SNP callers are not designed for metagenomic data, check out our new solution 👉https://academic.oup.com/nar/article/54/10/gkag556/8700491 @narjournal.bsky.social

SNooPy calls much more SNPs than other tools, including DeepVariant. This is especially true for datasets with high coverage and several strains of the same species, typically human gut metagenomes sequencing 🦠

The key is distinguishing SNPs from sequencing errors. In metagenomic data, you cannot rely on "50% of the reads should carry the variant", as you could with human/diploid reads.

Roland Faure reposted

This is now published in Genome Research (doi.org/10.1101/gr.2...). Thank you everyone for your feedback and also the anonymous reviewers who helped to greatly improve the paper. I hope this becomes a useful resource for the community.

doi.orgHash functions in nucleotide sequence analysisRandomness is a powerful tool in the design and analysis of algorithms and data structures for nucleotide sequence data. Nucleotide sequences are not themselves random but are often randomized using hash functions. Despite their widespread use in genomics, there is no comprehensive review of the types of hash functions used and their various applications. In this survey intended for bioinformatic methods developers, we divide hash functions into four categories: scattering hash functions, permutations, minimum perfect hash functions, and locality-sensitive hash functions. For each category, we provide examples of both general-use hash functions that have been applied in nucleotide sequence analysis and hash functions that have been designed specifically for nucleotide sequence analysis. We highlight their salient properties, commonalities, differences, and application areas.
Paul Medvedev @pashadag.bsky.social

1/4 Hash functions in genomic sequence analysis (tinyurl.com/4kk9ccmt) : a new survey written together with Ke Chen, Xiang Li, Qian Shi, and Mingfu Shao. Before submitting it, we are posting it online to get feedback from the community.

Roland Faure reposted

Roland Faure reposted

what's the current status of HERRO and (bacterial) nanopore? Last I saw was Ryan Wick's blog evaluating it. How many people now use it (or did it actually get folded into a basecaller, or did it get dropped in favour of something else)?

Roland Faure reposted

📌 Les #soumissions pour @jobim2026.bsky.social sont ouvertes jusqu'au 15/03/26. 📝 Soumission de travaux originaux, articles longs (+ PCI), activités de plateformes et de service, posters et démonstrations 📍 Plus d’infos sur : jobim2026.sfbi.fr #JOBIM2026 #bioinfo #Strasbourg

JOBIM 2026@jobim2026.bsky.social

📣 Ouverture des soumissionsSoumissions ouvertes pour #JOBIM2026 🎉  🗓️ 1er février → 15 mars 2026 👉 Posters, démos, highlights, articles longs, plateformes & services… 🔗 premc.org/jobim-2026/ #bioinfo #Strasbourg

Roland Faure reposted

🗜️⚡ 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...

github.comGitHub - ebiggers/libdeflate: Heavily optimized library for DEFLATE/zlib/gzip compression and decompressionHeavily optimized library for DEFLATE/zlib/gzip compression and decompression - ebiggers/libdeflate

Roland Faure reposted

"..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

bioRxiv Bioinfo@biorxiv-bioinfo.bsky.social

Accelign: a GPU-based Library for Accelerating Pairwise Sequence Alignment https://www.biorxiv.org/content/10.64898/2025.12.17.694868v1

Roland Faure reposted

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 Ondřej Sladký and @pavelvesely.bsky.social we asked: can we build one that works efficiently for any k-mer set?

Bioinformatics Advances@bioinfoadv.bsky.social

🧮 Just out in Bioinformatics Advances: “FroM Superstring to Indexing: A space-efficient index for unconstrained k-mer sets using the Masked Burrows-Wheeler Transform (MBWT)”  Full article available: https://doi.org/10.1093/bioadv/vbaf290  Authors include: @pavelvesely.bsky.social, @brinda.eu

View full thread
View full thread

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.

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%

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.

Roland Faure reposted

Preprint Alert! We present new strategies to accelerate large-scale document comparison using MinHash-like sketches. A thread:

bioRxiv Bioinfo@biorxiv-bioinfo.bsky.social

Compressed inverted indexes for scalable sequence similarity https://www.biorxiv.org/content/10.1101/2025.11.21.689685v1

🧵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 ⚡. And thanks to MSRs, *highly similar strains are not collapsed*

🧵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.

🧵4/6 Two key properties that make MSRs sketches really cool: 👉 They are alignable sequences: you can just feed them in existing assembler 👉 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

🧵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*

Roland Faure reposted

🌎👩‍🔬 For 15+ years biology has accumulated petabytes (million gigabytes) of🧬DNA sequencing data🧬 from the far reaches of our planet.🦠🍄🌵 Logan now democratizes efficient access to the world’s most comprehensive genetics dataset. Free and open. doi.org/10.1101/2024...

Roland Faure reposted

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

bioRxiv Bioinfo@biorxiv-bioinfo.bsky.social

High-resolution metagenome assembly for modern long reads with myloasm https://www.biorxiv.org/content/10.1101/2025.09.05.674543v1

Roland Faure reposted

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. 🔗https://www.biorxiv.org/content/10.1101/2025.05.12.653324v1 1/9

Roland Faure reposted

Replying to @baym.lol

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/

Roland Faure reposted

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

Michael Baym@baym.lol

Our latest paper, in which @brinda.eu (along with @zaminiqbal.bsky.social and others) introduces phylogenetic compression for storage and search of enormous microbial genome libraries, was published today in @naturemethods.bsky.social: rdcu.be/eg4OA 1/

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 😃

Roland Faure reposted

Ragnar's made some incredible optimizations on the computation of minimizers, can't wait to see how these improvements will benefit bioinfo tools!

Ragnar {Groot Koerkamp}@curiouscoding.nl

Nice result to end the day (night*): After discussions with @imartayan.bsky.social, the SIMD minimizer code now also does proper canonical (revcomp) minimizers: ~1ns/bp for fwd minis +0.4ns/bp with collect and dedup +0.6ns/bp with canonical hashes. Super happy how it's only 2x slower in the end!

So glad to have successfully defended my Ph.D. last week 😀 Work on producing haplotype-resolved metagenomic assemblies using noisy long reads (HairSplitter) and high-fidelity long reads (Alice assembler, unpublished yet). Thanks to my advisors Dominique Lavenier and Jean-François Flot ❤️

You are up to date.