Laurent Jacob

legend 2025 will be next Tuesday/Wednesday/Thursday.

The detailed program is on legend2025.sciencesconf.org?lang=en

If you are not attending the conference you can still follow the presentations online (Zoom links are on the website).

STORIES: learning cell fate landscapes from spatial transcriptomics using optimal transport - Nature Methods By learning a differentiation potential using an optimal transport-based approach, STORIES models and infers cell fate trajectories using spatiotemporal omics data.

Happy to share STORIES out now on Nature Methods

STORIES learns cell fate landscapes from spatial tramscripromics data profiled at several time points, thus allowing prediction of future cell states.

Led by Geert-Jan Huizing and Jules Samaran

www.nature.com/articles/s41...

@pasteur.fr

Very happy about this work on phylogenetic neural inference, led by @lblassel.bsky.social :)

October 17 is your last chance to register for the 2nd conference on Machine Learning for Evolutionary Genomics Data (Dec 8-12), in the French Alps at legend2025.sciencesconf.org
The conference talks are online at legend2025.sciencesconf.org/data/book_le...

‘Google for DNA’ brings order to biology’s big data MetaGraph compresses vast data archives into a search engine for scientists, opening up new frontiers of biological discovery.

Ca n'est pas si souvent, un article publié dans Nature met ma communauté à l'honneur (la bioinformatique des séquences). Je vous raconte ?
www.nature.com/articles/d41...

Exploring the space of self-reproducing RNA using generative models, Martin Weigt Exploring the archaic introgression landscape of admixed populations through joint ancestry inference, Jazeps Medina Tretmanis [et al.] Predicting natural variation in the yeast phenotypic landscape with machine learning, Sakshi Khaiwal [et al.] Phylodynamic modeling with unsupervised Bayesian neural networks, Marino Gabriele [et al.] Likelihood-free inference of phylogenetic tree posterior distributions, Luc Blas- sel [et al.] Generative continuous time model reveals epistatic signatures in protein evolu- tion, Barrat-Charlaix Pierre Neural posterior estimation for high-dimensional genomic data from complex pop- ulation genetic models, Jiseon Min [et al.] A differentiable model for detecting diversifying selection directly from alignments in large-scale bacterial datasets, Leonie Lorenz [et al.] Detecting interspecific positive selection using transformers, Charlotte West [et al.] Predicting Multiple Sequence Alignment Uncertainty via Machine Learning, Lucia Martin-Fernandez [et al.] Graph Neural Networks for Likelihood-Free Inference in Diversification Mod- els, Amélie Leroy [et al.] Popformer: learning general signatures of genetic variation and natural selection with a self-supervised transformer, Leon Zong [et al.]

PRIVET: PRIVacy metric based on Extreme value Theory, Antoine Szatkownik [et al.] Generative models for inferring the evolutionary history of the malaria vector Anopheles gambiae, Amelia Eneli [et al.] Language Models Outperform Supervised-Only Approaches for Conserved Ele- ment Comprehension, Eyes Robson [et al.] Identification and Classification of Orphan Genes, Spurious Orphan Genes, and Conserved Genes from the human microbiome, Chen Chen Neural Simulation-based inference of demography and selection, Francisco De Borja Campuzano Jiménez [et al.] Species Identification and aDNA Read Mapping Using k-mer Embeddings, Filip Thor [et al.] Contrastive Learning for Population Structure and Trait Prediction, Filip Thor [et al.] Protein and genomic language models chart a vast landscape of antiphage de- fenses, Mordret Ernest The Phylogenomics and Sparse Learning of Trait Innovations, Gaurav Diwan [et al.]

The decisions for LEGEND are out: legend2025.sciencesconf.org/data/book_le...

I'm really looking forward to hearing these 21 exciting presentations (and additional 30 posters) next December.

If you want to attend too, registration is open until October 17th through legend2025.sciencesconf.org

Only a few hours left to submit your abstract for a talk at the Machine Learning in Evolutionary Genomics conference in December in Aussois in the French alps!

MLCB - Schedule The in-person component will be held at the New York Genome Center, 101 6th Ave, New York, NY 10013.

#MLCB2025 is tomorrow & Thursday with a fantastic lineup of keynotes & contributed talks www.mlcb.org/schedule. We'll be livestreaming through our YouTube channel www.youtube.com/@mlcbconf. Thanks to www.corteva.com, instadeep.com, the Simons Center at CSHL & NYGC for generous support!

Achievement unlocked: defend your habilitation thesis on the same day than your partner. That was quite a science + celebration day, thanks to all involved 💙✨

🌎👩‍🔬 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...

The call for abstract for LEGEND is now open:
legend2025.sciencesconf.org

It will close on September 22nd (oral presentations) and October 1st (posters).

Send us your best work on Machine Learning for Evolutionary Genomics and come discuss it with us in the French Alps next December!

#TalentCNRS 🥉| Flora Jay, entre génomes synthétiques et récits évolutifs, reçoit la médaille de bronze du CNRS.
➡️ www.ins2i.cnrs.fr/fr/cnrsinfo/...
🤝 @lisnlab.bsky.social @cnrs-paris-saclay.bsky.social

Preprint alert! 🦌
Our new abundance index, REINDEER2, is out!
It's cheap to build and update, offers tunable abundance precision at kmer level, and delivers very high query throughput.

Short thread!

www.biorxiv.org/content/10.1...

github.com/Yohan-Hernan...

Registration is now open!

The 580€ include housing and all meals.

We will close on October 17th or when reaching 80 participants.

The 2026 Probabilistic Modeling in Genomics (ProbGen) meeting will be held at UC Berkeley, March 25-28, 2026. We have an amazing list of keynote speakers and session chairs:
probgen2026.github.io

Please help spread the news.

Merci à @cnrs-rhoneauvergne.bsky.social et @astropierre.com pour cette interview sur mes travaux en IA pour la génomique évolutive!

There is a nice example in @stephaneguindon.bsky.social's Ph.D thesis p.55

theses.hal.science/tel-00843343...

The design matrix of the regression should be nPairs x nBranches, and have a 1 at coordinates (i,j) such that branch j belongs to the path defined by pair i in the tree, 0 otherwise.

I think one way to do this is the least squares method, which gives you the set of branch lengths on your given topology such that the sum of squared differences between your given distances and the distances on the tree are minimal.

Phyloformer is finally published in MBE! 🎉

academic.oup.com/mbe/advance-...

The thread below provides a summary of our neural network for likelihood-free phylogenetic reconstruction.

People having breakfast in front of the Alps in the Centre Paul Langevin.

Come hear about the latest advances in the field and discuss your own work at Centre Paul Langevin in beautiful Aussois.

A headshot of Dr Burak Yelmen.

Burak Yelmen from the University of Tartu will give a keynote presentation on "A perspective on generative neural networks in genomics with applications in synthetic data generation".

A headshot of Dr Claudia Solis-Lemus.

Claudia Solís-Lemus from the University of Wisconsin-Madison will give a keynote presentation on "The good, the bad and the ugly of deep learning in phylogenetic inference".

A headshot of Dr Anne-Florence Bitbol.

Anne-Florence Bitbol from EPFL will give a keynote presentation on "Coevolution-aware language models".

A legendary being holds a phylogenetic tree in the palm of their hand, with snowy mountains in the background.

The next LEGEND conference on machine learning for evolutionary genomics will be in Aussois (French Alps) between December 8th and 12th.

Mark your calendars and make sure your best work is ready next September when the call for abstracts opens 🙂

legend2025.sciencesconf.org

MUSET: Set of utilities for constructing abundance unitig matrices from sequencing data AbstractSummary. MUSET is a novel set of utilities designed to efficiently construct abundance unitig matrices from sequencing data. Unitig matrices extend

🧬 Excited to share our latest work, MUSET 🌭, a new tool for creating abundance unitig matrices from sequencing data. It was published yesterday in Oxford Bioinformatics if you want to have a look👀 :

academic.oup.com/bioinformati...

Let's break it down:

My book is (at last) out, just in time for Christmas!
A blog post to celebrate and present it: francisbach.com/my-book-is-o...

Ok, I tried to create my own list of people working on developing statistical or machine learning models applied to omics data. I am sure I missed a lot of cool people. If you'd like to be added, let me know. #Stats #ML #Omics
go.bsky.app/73rcuJn

Hi Raphael, thanks for putting this together, I'll be happy to be in the list if you think it makes sense :)

A sketch summarizing the entire Phyloformer process.

All this work was done by Luca Nesterenko and
@lblassel.bsky.social , assisted by P. Veber, Bastien Boussau
and myself.

The code and data are available at github.com/lucanest/Phy...

Please share if you find this interesting, and we welcome your feedback :)