David Mauduit

We are thrilled to share our new pre-print: “System-wide extraction of cis-regulatory rules from sequence-to-function models in human neural development”. S2F-deeplearning models can accurately encode enhancers, yet decoding these models into human-interpretable rules remains a major challenge.

TF-MINDI is out! A new method to learn cis-regulatory codes through rich embeddings of TF binding sites. TF-MINDI decomposes motif neighbourhoods, and works downstream of any sequence-to-function deep learning model. We deeply study the enhancer code in human neural development, check out the thread

Such a treat to host @gioelelamanno.bsky.social (EPFL) today and be the first to hear more about his lab's latest work, posted just two days ago on bioRxiv.

Missed the talk?
Here's the preprint: https://www.biorxiv.org/content/10.1101/2025.10.13.682018v1

ikea-style logo of splongget

1/ First preprint from @jdemeul.bsky.social lab 🥳! We present our new multi-modal single-cell long-read method SPLONGGET (Single-cell Profiling of LONG-read Genome, Epigenome, and Transcriptome)! www.biorxiv.org/content/10.1...

Evaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex Johansen et al. report the results of a community challenge to predict functional enhancers targeting specific brain cell types. By comparing multi-omics machine learning approaches using in vivo data...

Check out our work on evaluating methods for predicting in vivo cell enhancer activity in the mouse cortex! Combined, scATAC peak specificity and sequence-based CREsted predictions gave the best predictive performance, aiming to advance genetic tool design for cell targeting in the brain.

VIB.AI's International PhD call is now open!
https://tinyurl.com/53nc9vnd

If you're interested in applying AI to biology, take a look at the PhD projects across our labs.

🗓️ Deadline: June 22nd

We released our preprint on the CREsted package. CREsted allows for complete modeling of cell type-specific enhancer codes from scATAC-seq data. We demonstrate CREsted’s robust functionality in various species and tissues, and in vivo validate our findings: www.biorxiv.org/content/10.1...

Data collected with the new sequencing platform HyDrop v2 is shown. First, a schematic overview of the bead batches of the microfluidic beads is followed by a tSNE and a barplot showing the costs in comparison to 10x Genomics. Then, a track of mouse data (cortex) is shown together with nucleotide contribution scores in the FIRE enhancer in microglia. Here, the HyDrop and 10x based models show the same contributions. On the right, the Drosophila embryo collection is explained; in the paper HyDrop v2 and 10x data are compared to sciATAC data. Then, a nucleotide contribution score is also shown, whereas HyDrop v2 and 10x models show the same contribution, just as in mouse.

Our new preprint is out! We optimized our open-source platform, HyDrop (v2), for scATAC sequencing and generated new atlases for the mouse cortex and Drosophila embryo with 607k cells. Now, we can train sequence-to-function models on data generated with HyDrop v2!
www.biorxiv.org/content/10.1...

HyDrop v2: Scalable atlas construction for training sequence-to-function models Deciphering cis-regulatory logic underlying cell type identity is a fundamental question in biology. Single-cell chromatin accessibility (scATAC-seq) data has enabled training of sequence-to-function ...

2) HyDrop-v2: with a new bead design it provides scalable and cost-effective generation of scATAC-seq atlases. With HyDrop atlases of the fly embryo and mouse cortex we show that CREsted models trained on HyDrop data are equivalent to models trained 10x atlases. www.biorxiv.org/content/10.1...

CREsted: modeling genomic and synthetic cell type-specific enhancers across tissues and species Sequence-based deep learning models have become the state of the art for the analysis of the genomic regulatory code. Particularly for transcriptional enhancers, deep learning models excel at decipher...

Very proud of two new preprints from the lab:
1) CREsted: to train sequence-to-function deep learning models on scATAC-seq atlases, and use them to decipher enhancer logic and design synthetic enhancers. This has been a wonderful lab-wide collaborative effort. www.biorxiv.org/content/10.1...

Isn't Nestle Swiss? Why boycott this brand specificaly?

Ziga Avsec (Google DeepMind), Julien Gagneur (TU Munich), Tanja Kortemme (UCSF), David Kelley (Calico), Ben Lehner (Sanger), Franca Fraternali (UCL), Oliver Stegle (EMBL/DKFZ), and Amos Tanay (Weizmann)

Looking forward to the Inaugural Symposium of the Center for AI & Computational Biology vib.ai with a great line-up of speakers at the interface of AI & biology: D. Kelley, J. Gagneur, Z. Avsec, T. Kortemme, B. Lehner, F. Fraternali, A. Tanay & O. Stegle (20Nov) www.vibconferences.be/events/vibai...