Really amazing work and a big effort by Olga, Alex, Julie, Koen and many others. Check it out for sure!

New preprint @cxqiu.bsky.social @jshendure.bsky.social ! Can we learn regulatory grammars of human cell types — by training on mouse development and transferring across 241 mammalian genomes? Introducing STEAM & a whole-organism scATAC-seq atlas from E10 to birth. www.biorxiv.org/content/10.6...

CREsted is finally out! You can find the article, together with a summarizing Research Briefing, in thread. 🦎

Last summer I spent 4 months working at the @alleninstitute.org as a Visiting Scientist. Recently we released some preprints about the work we collaborated on, where from new multiome atlases of CNS regions we tried to decipher underlying enhancer logic with CREsted (among many other things). (1/n)

Paper alert! 💻 How many cells do you need to train reliable deep learning models in regulatory genomics? We asked how data quality, sequencing depth, and dataset size affect training of sequence-to-function models from scATAC-seq. Out now www.nature.com/articles/s41... (details below)

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.

Relieved to finally post my whole developing brain evolutionary "theory of everything" preprint! www.biorxiv.org/content/10.1...

We have two open positions for a ML and a LLM engineer to launch a machine learning expertise unit in our center @vibai.bsky.social, see vib.ai/en/opportuni...

We will have our next community meeting on Tuesday, 2025-09-16 at 18:00 CEST! Niklas Kempynck will be presenting on CREsted, a package for training enhancer models on scATAC-seq data. (Zoom registration link and more information in thread!) 🧵

I wrote a quick application note on Tomtom-lite, a Python implementation of the Tomtom algorithm for comparing PWMs against each other. This implementation can be 10-1000x faster and, as a Python function, can be integrated into your workflows easier. www.biorxiv.org/content/10.1...

One thousand candidate enhancers tested in vivo in the mouse brain! A massive resource and oh so useful as validation set for genome-wide enhancer prediction methods. Super fun to be involved in one of the papers: ‘the prediction challenge paper’ by Nelson&Niklas et al www.cell.com/cell-genomic...

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.

Calling someone bird-brained is, in fact, a way of calling someone highly intelligent. @yaseminsaplakoglu.bsky.social reports: www.quantamagazine.org/intelligence...

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...

Also check out Hannah’s thread on our latest preprint on HyDrop v2, an open-source platform for scATAC-sequencing, and a great, cost-efficient way of generating data for S2F models. 🙌

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...

Very excited to share our new preprint together with @daniedaaboul.bsky.social, where we studied the gene regulatory code that hippocampal granule cells (GCs) use during synapse formation (1/n)

How does gene regulation shape brain evolution? Our new preprint dives into this question in the context of mammalian cerebellum development! rb.gy/dbcxjz Led by @ioansarr.bsky.social, @marisepp.bsky.social and @tyamadat.bsky.social, in collaboration with @steinaerts.bsky.social

The latest Discover ASAP episode dives into "Cell Type Directed Design of Synthetic Enhancers," a study published in Nature by CRN Team Voet. They discuss how machine learning enables precise enhancer design for targeted gene expression 🧬 Watch: www.youtube.com/watch?v=Qcms...

We wrote a review article on modelling and design of transcriptional enhancers using sequence-to-function models. From conventional machine learning methods to CNNs and using models as oracles/generative AI for synthetic enhancer design! @natrevbioeng.bsky.social www.nature.com/articles/s44...

This has been a fantastic adventure - to capture the genomic regulatory code underlying brain cell types (using deep learning models trained on chromatin accessibility), and then use these models to compare cell types between the bird and mammalian brain

Just very happy to have our paper out today! A big thanks to all our co-authors, and to Nikolai and @steinaerts.bsky.social for the teamwork over the past years. If you are interested in using our models for cross-species enhancer studies, check out crested.readthedocs.io/en/stable/mo... 🙂