Stein Aerts

The @steinaerts.bsky.social lab is looking for a postdoctoral researcher to develop next-generation sequence-to-function models for glioblastoma, one of the most aggressive brain cancers.

More info & how to apply 👉 https://vib.ai/en/opportunities#/job-description/130090

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)

Spark

Introducing IZIKAI. ✨

My son, Juul Aerts, is on vocals and piano, and the band just dropped their debut single, "Spark."

🎧 Listen to "Spark" here: open.spotify.com/track/7D8KxZ...
📸 Follow their journey: www.instagram.com/izikai__/

#IZIKAI #ProudDad

The evolution of gene regulation in mammalian cerebellum development Gene regulatory changes are considered major drivers of evolutionary innovations, including the cerebellum’s expansion during human evolution, yet they remain largely unexplored. In this study, we com...

Outstanding @science.org study on the evolution of gene regulation shaping
#cerebellum development 🧪🧠🧬
@ioansarr.bsky.social @marisepp.bsky.social @tyamadat.bsky.social @steinaerts.bsky.social @kaessmannlab.bsky.social
www.science.org/doi/10.1126/...

Big congrats to the entire Kaessmann lab for this spectacular achievement and beautiful insights. It was a great honour to contribute to this study and to host Ioannis in our lab, an absolutely brilliant scientist. Evolution of genomic enhancers controlling neuronal cell types is just too cool..

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)

Hydrop-v2 is now published ! Allows generating cheap scATAC-seq training data for enhancer modeling with CREsted. Make sure to check out the 600K cell atlas of the last 4 hours of Drosophila embryo development. Fun to use bioML for technology benchmarking :)

YouTube video by VIB-KU Leuven Center for Neuroscience VIB-KU Leuven Center for Neuroscience

🚀 Proudly introducing the VIB-KU Leuven Center For Neuroscience, a merger of the two former VIB research centers VIB-KU Leuven Center for Brain & Disease Research and Neuro-Electronics Research Flanders (NERF)! Our new motto: Bold Science, Real Impact.

www.youtube.com/watch?v=uhaq...

New preprint from the lab and wonderful work by Seppe de Winter:
System-wide extraction of cis-regulatory rules from sequence-to-function models in human neural development
www.biorxiv.org/content/10.6...

tSNE dimensionality reduction of facial mesenchyme TF-MINDI seqlets colored based on TF-family. The coordinator instances are circled and an arrow drawn to a PCA of those coordinator instances colored based on coordinator motif score. This shows that TF-MINDI captures multiple coordinator affinities. For each affinity bin a TF binding motif logo is shown.

To test the sufficiency of the TF-MINDI extracted enhancer code rules we turn to synthetic enhancer design in facial mesenchyme cells. A homeobox-ebox dimer motif (Coordinator) has been shown to be instrumental for this cell type. TF-MINDI identified Coordinator instances at varying affinities.

A large tSNE dimensionality reduction showing PBMC TF-MINDI seqlets colored based on TF-family. This is surrounded by four smaller tSNE dimensionality reductons colored based on TF-ChIP-seq Z-score. Showing specific enrichment of TFs in TF binding sites annotated to the family of that TF. Bottom right shows ROC curve, comparing TF-MINDi based prediction of ChIP-seq signal with motif enrichment based prediction (cisTarget). This shows that TF-MINDI is more accurate.

We validate the TF-MINDI instances using ChIP-seq data in PBMC. Showing that TF-MINDI is more accurate compared to traditional motif enrichment analysis tools.

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

System-wide extraction of cis-regulatory rules from sequence-to-function models in human neural development The genomic cis-regulatory code (CRC) underlies spatiotemporal specificity of gene expression. While sequence-to-function (S2F) models can accurately encode the CRC of transcriptional enhancers, decod...

Check out the preprint: doi.org/10.64898/202... and the TF-MINDI package: github.com/aertslab/TF-MINDI. With @lukasmahieu.bsky.social ’s help this has become an amazing and user-friendly package, please give it a try and provide feedback.

Figure showing four panels. Top left: TF-MNDI logo (pink background and yellow letters), showing the text: "Transcription Factor Motif Instance Neighborhood Decomposition and Interpretation". Top right: TF-MINDI workflow. 1. seqlets are called (showing nucleotide level contribution scores and seqlets as blocks of nucleotides with high contribution). 2. Seqlets are embedded (showing, for each seqlet, a representation of a vector as a heatmap) and 3 seqlets are clustered and annotated (showing a schematic representation of a dimensionality reduction with seqlets colored based on TF-families as well as TF binding motif logos). Bottom left, tSNE dimensionality reduction of organoid seqlets colored based on TF family. Bottom right, similar tSNE dimensionality reduction for embryo seqlets.

To obtain high dimensional embeddings of S2F identified motifs, annotate TFBS across cell-type specific peaks and model TFBS co-occurrences we developed a new python package named TF-MINDI. Resulting in > 400k annotated TFBS instances across the genome (each dot in the tSNE below is one instance).

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

This is the happy face of four researchers embarking on a cool scientific collaboration backed by 7-years of structural financing!

Congrats @steinaerts.bsky.social, @joanampereira.bsky.social, @ppjgoncalves.bsky.social, and Maarten De Vos on your Methusalem grant.

https://tinyurl.com/nvcardzy

Open Senior Bioinformatician position at
@sangerinstitute.bsky.social
Tree of Life, to work on the Biodiversity Cell Atlas initiative with @marakat.bsky.social and me.

📅 Apply by January 18
🔗 sanger.wd103.myworkdayjobs.com/en-US/Wellco...

Please share with anyone who might be interested!

Looking to start your lab in generative biology / AI?
Come join us at the @sangerinstitute.bsky.social
Sanger is core-funded so you can generate data at scale to train the next generation of models and understanding. Design/Engineering/Chemistry/Proteins/Pathways!
pls RT
tinyurl.com/GenGenFaculty

Do transcriptional activators work on any promoter? Our data says no. 🙅‍♂️
Despite driving ~2/3 of mammalian genes, CpG island (CGI) promoters have remained a puzzle. We identified >50 activators that are exclusively compatible with this promoter class. 🧬

Predictive design of tissue-specific mammalian enhancers that function in vivo in the mouse embryo Enhancers control tissue-specific gene expression across metazoans. Although deep learning has enabled enhancer prediction and design in mammalian cell lines and invertebrate systems, it remains uncle...

Our preprint "Predictive design of tissue-specific mammalian enhancers that function in vivo in the mouse embryo" is on bioRxiv: www.biorxiv.org/content/10.6... . Amazing collaboration by @shenzhichen1999.bsky.social, Vincent Loubiere (@impvienna.bsky.social,@viennabiocenter.bsky.social),... (1/2)

Not that long ago, in vivo mouse enhancer design was a dream. Today, it's a reality! Using transfer deep learning to design de novo synthetic embryonic enhancers active in the heart, limb, and CNS. Great collab with @alex-stark.bsky.social lab! @ucibiosci.bsky.social @impvienna.bsky.social

Congratulations @aelek.bsky.social and @martaig.bsky.social ! Thanks @aelek.bsky.social for your visit to our lab and for introducing us to the world of Nematostella :)

Decoding cnidarian cell type gene regulation - Nature Ecology & Evolution This study reconstructs the gene regulatory networks that define cell types in the sea anemone Nematostella vectensis, providing a valuable resource for comparative regulatory genomics and the evoluti...

Excited to share the final version of our study on Nematostella cell type regulatory programs. Part of our @erc.europa.eu StG project, this was a challenging 5-year effort extraodinarily led by @aelek.bsky.social and @martaig.bsky.social.

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

Decoding cnidarian cell type gene regulation - Nature Ecology & Evolution This study reconstructs the gene regulatory networks that define cell types in the sea anemone Nematostella vectensis, providing a valuable resource for comparative regulatory genomics and the evoluti...

Lovely Xmas gift 🎄—our paper is out today in @natecoevo.nature.com www.nature.com/articles/s41...! Huge thanks to everyone who made it possible, especially @aelek.bsky.social and @arnausebe.bsky.social

Join us for the AI & Biology conference in beautiful Suzhou, China, Apr 20–23, 2026! A place to spark dialogue about the future of AI × biology.
We invite abstract submissions from all intersecting fields (deadline Feb 13).
Please help spread the word!
www.csh-asia.org?content/3008

Please consider attending and RT. Great lineup of speakers!

AI reviewers are here — we are not ready Artificial intelligence promises rapid and polite feedback on papers — but we must first review the reviewer.

@nature.com asked me to write an op-ed on the perspective of the AI reviewing process, prompted by the recent partnership between @biorxivpreprint.bsky.social and @qedscience.bsky.social

Hope my perspective adds value to the conversation.

www.nature.com/articles/d41...

JASPAR 2026 is out 🎉

The new release massively expands the TF motif collections and adds a dedicated DeepLearning collection of motifs learned from deep learning models.

Database: jaspar.elixir.no
Paper (NAR): doi.org/10.1093/nar/...

🧵1/2

First paper from the lab is now online
@natneuro.nature.com !
We mapped injury induced enhancers in the mouse CNS and decoded their sequence architecture. Little 🧵 rdcu.be/eSQi1