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Lukas Mahieu

@lukasmahieu.bsky.social
68 followers 471 following 0 posts

AI Engineer @ Stein Aerts lab

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Reposted by Lukas Mahieu
Hannah Dickmänken @hannahdckmnkn.bsky.social · 29/01/2026
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)
nature.com
Evaluating single-cell ATAC-seq atlasing technologies using sequence-to-function modeling - Nature Communications
Generating high-quality training data for machine learning is costly. Here, authors include sequence-to-function modeling in benchmarking of custom and commercial droplet-based scATAC platforms, and r...
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Reposted by Lukas Mahieu
Seppe De Winter @seppedewinter.bsky.social · 15/01/2026
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.
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Reposted by Lukas Mahieu
Anamaria Elek @aelek.bsky.social · 06/07/2025
I am very happy to have posted my first bioRxiv preprint. A long time in the making - and still adding a few final touches to it - but we're excited to finally have it out there in the wild: www.biorxiv.org/content/10.1... Read below for a few highlights...
biorxiv.org
Decoding cnidarian cell type gene regulation
Animal cell types are defined by differential access to genomic information, a process orchestrated by the combinatorial activity of transcription factors that bind to cis -regulatory elements (CREs) to control gene expression. However, the regulatory logic and specific gene networks that define cell identities remain poorly resolved across the animal tree of life. As early-branching metazoans, cnidarians can offer insights into the early evolution of cell type-specific genome regulation. Here, we profiled chromatin accessibility in 60,000 cells from whole adults and gastrula-stage embryos of the sea anemone Nematostella vectensis. We identified 112,728 CREs and quantified their activity across cell types, revealing pervasive combinatorial enhancer usage and distinct promoter architectures. To decode the underlying regulatory grammar, we trained sequence-based models predicting CRE accessibility and used these models to infer ontogenetic relationships among cell types. By integrating sequence motifs, transcription factor expression, and CRE accessibility, we systematically reconstructed the gene regulatory networks that define cnidarian cell types. Our results reveal the regulatory complexity underlying cell differentiation in a morphologically simple animal and highlight conserved principles in animal gene regulation. This work provides a foundation for comparative regulatory genomics to understand the evolutionary emergence of animal cell type diversity. ### Competing Interest Statement The authors have declared no competing interest. European Research Council, https://ror.org/0472cxd90, ERC-StG 851647 Ministerio de Ciencia e Innovación, https://ror.org/05r0vyz12, PID2021-124757NB-I00, FPI Severo Ochoa PhD fellowship European Union, https://ror.org/019w4f821, Marie Skłodowska-Curie INTREPiD co-fund agreement 75442, Marie Skłodowska-Curie grant agreement 101031767
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Reposted by Lukas Mahieu
Hannah Dickmänken @hannahdckmnkn.bsky.social · 04/04/2025
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...
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.
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Reposted by Lukas Mahieu
bioRxiv Genomics @biorxiv-genomic.bsky.social · 03/04/2025
CREsted: modeling genomic and synthetic cell type-specific enhancers across tissues and species www.biorxiv.org/content/10.1101/202…
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Reposted by Lukas Mahieu
Niklas Kempynck @niklaskemp.bsky.social · 14/02/2025
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... 🙂
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