1/ 🧬 Happy to share our new preprint on modeling cis-regulatory variation in human brain enhancers across a large Parkinson’s disease cohort: www.biorxiv.org/content/10.6... Details in the thread below:
biorxiv.org
Seppe De Winter
@seppedewinter.bsky.social
Post-doctoral researcher at aertslab VIB-AI KU Leuven. https://seppedewinter.net
Check out our @natmethods.nature.com publication on CREsted, a user-friendly sequence-to-function framework to decipher enhancer codes and design synthetic enhancers! 🦎
Niklas Kempynck@niklaskemp.bsky.social · 4mo agoCREsted is finally out! You can find the article, together with a summarizing Research Briefing, in thread. 🦎
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)
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...
nature.com
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
Seppe De Winter@seppedewinter.bsky.social · 7mo agoWe 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.
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.
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...
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.
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...
cell.com
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...
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...
cell.com
allen institute@alleninstitute.org · last yr.In the battle against brain disease, researchers can now rely on a new arsenal of genetic tools – The Armamentarium. Together with scientists from across the NIH BRAIN Initiative, we’ve created and published over 1000 new enhancer AAV vectors. 🧠📈
Our preprint on designing and editing cis-regulatory elements using Ledidi is out! Ledidi turns *any* ML model (or set of models) into a designer of edits to DNA sequences that induce desired characteristics. Preprint: www.biorxiv.org/content/10.1... GitHub: github.com/jmschrei/led...
Programmatic design and editing of cis-regulatory elements
The development of modern genome editing tools has enabled researchers to make such edits with high precision but has left unsolved the problem of designing these edits. As a solution, we propose Ledi...
biorxiv.org
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...
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...
biorxiv.org
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...
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...
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
📄 Update on our preprint about Gene Regulatory Net (GRN) benchmarking 📄 We have included the original and decoupled version of SCENIC+, added a new metric and two more databases. Dictys and SCENIC+ outperformed others, but still performed poorly in causal mechanistic tasks. doi.org/10.1101/2024... 👇
Saez-Rodriguez Group@saezlab.bsky.social · 2y agoWe present Gene Regulatory nETwork Analsyis (GRETA), a framework to infer, compare and evaluate gene regulatory networks #GRNs. With it, we have benchmarked multimodal and unimodal GRN inference methods. Check the results here 👇 Paper: doi.org/10.1101/2024.12.20.629764 Code: github.com/saezlab/greta
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...
Modelling and design of transcriptional enhancers - Nature Reviews Bioengineering
Enhancers are genomic elements critical for regulating gene expression. In this Review, the authors discuss how sequence-to-function models can be used to unravel the rules underlying enhancer activit...
nature.com
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...
KU Leuven turns 600(!) this year and is celebrating with a public event this weekend! The @steinaerts.bsky.social lab is offering guided lab tours. Want a behind-the-scenes look? All tours on Saturday are full, but you can still register for Sunday! www.kuleuven.be/600years/exp...
Explore cellular diversity with microscopy and AI: registration | KU Leuven
kuleuven.be
In a new study, Nikolai Hecker, Niklas Kempynck et al. in the team of @steinaerts.bsky.social explore 300 million years of brain evolution through the lens of enhancer codes. www.science.org/doi/10.1126/...
Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium
Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We used deep learning to characterize these enhancer codes and devised three metr...
science.org
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
Niklas Kempynck@niklaskemp.bsky.social · last yr.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... 🙂
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... 🙂
VIB.AI@vibai.bsky.social · last yr.In a new study, Nikolai Hecker, Niklas Kempynck et al. in the team of @steinaerts.bsky.social explore 300 million years of brain evolution through the lens of enhancer codes. www.science.org/doi/10.1126/...