Seppe De Winter

Seppe De Winter

@seppedewinter.bsky.social

Post-doctoral researcher at aertslab VIB-AI KU Leuven. https://seppedewinter.net

88 followers111 following18 posts

Seppe De Winter reposted

Hannah Dickmänken
@hannahdckmnkn.bsky.social

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.comEvaluating single-cell ATAC-seq atlasing technologies using sequence-to-function modeling - Nature CommunicationsGenerating 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...

Seppe De Winter reposted

Stein Aerts
@steinaerts.bsky.social

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

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.

Seppe De Winter
@seppedewinter.bsky.social

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.

View full thread
Seppe De Winter
@seppedewinter.bsky.social

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.

doi.orgSystem-wide extraction of cis-regulatory rules from sequence-to-function models in human neural developmentThe 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...
Seppe De Winter
@seppedewinter.bsky.social

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.

View full thread
Seppe De Winter
@seppedewinter.bsky.social

Finally, using topic-modelling we model TFBS co-occurrences. We validate these white-box enhancer models by performing cross-species integration of our TF-MINDI results, with a zebrafish developmental S2F model, and show that the topic models can recover zebrafish floor plate-specific regions.

Seppe De Winter
@seppedewinter.bsky.social

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.

View full thread
Seppe De Winter
@seppedewinter.bsky.social

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.

Seppe De Winter
@seppedewinter.bsky.social

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.

View full thread
Seppe De Winter
@seppedewinter.bsky.social

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).

Seppe De Winter
@seppedewinter.bsky.social

Counting the number of TFBS instances across cell type-specific peaks reveals cell type-specific regulators in the form of a code-table. Similar codes are obtained for the organoid and embryo and we link instances to TFs based on cell type-specific TF-expression.

Seppe De Winter
@seppedewinter.bsky.social

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.

Seppe De Winter
@seppedewinter.bsky.social

We tackle this challenge in human neural development. We generate two multiome atlases: on neural tube organoids and on a 4 p.c.w. human embryo. The organoids recapitulate human embryonic development strikingly well. DeepNeuralTube S2F models recover most known cell type-specific TF binding motifs.

Seppe De Winter reposted

Niklas Kempynck
@niklaskemp.bsky.social

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.

cell.comEvaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortexJohansen 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...

Seppe De Winter reposted

Stein Aerts
@steinaerts.bsky.social

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...

cell.comEvaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortexJohansen 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...
allen institute@alleninstitute.org

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. 🧠📈

Seppe De Winter reposted

Jacob Schreiber
@jmschreiber91.bsky.social

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...

biorxiv.orgProgrammatic design and editing of cis-regulatory elementsThe 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...

Seppe De Winter reposted

Stein Aerts
@steinaerts.bsky.social

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...

biorxiv.orgCREsted: modeling genomic and synthetic cell type-specific enhancers across tissues and speciesSequence-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...

Seppe De Winter reposted

Saez-Rodriguez Group
@saezlab.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

We 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

Seppe De Winter reposted

VIB.AI
@vibai.bsky.social

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/...

science.orgEnhancer-driven cell type comparison reveals similarities between the mammalian and bird palliumCombinations 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...

Seppe De Winter reposted

Stein Aerts
@steinaerts.bsky.social

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

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... 🙂

Seppe De Winter reposted

Niklas Kempynck
@niklaskemp.bsky.social

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

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/...

science.orgEnhancer-driven cell type comparison reveals similarities between the mammalian and bird palliumCombinations 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...

You are up to date.