Wouter R. A. Meuleman

The paper designs cell type-specific enhancers using deep learning models trained on chromatin accessibility data and validates their function across human and mouse cell types.

Epilogos provides an information-theoretic framework for visualizing and analyzing multi-tissue chromatin state maps, enabling navigation, prioritization, and comparison of genomic regions.

This study uses iterative deep learning to design human enhancers with cell type-specific activity, validated through experimental testing and perturbation experiments.

DNA-Diffusion uses diffusion models to generate cell type-specific regulatory DNA sequences that modulate chromatin accessibility and gene expression.

This paper develops a statistical method to identify gene co-essentiality, creating a genome-wide functional module almanac that predicts functions for 102 uncharacterized genes and validates key findings.

The paper creates high-precision maps of DNase I hypersensitive sites across 733 human biosamples and develops a regulatory vocabulary to annotate regulatory DNA, concentrating disease-associated signals.

The paper maps transcription factor footprints across 243 human cell and tissue types, revealing their structure, function, and relationship to genetic variation and disease.

The paper creates a mouse regulatory DNA atlas by mapping DNase I hypersensitive sites across development and shows dynamic changes in regulatory elements and transcription factor activity.

The paper develops EpiMap, a compendium of 833 epigenomes, to annotate genetic variants and dissect complex traits by analyzing epigenomic data across 800 samples.