Christophe Vroland

This paper improves ATAC-seq peak calling by integrating whole-genome sequencing data to control for DNA dosage biases, enhancing peak accuracy and relevance to gene regulation.

The paper maps RNA-DNA interactions in 16 human cell types, showing cell-type-specific and dynamic networks that correlate with gene regulation and disease traits.

The paper develops a machine learning method to identify cis-regulatory element profiles and their sequence features during neuronal differentiation using single-cell ATAC-seq and RNA-seq data.

The paper identifies RNA-initiating sites at STRs and links them to gene expression, revealing regulatory roles and interactions with Alu repeats.