Laurent Bréhélin

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 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.

The paper optimizes data integration for GRN inference in Arabidopsis thaliana by introducing DIOgene, which improves prediction accuracy and identifies gene-specific integration intensities.

This paper introduces TFscope, a machine learning method to identify genomic features explaining binding differences between TFs in ChIP-seq experiments, showing that co-factors and nucleotide environment often drive differences in cell types or treatments, while core motif differences are key for paralogous TFs.

The paper introduces a computational method to identify long regulatory elements across eukaryotic genomes, including Plasmodium falciparum, and shows their role in gene regulation.

The paper investigates transcription initiation at polyT short tandem repeats (STRs) using CAGE data and develops a deep learning model to predict STR-associated transcription with high accuracy.

The paper introduces TFcoop, a method to infer TF combinations involved in target TF binding, and shows it outperforms PWM methods while revealing cell-type-specific and promoter-class-specific TF interactions.

The paper develops a sequence-based method to predict gene expression accurately, showing that nucleotide composition and regulatory region contributions are key factors.

The paper improves pairwise protein sequence comparison by integrating domain co-occurrence information, increasing BLAST hit sensitivity and proteome coverage.