Christophe Menichelli

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 identifies widespread transcription initiation at microsatellites across species and develops deep learning models to predict STR-associated transcription and its impact on disease variants.

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