Contribution of DNA low complexity regions in genomic regulations – LOWCO

Finding how regulatory DNA sequence operates to control genome expression (i.e. characterizing a DNA cis-regulatory code) is an intense field of research as this information is key to clinically interpret genetic variations and foster genomic medicine. Bioinformatics and machine learning approaches are instrumental in this task but most existing approaches focus on single nucleotides and motifs. They do not take-into-account the fact that the nucleotide distribution along the genome is not uniform, and forms regions with biased composition containing simple sequence repeats - hereafter called Low Complexity Regions, LCRs. Yet these regions have already been proven to play central roles in genomic regulations (see for instance CpG islands or microsatellites). In the present project, we propose to combine statistical learning and molecular biology in a transdisciplinary strategy (i) to build machine learning-based methods aimed at characterizing LCRs implicated in genomic regulations mainly focusing on two specific biological processes: transcription factor binding on DNA and RNA transcription and (ii) to experimentally challenge and improve these models using high-throughput technologies in mammalian cells and in Plasmodium falciparum. We anticipate that this work will provide means to interpret thousands of genetic variations that are already associated with diseases through genetic studies but without being supported by a specific biological process.