Computer vision for pattern detection in chromosome contact maps

Cyril Matthey-Doret (1, 2) , Lyam Baudry (1, 2) , Axel Breuer (3) , Rémi Montagne (1) , Nadège Guiglielmoni (1) , Vittore Scolari (1) , Etienne Jean (1) , Arnaud Campeas (3) , Philippe Henri Chanut (3) , Edgar Oriol (3) , Adrien Méot (3) , Laurent Politis (3) , Antoine Vigouroux (4) , Pierrick Moreau (1) , Romain Koszul (1, 5) , Axel Cournac (1, 5)

1 Régulation spatiale des Génomes - Spatial Regulation of Genomes
2 Collège Doctoral
3 ENGIE
4 Biologie de Synthèse - Synthetic biology
5 Département de Biologie Computationnelle - Department of Computational Biology

Chromosomes of all species studied so far display a variety of higher-order organisational features, such as self-interacting domains or loops. These structures, which are often associated to biological functions, form distinct, visible patterns on genome-wide contact maps generated by chromosome conformation capture approaches such as Hi-C. Here we present Chromosight, an algorithm inspired from computer vision that can detect patterns in contact maps. Chromosight has greater sensitivity than existing methods on synthetic simulated data, while being faster and applicable to any type of genomes, including bacteria, viruses, yeasts and mammals. Our method does not require any prior training dataset and works well with default parameters on data generated with various protocols.

Domains: Life Sciences [q-bio]

Cyril Matthey-Doret, Lyam Baudry, Axel Breuer, Rémi Montagne, Nadège Guiglielmoni, et al.. Computer vision for pattern detection in chromosome contact maps. Nature Communications, 2020, 11 (1), pp.5795. ⟨10.1038/s41467-020-19562-7⟩. ⟨pasteur-03263561⟩