Computer vision for pattern detection in chromosome contact maps

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. Chromatin loops bridging distant loci within chromosomes can be detected by a variety of techniques such as Hi-C. Here the authors present Chromosight, an algorithm applied on mammalian, bacterial, viral and yeast genomes, able to detect various types of pattern in chromosome contact maps, including chromosomal loops.