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. In this seminar, I will present Chromosight, an algorithm inspired from computer vision that can detect and quantify any patterns in contact maps. I will show different applications in data from different protocols and from various organisms including bacteria, yeast, mammals and virus.
In a second part, I will present an ongoing project which consists of the development of an algorithm capable of reconstructing currently invisible parts in contact maps using statistical inference methods or machine learning. This method could thus reveal the contacts of several hardly accessible genomic objects such as transposons, superintegrons and or any type of repeated sequence present within a chromosome contact map.