A supervised learning framework for chromatin loop detection in genome-wide contact maps

Abstract

Predicting chromatin loops from genome-wide interaction matrices such as Hi-C data provides insight into gene regulation events. Here, the authors present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps, and apply it to systematically predict chromatin loops in 56 Hi-C datasets, with results available at the 3D Genome Browser.