PT - JOURNAL ARTICLE AU - Salameh, Tarik J. AU - Wang, Xiaotao AU - Song, Fan AU - Zhang, Bo AU - Wright, Sage M. AU - Khunsriraksakul, Chachrit AU - Yue, Feng TI - A supervised learning framework for chromatin loop detection in genome-wide contact maps AID - 10.1101/739698 DP - 2019 Jan 01 TA - bioRxiv PG - 739698 4099 - http://biorxiv.org/content/early/2019/08/20/739698.short 4100 - http://biorxiv.org/content/early/2019/08/20/739698.full AB - Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepen our understanding of proper gene regulation events. Current approaches are mainly focused on searching for statistically enriched dots on a genome-wide map. However, given the availability of a wide variety of orthogonal data types such as ChIA-PET, GAM, SPRITE, and high-throughput imaging, a supervised learning approach could facilitate the discovery of a comprehensive set of chromatin interactions. Here we present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps. Compared with current enrichment-based approaches, Peakachu identified more meaningful short-range interactions. We show that our models perform well in different platforms such as Hi-C, Micro-C, and DNA SPRITE, across different sequencing depths, and across different species. We applied this framework to systematically predict chromatin loops in 56 Hi-C datasets, and the results are available at the 3D Genome Browser (www.3dgenome.org).