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

Tarik J. Salameh, Xiaotao Wang (), Fan Song, Bo Zhang, Sage M. Wright, Chachrit Khunsriraksakul, Yijun Ruan and Feng Yue ()
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Tarik J. Salameh: The Pennsylvania State University, University Park
Xiaotao Wang: Northwestern University Feinberg School of Medicine
Fan Song: The Pennsylvania State University, University Park
Bo Zhang: The Pennsylvania State University, University Park
Sage M. Wright: The Pennsylvania State University, University Park
Chachrit Khunsriraksakul: The Pennsylvania State University, University Park
Yijun Ruan: The Jackson Laboratory for Genomic Medicine
Feng Yue: Northwestern University Feinberg School of Medicine

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on a genome-wide map. However, given the availability of orthogonal data types such as ChIA-PET, HiChIP, Capture Hi-C, 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. We compare Peakachu with current enrichment-based approaches, and find that Peakachu identifies a unique set of short-range interactions. We show that our models perform well in different platforms, across different sequencing depths, and across different species. We apply this framework to predict chromatin loops in 56 Hi-C datasets, and release the results at the 3D Genome Browser.

Date: 2020
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DOI: 10.1038/s41467-020-17239-9

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