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

Document Type

Article

Publication Date

7-9-2020

Keywords

JGM, JAXCC

JAX Source

Nat Commun 2020 Jul 9; 11:3428

Volume

11

Issue

1

First Page

3428

Last Page

3428

ISSN

2041-1723

PMID

32647330

DOI

https://doi.org/10.1038/s41467-020-17239-9

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.

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