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.
Original language | English (US) |
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Article number | 3428 |
Journal | Nature communications |
Volume | 11 |
Issue number | 1 |
DOIs | |
State | Published - Dec 1 2020 |
Funding
This work was supported by NIH grants R35GM124820, R01HG009906, U01CA200060, and R24DK106766 to F.Y. We want to thank Drs. Job Dekker, William Noble, and the rest of 4D Nucleome Project Joint Analysis Workgroup for the discussion and suggestions. We thank Drs. Job Dekker and Oliver Rando’s group (Liyan Yang, Nils Krietenstein, Sergey Venev, Johan Gibcus) for the Micro-C and Hi-C data in H1ESC cells (funded by DK107980), Dr. Keji Zhao for the TrAC-loop data in GM12878 cells, and Dr. Bing Ren’s group for the H3K4me3 PLAC-Seq data in GM12878 cells.
ASJC Scopus subject areas
- General Chemistry
- General Biochemistry, Genetics and Molecular Biology
- General Physics and Astronomy