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.
journal_name
Nat Communjournal_title
Nature communicationsauthors
Salameh TJ,Wang X,Song F,Zhang B,Wright SM,Khunsriraksakul C,Ruan Y,Yue Fdoi
10.1038/s41467-020-17239-9subject
Has Abstractpub_date
2020-07-09 00:00:00pages
3428issue
1issn
2041-1723pii
10.1038/s41467-020-17239-9journal_volume
11pub_type
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