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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, View ORCID ProfileFeng Yue
doi: https://doi.org/10.1101/739698
Tarik J. Salameh
1Bioinformatics and Genomics Program, The Pennsylvania State University, University Park, State College, PA 16802, USA
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Xiaotao Wang
2Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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Fan Song
1Bioinformatics and Genomics Program, The Pennsylvania State University, University Park, State College, PA 16802, USA
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Bo Zhang
1Bioinformatics and Genomics Program, The Pennsylvania State University, University Park, State College, PA 16802, USA
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Sage M. Wright
1Bioinformatics and Genomics Program, The Pennsylvania State University, University Park, State College, PA 16802, USA
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Chachrit Khunsriraksakul
1Bioinformatics and Genomics Program, The Pennsylvania State University, University Park, State College, PA 16802, USA
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Feng Yue
1Bioinformatics and Genomics Program, The Pennsylvania State University, University Park, State College, PA 16802, USA
2Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
3Department of Biochemistry and Molecular Biology, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA
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  • ORCID record for Feng Yue
  • For correspondence: Yue@northwestern.edu
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ABSTRACT

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).

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Posted August 20, 2019.
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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, Feng Yue
bioRxiv 739698; doi: https://doi.org/10.1101/739698
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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, Feng Yue
bioRxiv 739698; doi: https://doi.org/10.1101/739698

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