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Probing multi-way chromatin interaction with hypergraph representation learning

View ORCID ProfileRuochi Zhang, View ORCID ProfileJian Ma
doi: https://doi.org/10.1101/2020.01.22.916171
Ruochi Zhang
1Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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Jian Ma
1Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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  • For correspondence: jianma@cs.cmu.edu
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Abstract

Advances in high-throughput mapping of 3D genome organization have enabled genome-wide characterization of chromatin interactions. However, proximity ligation based mapping approaches for pairwise chromatin interaction such as Hi-C cannot capture multi-way interactions, which are informative to delineate higher-order genome organization and gene regulation mechanisms at single-nucleus resolution. The very recent development of ligation-free chromatin interaction mapping methods such as SPRITE and ChIA-Drop has offered new opportunities to uncover simultaneous interactions involving multiple genomic loci within the same nuclei. Unfortunately, methods for analyzing multi-way chromatin interaction data are significantly underexplored. Here we develop a new computational method, called MATCHA, based on hypergraph representation learning where multi-way chromatin interactions are represented as hyperedges. Applications to SPRITE and ChIA-Drop data suggest that MATCHA is effective to denoise the data and make de novo predictions of multi-way chromatin interactions, reducing the potential false positives and false negatives from the original data. We also show that MATCHA is able to distinguish between multi-way interaction in a single nucleus and combination of pairwise interactions in a cell population. In addition, the embeddings from MATCHA reflect 3D genome spatial localization and function. MATCHA provides a promising framework to significantly improve the analysis of multi-way chromatin interaction data and has the potential to offer unique insights into higher-order chromosome organization and function.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 23, 2020.
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Probing multi-way chromatin interaction with hypergraph representation learning
Ruochi Zhang, Jian Ma
bioRxiv 2020.01.22.916171; doi: https://doi.org/10.1101/2020.01.22.916171
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Probing multi-way chromatin interaction with hypergraph representation learning
Ruochi Zhang, Jian Ma
bioRxiv 2020.01.22.916171; doi: https://doi.org/10.1101/2020.01.22.916171

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