Journal
NUCLEIC ACIDS RESEARCH
Volume 46, Issue 8, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gky065
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Funding
- National Natural Science Foundation of China [61472205, 81630103]
- China's Youth 1000-Talent Program
- Beijing Advanced Innovation Center for Structural Biology
- NCSA Faculty fellowship
- Israeli Center of Excellence (I CORE) for Chromatin and RNA in Gene Regulation [1796/12]
- Israel Science Foundation [913/15]
- Israeli Center of Excellence (I-CORE) for Gene Regulation in Complex Human Disease [41/11]
- Sloan Research Fellowship
- NSF [1652815]
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1652815] Funding Source: National Science Foundation
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Decoding the spatial organizations of chromosomes has crucial implications for studying eukaryotic gene regulation. Recently, chromosomal conformation capture based technologies, such as Hi-C, have been widely used to uncover the interaction frequencies of genomic loci in a high-throughput and genome-wide manner and provide new insights into the folding of three-dimensional (3D) genome structure. In this paper, we develop a novel manifold learning based framework, called GEM (Genomic organization reconstructor based on conformational Energy and Manifold learning), to reconstruct the three-dimensional organizations of chromosomes by integrating Hi-C data with biophysical feasibility. Unlike previous methods, which explicitly assume specific relationships between Hi-C interaction frequencies and spatial distances, our model directly embeds the neighboring affinities from Hi-C space into 3D Euclidean space. Extensive validations demonstrated that GEM not only greatly outperformed other state-of-art modeling methods but also provided a physically and physiologically valid 3D representations of the organizations of chromosomes. Furthermore, we for the first time apply the modeled chromatin structures to recover long-range genomic interactions missing from original Hi-C data.
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