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.
Bibliographical noteFunding Information:
National Natural Science Foundation of China [61472205, 81630103]; China’s Youth 1000-Talent Program; Beijing Advanced Innovation Center for Structural Biology; NCSA Faculty fellowship 2017; Israeli Center of Excellence (I-CORE) for Chromatin and RNA in Gene Regulation [1796/12]; Israel Science Foundation [913/15]. T.K. is a member of the Israeli Center of Excellence (I-CORE) for Gene Regulation in Complex Human Disease [41/11]. J.P. is funded by a Sloan Research Fellowship and NSF Career Award 1652815. Funding for open access charge: China’s Youth 1000-Talent Program. Conflict of interest statement. None declared.
© The Author(s) 2018..