Reconstructing spatial organizations of chromosomes through manifold learning. Issue 8 (2nd February 2018)

• Record Type: Journal Article
• Authors: Zhu, Guangxiang; Deng, Wenxuan; Hu, Hailin; Ma, Rui; Zhang, Sai; Yang, Jinglin; Peng, Jian; Kaplan, Tommy; Zeng, Jianyang
• Is Part Of: Nucleic acids research. Volume 46:Issue 8(2018)
• Page Start: e50
• Page End: e50
• Publication Date: 2018-02-02
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gky065
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: 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 (G enomic organization reconstructor based on conformational E nergy and M anifold 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.