Reconstructing spatial organizations of chromosomes through manifold learning.

5 years ago

Reconstructing spatial organizations of chromosomes through manifold learning.

Tommy Kaplan, Jian Peng, Guangxiang Zhu, Jianyang Zeng, Jinglin Yang, Sai Zhang, Hailin Hu, Rui Ma, Wenxuan Deng

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.

Publisher URL: http://doi.org/10.1093/nar/gky065

DOI: 10.1093/nar/gky065