Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data

• Type of Material: Article
• Authors: Ming Hu, Harvard University; Ke Deng, Harvard University; Zhaohui Qin, Emory University; Jun S. Liu, Harvard University
• Language: English
• Date: 2013-06-01
• Publisher: Springer Verlag (Germany)
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2013, Higher Education Press and Springer-Verlag GmbH.
• Final Published Version (URL): http://dx.doi.org/10.1007/s40484-013-0016-0
• Title of Journal or Parent Work: Quantitative Biology
• ISSN: 2095-4689
• Volume: 1
• Issue: 2
• Start Page: 156
• End Page: 174
• Grant/Funding Information: This research was supported in part by the NSF grant (No. DMS-1120368); and NIH grants (Nos. R01-HG02518-02 and R01-HG005119).
• Research Categories: Biology, Biostatistics

Abstract

Understanding how chromosomes fold provides insights into the transcription regulation, hence, the functional state of the cell. Using the next generation sequencing technology, the recently developed Hi-C approach enables a global view of spatial chromatin organization in the nucleus, which substantially expands our knowledge about genome organization and function. However, due to multiple layers of biases, noises and uncertainties buried in the protocol of Hi-C experiments, analyzing and interpreting Hi-C data poses great challenges, and requires novel statistical methods to be developed. This article provides an overview of recent Hi-C studies and their impacts on biomedical research, describes major challenges in statistical analysis of Hi-C data, and discusses some perspectives for future research.