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

作     者：Ming Hu Ke Deng Zhaohui Qin Jun S. Liu 

作者机构：Department of Statistics Harvard University Cambridge MA 02138 USA Mathematical Sciences Center Tsinghua University Beijing 100084 China Department of Biostatistics and Bioinformatics Rollins School of Public Health Emory University Atlanta GA 30322 USA 

出 版 物：《Frontiers of Electrical and Electronic Engineering in China》 (中国电气与电子工程前沿（英文版）)

年 卷 期：2013年第8卷第2期

页      面：156-174页

学科分类：0710[理学-生物学] 02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 09[农学] 071007[理学-遗传学] 0714[理学-统计学（可授理学、经济学学位）] 0901[农学-作物学] 070103[理学-概率论与数理统计] 090102[农学-作物遗传育种] 0701[理学-数学] 

基　　金：National Science Foundation, NSF, (DMS-1120368) National Institutes of Health, NIH, (R01-HG005119, R01-HG02518-02) 

主　　题：Polymer Model Chromatin Interaction Topological Domain Chromosome Conformation Capture Biophysical Principle 

摘      要：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.