title: Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data.
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.
authors.affiliation: Department of Statistics, Harvard University, Cambridge, MA 02138, USA.
authors.forename: Ming
authors.initials: M
authors.lastname: Hu
authors.affiliation: Department of Statistics, Harvard University, Cambridge, MA 02138, USA ; Mathematical Sciences Center, Tsinghua University, Beijing 100084, China.
authors.forename: Ke
authors.initials: K
authors.lastname: Deng
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
authors.forename: Zhaohui
authors.initials: Z
authors.lastname: Qin
authors.affiliation: Department of Statistics, Harvard University, Cambridge, MA 02138, USA.
authors.forename: Jun S
authors.initials: JS
authors.lastname: Liu