Open AccessUnderstanding spatial organizations of chromosomes via statistical analysis of Hi‐C data
Author(s) -
Hu Ming,
Deng Ke,
Qin Zhaohui,
Liu Jun S.
Publication year - 2013
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.1007/s40484-013-0016-0
Subject(s) - chromatin , spatial organization , data science , computational biology , computer science , chromosome conformation capture , function (biology) , statistical analysis , biology , transcription factor , genetics , evolutionary biology , dna , gene , statistics , mathematics , enhancer
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.