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Quantitative Biology

, Volume 1, Issue 2, pp 156–174 | Cite as

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

  • Ming Hu
  • Ke Deng
  • Zhaohui Qin
  • Jun S. LiuEmail author
Review

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.

Keywords

Polymer Model Chromatin Interaction Topological Domain Chromosome Conformation Capture Biophysical Principle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Higher Education Press and Springer-Verlag GmbH 2013

Authors and Affiliations

  1. 1.Department of StatisticsHarvard UniversityCambridgeUSA
  2. 2.Mathematical Sciences CenterTsinghua UniversityBeijingChina
  3. 3.Department of Biostatistics and Bioinformatics, Rollins School of Public HealthEmory UniversityAtlantaUSA

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