Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data.
Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data.
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DOI：
10.1007/s40484-013-0016-0
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发表时间：
2013-06
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了解染色体如何折叠提供了对转录调控的见解，因此，细胞的功能状态。使用下一代测序技术，最近开发的Hi-C方法使核中空间染色质组织的全局视图成为可能，这大大扩展了我们对基因组组织和功能的了解。然而，由于Hi-C实验协议中隐藏的多层偏差，噪声和不确定性，分析和解释Hi-C数据带来了巨大的挑战，需要开发新的统计方法。本文概述了最近的Hi-C研究及其对生物医学研究的影响，描述了Hi-C数据统计分析的主要挑战，并讨论了未来研究的一些观点。
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.