Ay Ferhat, Noble William S
Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA.
Feinberg School of Medicine, Northwestern University, Chicago, 60661, IL, USA.
Genome Biol. 2015 Sep 2;16:183. doi: 10.1186/s13059-015-0745-7.
The rapidly increasing quantity of genome-wide chromosome conformation capture data presents great opportunities and challenges in the computational modeling and interpretation of the three-dimensional genome. In particular, with recent trends towards higher-resolution high-throughput chromosome conformation capture (Hi-C) data, the diversity and complexity of biological hypotheses that can be tested necessitates rigorous computational and statistical methods as well as scalable pipelines to interpret these datasets. Here we review computational tools to interpret Hi-C data, including pipelines for mapping, filtering, and normalization, and methods for confidence estimation, domain calling, visualization, and three-dimensional modeling.
全基因组染色体构象捕获数据量的迅速增加,在三维基因组的计算建模和解释方面带来了巨大的机遇和挑战。特别是,随着近期出现的更高分辨率高通量染色体构象捕获(Hi-C)数据的趋势,可测试的生物学假设的多样性和复杂性使得需要严格的计算和统计方法以及可扩展的流程来解释这些数据集。在这里,我们回顾了用于解释Hi-C数据的计算工具,包括用于映射、过滤和归一化的流程,以及用于置信度估计、结构域识别、可视化和三维建模的方法。