Fang Kun, Wang Junbai, Liu Lu, Jin Victor X
Institute for Health and Equity, MCW Cancer Center, and Mellowes Center for Genome Science and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, USA.
Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, Lørenskog, Norway.
Comput Struct Biotechnol J. 2022 Jul 26;20:3955-3962. doi: 10.1016/j.csbj.2022.07.037. eCollection 2022.
With ever-growing genomic sequencing data, the data variabilities and the underlying biases of the sequencing technologies pose significant computational challenges ranging from the need for accurately detecting the nucleosome positioning or chromatin interaction to the need for developing normalization methods to eliminate systematic biases. This review mainly surveys the computational methods for mapping the higher-resolution nucleosome and higher-order chromatin architectures. While a detailed discussion of the underlying algorithms is beyond the scope of our survey, we have discussed the methods and tools that can detect the nucleosomes in the genome, then demonstrated the computational methods for identifying 3D chromatin domains and interactions. We further illustrated computational approaches for integrating multi-omics data with Hi-C data and the advance of single-cell (sc)Hi-C data analysis. Our survey provides a comprehensive and valuable resource for biomedical scientists interested in studying nucleosome organization and chromatin structures as well as for computational scientists who are interested in improving upon them.
随着基因组测序数据的不断增长，测序技术的数据变异性和潜在偏差带来了重大的计算挑战，从准确检测核小体定位或染色质相互作用的需求到开发归一化方法以消除系统偏差的需求。本综述主要概述了用于绘制高分辨率核小体和高阶染色质结构的计算方法。虽然对基础算法的详细讨论超出了我们综述的范围，但我们讨论了可在基因组中检测核小体的方法和工具，然后展示了用于识别三维染色质结构域和相互作用的计算方法。我们进一步阐述了将多组学数据与Hi-C数据整合的计算方法以及单细胞（sc）Hi-C数据分析的进展。我们的综述为有兴趣研究核小体组织和染色质结构的生物医学科学家以及有兴趣改进这些方法的计算科学家提供了全面且有价值的资源。
