Suppr超能文献

R 教程:从多个 Hi-C 数据集中检测差异相互作用的染色质区域

R Tutorial: Detection of Differentially Interacting Chromatin Regions From Multiple Hi-C Datasets.

作者信息

Stansfield John C, Tran Duc, Nguyen Tin, Dozmorov Mikhail G

机构信息

Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia.

Department of Computer Science & Engineering, University of Nevada, Reno, Nevada.

出版信息

Curr Protoc Bioinformatics. 2019 Jun;66(1):e76. doi: 10.1002/cpbi.76. Epub 2019 May 24.

Abstract

The three-dimensional (3D) interactions of chromatin regulate cell-type-specific gene expression, recombination, X-chromosome inactivation, and many other genomic processes. High-throughput chromatin conformation capture (Hi-C) technologies capture the structure of the chromatin on a global scale by measuring all-vs.-all interactions and can provide new insights into genomic regulation. The workflow presented here describes how to analyze and interpret a comparative Hi-C experiment. We describe the process of obtaining Hi-C data from public repositories and give suggestions for pre-processing pipelines for users who intend to analyze their own raw data. We then describe the data normalization and comparative analysis process. We present three protocols describing the use of the multiHiCcompare, diffHic, and FIND R packages, respectively, to perform a comparative analysis of Hi-C experiments. Finally, visualization of the results and downstream interpretation of the differentially interacting regions are discussed. The bulk of this tutorial uses the R programming environment, and the processes described can be performed with most operating systems and a single computer. © 2019 by John Wiley & Sons, Inc.

摘要

染色质的三维(3D)相互作用调控细胞类型特异性基因表达、重组、X染色体失活以及许多其他基因组过程。高通量染色质构象捕获(Hi-C)技术通过测量全基因组范围内的相互作用来捕获染色质结构,并能为基因组调控提供新的见解。本文介绍的工作流程描述了如何分析和解读比较性Hi-C实验。我们阐述了从公共数据库获取Hi-C数据的过程,并为打算分析自身原始数据的用户提供预处理流程建议。接着,我们描述了数据归一化和比较分析过程。我们分别介绍了三个方案,描述了如何使用multiHiCcompare、diffHic和FIND R软件包对Hi-C实验进行比较分析。最后,讨论了结果的可视化以及差异相互作用区域的下游解读。本教程主要使用R编程环境,所述过程在大多数操作系统和单台计算机上均可执行。© 2019约翰威立国际出版公司

相似文献

1
R Tutorial: Detection of Differentially Interacting Chromatin Regions From Multiple Hi-C Datasets.
Curr Protoc Bioinformatics. 2019 Jun;66(1):e76. doi: 10.1002/cpbi.76. Epub 2019 May 24.
2
HiCcompare: an R-package for joint normalization and comparison of HI-C datasets.
BMC Bioinformatics. 2018 Jul 31;19(1):279. doi: 10.1186/s12859-018-2288-x.
3
multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments.
Bioinformatics. 2019 Sep 1;35(17):2916-2923. doi: 10.1093/bioinformatics/btz048.
4
FreeHi-C spike-in simulations for benchmarking differential chromatin interaction detection.
Methods. 2021 May;189:3-11. doi: 10.1016/j.ymeth.2020.07.001. Epub 2020 Jul 12.
5
diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C data.
BMC Bioinformatics. 2015 Aug 19;16:258. doi: 10.1186/s12859-015-0683-0.
7
DiffGR: Detecting Differentially Interacting Genomic Regions from Hi-C Contact Maps.
Genomics Proteomics Bioinformatics. 2024 Jul 3;22(2). doi: 10.1093/gpbjnl/qzae028.
8
Measuring significant changes in chromatin conformation with ACCOST.
Nucleic Acids Res. 2020 Mar 18;48(5):2303-2311. doi: 10.1093/nar/gkaa069.
9
Computational Analysis of Hi-C Data.
Methods Mol Biol. 2021;2157:103-125. doi: 10.1007/978-1-0716-0664-3_7.
10
Detecting chromosomal interactions in Capture Hi-C data with CHiCAGO and companion tools.
Nat Protoc. 2021 Sep;16(9):4144-4176. doi: 10.1038/s41596-021-00567-5. Epub 2021 Aug 9.

引用本文的文献

1
Pore-C sequencing identifies episome-driven chromosome conformation perturbations differentiating pneumococcal epigenetic variants.
PLoS Pathog. 2025 Aug 14;21(8):e1013392. doi: 10.1371/journal.ppat.1013392. eCollection 2025 Aug.
3
Methods for the Differential Analysis of Hi-C Data.
Methods Mol Biol. 2022;2301:61-95. doi: 10.1007/978-1-0716-1390-0_4.

本文引用的文献

1
Removing unwanted variation between samples in Hi-C experiments.
Brief Bioinform. 2024 Mar 27;25(3). doi: 10.1093/bib/bbae217.
2
multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments.
Bioinformatics. 2019 Sep 1;35(17):2916-2923. doi: 10.1093/bioinformatics/btz048.
3
HiCcompare: an R-package for joint normalization and comparison of HI-C datasets.
BMC Bioinformatics. 2018 Jul 31;19(1):279. doi: 10.1186/s12859-018-2288-x.
4
FIND: difFerential chromatin INteractions Detection using a spatial Poisson process.
Genome Res. 2018 Mar 1;28(3):412-422. doi: 10.1101/gr.212241.116.
5
Multiscale 3D Genome Rewiring during Mouse Neural Development.
Cell. 2017 Oct 19;171(3):557-572.e24. doi: 10.1016/j.cell.2017.09.043.
6
Cohesin Loss Eliminates All Loop Domains.
Cell. 2017 Oct 5;171(2):305-320.e24. doi: 10.1016/j.cell.2017.09.026.
7
Juicer Provides a One-Click System for Analyzing Loop-Resolution Hi-C Experiments.
Cell Syst. 2016 Jul;3(1):95-8. doi: 10.1016/j.cels.2016.07.002.
8
GenomeRunner web server: regulatory similarity and differences define the functional impact of SNP sets.
Bioinformatics. 2016 Aug 1;32(15):2256-63. doi: 10.1093/bioinformatics/btw169. Epub 2016 Apr 1.
10
HiC-Pro: an optimized and flexible pipeline for Hi-C data processing.
Genome Biol. 2015 Dec 1;16:259. doi: 10.1186/s13059-015-0831-x.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验