Detecting chromosomal interactions in Capture Hi-C data with CHiCAGO and companion tools

被引:16
作者
Freire-Pritchett, Paula [1 ]
Ray-Jones, Helen [2 ,3 ]
Della Rosa, Monica [2 ,3 ]
Eijsbouts, Chris Q. [4 ,5 ]
Orchard, William R. [6 ]
Wingett, Steven W. [7 ,11 ]
Wallace, Chris [8 ,9 ]
Cairns, Jonathan [10 ]
Spivakov, Mikhail [2 ,3 ]
Malysheva, Valeriya [2 ,3 ]
机构
[1] MRC Lab Mol Biol, Cell Biol Div, Cambridge, England
[2] MRC London Inst Med Sci, Epigenet Sect, Funct Gene Control Grp, London, England
[3] Imperial Coll London, Fac Med, Inst Clin Sci, London, England
[4] Univ Oxford, Li Ka Shing Ctr Hlth Informat & Discovery, Big Data Inst, Oxford, England
[5] Univ Oxford, Wellcome Ctr Human Genet, Oxford, England
[6] Canc Res UK Cambridge Res Inst, Cambridge, England
[7] Babraham Inst, Bioinformat, Cambridge, England
[8] Univ Cambridge, Jeffrey Cheah Biomed Ctr, Cambridge Inst Therapeut Immunol & Infect Dis CIT, Cambridge Biomed Campus, Cambridge, England
[9] Cambridge Inst Publ Hlth, MRC Biostat Unit, Cambridge Biomed Campus,Forvie Site,Robinson Way, Cambridge, England
[10] Babraham Inst, Cambridge, England
[11] MRC Lab Mol Biol, Biol Div, Cambridge, England
基金
英国医学研究理事会; 英国惠康基金; 英国生物技术与生命科学研究理事会;
关键词
REGULATORY LANDSCAPES; PROMOTER CONTACTS; HIGH-RESOLUTION; CELLS; ORGANIZATION; PRINCIPLES; HUNDREDS; DOMAINS; LOCI;
D O I
10.1038/s41596-021-00567-5
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
This protocol describes a complete workflow for detecting significant contacts in Capture Hi-C data, including preprocessing, interaction calling and downstream analyses, based on the CHiCAGO pipeline and companion tools. Capture Hi-C is widely used to obtain high-resolution profiles of chromosomal interactions involving, at least on one end, regions of interest such as gene promoters. Signal detection in Capture Hi-C data is challenging and cannot be adequately accomplished with tools developed for other chromosome conformation capture methods, including standard Hi-C. Capture Hi-C Analysis of Genomic Organization (CHiCAGO) is a computational pipeline developed specifically for Capture Hi-C analysis. It implements a statistical model accounting for biological and technical background components, as well as bespoke normalization and multiple testing procedures for this data type. Here we provide a step-by-step guide to the CHiCAGO workflow that is aimed at users with basic experience of the command line and R. We also describe more advanced strategies for tuning the key parameters for custom experiments and provide guidance on data preprocessing and downstream analysis using companion tools. In a typical experiment, CHiCAGO takes similar to 2-3 h to run, although pre- and postprocessing steps may take much longer.
引用
收藏
页码:4144 / +
页数:39
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