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

被引：0
作者：
Paula Freire-Pritchett
Helen Ray-Jones
Monica Della Rosa
Chris Q. Eijsbouts
William R. Orchard
Steven W. Wingett
Chris Wallace
Jonathan Cairns
Mikhail Spivakov
Valeriya Malysheva
机构：
[1] MRC Laboratory of Molecular Biology,Cell Biology Division
[2] MRC London Institute of Medical Sciences,Functional Gene Control Group, Epigenetics Section
[3] Imperial College London,Institute of Clinical Sciences, Faculty of Medicine
[4] University of Oxford,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery
[5] Wellcome Centre for Human Genetics,Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus
[6] University of Oxford,MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health
[7] Cancer Research UK Cambridge Research Institute,Cell Biology Division
[8] Bioinformatics,undefined
[9] The Babraham Institute,undefined
[10] University of Cambridge,undefined
[11] Forvie Site,undefined
[12] Robinson Way,undefined
[13] The Babraham Institute,undefined
[14] MRC Laboratory of Molecular Biology,undefined
来源：
Nature Protocols | 2021年 / 16卷
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摘要：
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 ~2–3 h to run, although pre- and postprocessing steps may take much longer.
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页码：4144 / 4176
页数：32
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