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.

Capture Hi-C 广泛用于获取染色体相互作用的高分辨率图谱，至少在一端涉及感兴趣的区域，例如基因启动子。捕获 Hi-C 数据中的信号检测具有挑战性，并且无法使用为其他染色体构象捕获方法（包括标准 Hi-C）开发的工具充分完成。 Capture Hi-C 基因组组织分析 (CHiCAGO) 是专为 Capture Hi-C 分析而开发的计算流程。它实现了针对生物和技术背景成分的统计模型，以及针对该数据类型的定制标准化和多重测试程序。在这里，我们提供了 CHiCAGO 工作流程的分步指南，面向具有命令行和 R 基本经验的用户。我们还描述了用于调整自定义实验关键参数的更高级策略，并提供了数据预处理和数据处理方面的指导。使用配套工具进行下游分析。在典型的实验中，CHiCAGO 需要约 2-3 小时才能运行，但预处理和后处理步骤可能需要更长的时间。