The ability to identify regulatory interactions that mediate gene expression changes through distal elements, such as risk loci, is transforming our understanding of how genomes are spatially organized and regulated. Capture Hi-C (CHi-C) is a powerful tool to delineate such regulatory interactions. However, primary analysis and downstream interpretation of CHi-C profiles remains challenging and relies on disparate tools with ad-hoc input/output formats and specific assumptions for statistical modeling. Here we present a data processing and interaction calling toolkit (CHiCANE), specialized for the analysis and meaningful interpretation of CHi-C assays. In this protocol, we demonstrate applications of CHiCANE to region capture Hi-C (rCHi-C) and promoter capture Hi-C (pCHi-C) libraries, followed by quality assessment of interaction peaks, as well as downstream analysis specific to rCHi-C and pCHi-C to aid functional interpretation. For a typical rCHi-C/pCHi-C dataset this protocol takes up to 3 d for users with a moderate understanding of R programming and statistical concepts, although this is dependent on dataset size and compute power available. CHiCANE is freely available at https://cran.r-project.org/web/packages/chicane.

识别通过远端元件（如风险基因座）介导基因表达变化的调控相互作用的能力正在改变我们对基因组空间组织和调控方式的理解。Capture Hi-C (CHi-C) 是描述此类监管相互作用的强大工具。然而，CHi-C 配置文件的初步分析和下游解释仍然具有挑战性，并且依赖于具有特殊输入/输出格式和统计建模特定假设的不同工具。在这里，我们提出了一个数据处理和交互调用工具包 (CHiCANE)，专门用于 CHi-C 分析的分析和有意义的解释。在本协议中，我们展示了 CHiCANE 在区域捕获 Hi-C (rCHi-C) 和启动子捕获 Hi-C (pCHi-C) 库中的应用，然后对相互作用峰进行质量评估，以及特定于 rCHi-C 和 pCHi-C 的下游分析，以帮助功能解释。对于典型的 rCHi-C/pCHi-C 数据集，对于对 R 编程和统计概念有一定了解的用户，此协议最多需要 3 天，尽管这取决于数据集大小和可用的计算能力。CHiCANE 可在 https://cran.r-project.org/web/packages/chicane 免费获得。