使用 CustardPy 分析和可视化多组 Hi-C 和 Micro-C 数据。

Analysis and Visualization of Multiple Hi-C and Micro-C Data with CustardPy.

机构信息

Laboratory of Computational Genomics, Institute for Quantitative Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.

出版信息

Methods Mol Biol. 2025;2856:223-238. doi: 10.1007/978-1-0716-4136-1_13.

DOI:10.1007/978-1-0716-4136-1_13

PMID:39283455

Abstract

Three-dimensional (3D) genome structure plays crucial roles in biological processes and disease pathogenesis. Hi-C and Micro-C, well-established methods for 3D genome analysis, can identify a variety of 3D genome structures. However, selecting appropriate pipelines and tools for the analysis and setting up the required computing environment can sometimes pose challenges. To address this, we have introduced CustardPy, a Docker-based pipeline specifically designed for 3D genome analysis. CustardPy is designed to compare and evaluate multiple samples and wraps several existing tools to cover the entire workflow from FASTQ mapping to visualization. In this chapter, we demonstrate how to analyze and visualize Hi-C data using CustardPy and introduce several 3D genome features observed in Hi-C data.

摘要

三维（3D）基因组结构在生物过程和疾病发病机制中起着至关重要的作用。Hi-C 和 Micro-C 是用于 3D 基因组分析的成熟方法，可以识别各种 3D 基因组结构。然而，选择适当的分析管道和工具以及设置所需的计算环境有时可能会带来挑战。为了解决这个问题，我们引入了 CustardPy，这是一个基于 Docker 的专门用于 3D 基因组分析的管道。CustardPy 旨在比较和评估多个样本，并包装了几个现有的工具，以涵盖从 FASTQ 映射到可视化的整个工作流程。在本章中，我们将演示如何使用 CustardPy 分析和可视化 Hi-C 数据，并介绍在 Hi-C 数据中观察到的几个 3D 基因组特征。