Rajput Akanksha, Reilly Colleen, Peraza Airen Zaldivar, Wang Jian, Sioson Edgar, Matt Gavriel, Paul Robin, Lu Congyu, Acic Aleksandar, Gangwani Karishma, Zhou Xin
Department of Computational Biology, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, United States.
Comput Struct Biotechnol J. 2024 Oct 2;23:3467-3471. doi: 10.1016/j.csbj.2024.09.020. eCollection 2024 Dec.
The ProteinPaint Hi-C tool (ppHiC) facilitates web-based visualization and collaborative exploration of Hi-C data, a vital resource for understanding three-dimensional genomic structures. ppHiC allows researchers to easily analyze large Hi-C datasets on a web browser without requiring the computational expertise that has heretofore limited access to this complex genomic data. The platform is compatible with multiple Hi-C data versions and boasts a highly customizable interface, including a configuration panel for the precise adjustment of key visualization parameters. The tool's interactive features offer a broad range of views, from whole-genome landscapes to detailed interactions between pairs of loci, that are accessible within a single, integrated environment. Here, we demonstrate how using ppHiC to visualize an altered chromatin conformational landscape in neuroblastoma can inform understanding of the genomic rearrangements in this cancer.
ProteinPaint Hi-C工具（ppHiC）有助于对Hi-C数据进行基于网络的可视化和协作探索，Hi-C数据是理解三维基因组结构的重要资源。ppHiC使研究人员能够在网络浏览器上轻松分析大型Hi-C数据集，而无需具备迄今为止限制对这种复杂基因组数据访问的计算专业知识。该平台与多个Hi-C数据版本兼容，并拥有高度可定制的界面，包括用于精确调整关键可视化参数的配置面板。该工具的交互功能提供了广泛的视图，从全基因组景观到基因座对之间的详细相互作用，这些都可以在单个集成环境中访问。在这里，我们展示了如何使用ppHiC可视化神经母细胞瘤中改变的染色质构象景观，从而有助于理解这种癌症中的基因组重排。
