Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
J Mol Diagn. 2019 Nov;21(6):985-993. doi: 10.1016/j.jmoldx.2019.06.005. Epub 2019 Aug 2.
Visualization-driven data exploration is a highly effective modality for interpreting and discovering insights from high-throughput genomics data sets; however, it is vastly underutilized in routine workflows in clinical and translation settings. We have developed three open-source, browser-based, interactive genomics data visualization widgets that can be used as intuitive stand-alone applications or integrated with existing web-based laboratory information solutions. The widgets were developed in JavaScript using the D3.js library. These widgets run in any modern web browser across desktop and mobile devices for easy accessibility but are designed for client-side data processing to address data privacy concerns. jsProteinMapper plots the location of a variant of interest relative to the protein domains and multiple variant databases, assisting with clinical interpretation of sequence variants. jsComut generates a highly interactive and customizable comutation plot for visual exploration of genomic data sets with clinicopathologic annotations to reveal unique molecular profiles and clinical correlates. jsCodonWheel is an interactive version of the ubiquitous circular codon-to-amino acid translation table, which lets users quickly map nucleotide changes onto resulting amino acid differences. These open-source visualization tools may improve some of the key laboratory workflows that involve the review of large-scale genomics data sets in a high-volume setting. The intuitive and responsive user interface, highly customizable visualizations, and easy integration with existing web-based laboratory software are significant highlights of these tools.
可视化驱动的数据探索是一种从高通量基因组数据集解释和发现见解的高效方式;然而,在临床和转化环境中的常规工作流程中,它的应用非常有限。我们开发了三个开源的、基于浏览器的、交互式基因组学数据可视化小部件,可以作为直观的独立应用程序使用,也可以与现有的基于网络的实验室信息解决方案集成。这些小部件是使用 D3.js 库在 JavaScript 中开发的。这些小部件可以在任何现代网络浏览器中运行,无论是在桌面设备还是移动设备上,都易于访问,但它们是为客户端数据处理而设计的,以解决数据隐私问题。jsProteinMapper 可以根据蛋白质结构域和多个变体数据库,将感兴趣的变体的位置绘制成图,帮助临床医生解释序列变体。jsComut 生成了一个高度交互和可定制的计算图,用于可视化探索带有临床病理注释的基因组数据集,以揭示独特的分子特征和临床相关性。jsCodonWheel 是一个交互式的通用圆形密码子到氨基酸翻译表,可以让用户快速将核苷酸变化映射到相应的氨基酸差异。这些开源可视化工具可以改进一些关键的实验室工作流程,这些工作流程涉及在大容量设置中审查大规模基因组数据集。直观的响应式用户界面、高度可定制的可视化以及与现有的基于网络的实验室软件的轻松集成是这些工具的显著亮点。
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