Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways.
Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways.
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DOI：
10.1371/journal.pcbi.1004321
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发表时间：
2015-08
影响因子：
4.3
通讯作者：
Palsson BO
中科院分区：
文献类型：
--
作者：
King ZA;Dräger A;Ebrahim A;Sonnenschein N;Lewis NE;Palsson BO
生物学数据可视化是一个用于可视化生物学途径数据的Web应用程序。三个关键特性使Quebrator成为路径可视化的独特有效工具。首先，用户可以快速设计新的路径图。Quebrator基于用户数据和基因组规模模型提供路径建议，因此用户可以半自动化的方式绘制路径。其次，用户可以使用定义哪些酶催化每个反应的规则，可视化与相关反应和途径上的基因或蛋白质相关的数据。因此，用户可以识别常见基因组数据类型（例如RNA-Seq，蛋白质组学，ChIP）的趋势-结合代谢物和反应导向的数据类型（例如代谢组学，通量组学）。第三，Weber利用Web技术（SVG，D3，开发工具）的优势，使可视化可以快速适应，扩展，共享和嵌入。本文提供了这些功能的例子，并解释了如何使用的开发方法用于浏览器可以用来指导未来的可视化工具的开发。我们现在处于大数据时代。生物学发现比以往任何时候都更需要强大而灵活的工具来管理大型数据集，包括可视化和统计工具。基于途径的可视化特别强大，因为它使人们能够在实际生物过程的背景下分析复杂的数据集，并阐明细胞中的每个变化如何影响相关过程。为了促进这种方法，我们提出了一个Web应用程序，可以用来快速构建路径图。在生物地图上，与基因、反应和代谢物相关的各种数据集可以快速地在代谢中进行情境化，并且越来越多地超越代谢。现在可以在https://escher.github.io上免费使用（根据MIT许可证）。
Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools. We are now in the age of big data. More than ever before, biological discoveries require powerful and flexible tools for managing large datasets, including both visual and statistical tools. Pathway-based visualization is particularly powerful since it enables one to analyze complex datasets within the context of actual biological processes and to elucidate how each change in a cell effects related processes. To facilitate such approaches, we present Escher, a web application that can be used to rapidly build pathway maps. On Escher maps, diverse datasets related to genes, reactions, and metabolites can be quickly contextualized within metabolism and, increasingly, beyond metabolism. Escher is available now for free use (under the MIT license) at https://escher.github.io.