Metabolism Unit, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America.
PLoS One. 2012;7(5):e36911. doi: 10.1371/journal.pone.0036911. Epub 2012 May 14.
As pharmacological data sets become increasingly large and complex, new visual analysis and filtering programs are needed to aid their appreciation. One of the most commonly used methods for visualizing biological data is the Venn diagram. Currently used Venn analysis software often presents multiple problems to biological scientists, in that only a limited number of simultaneous data sets can be analyzed. An improved appreciation of the connectivity between multiple, highly-complex datasets is crucial for the next generation of data analysis of genomic and proteomic data streams. We describe the development of VENNTURE, a program that facilitates visualization of up to six datasets in a user-friendly manner. This program includes versatile output features, where grouped data points can be easily exported into a spreadsheet. To demonstrate its unique experimental utility we applied VENNTURE to a highly complex parallel paradigm, i.e. comparison of multiple G protein-coupled receptor drug dose phosphoproteomic data, in multiple cellular physiological contexts. VENNTURE was able to reliably and simply dissect six complex data sets into easily identifiable groups for straightforward analysis and data output. Applied to complex pharmacological datasets, VENNTURE's improved features and ease of analysis are much improved over currently available Venn diagram programs. VENNTURE enabled the delineation of highly complex patterns of dose-dependent G protein-coupled receptor activity and its dependence on physiological cellular contexts. This study highlights the potential for such a program in fields such as pharmacology, genomics, and bioinformatics.
随着药理学数据集变得越来越大且复杂,需要新的视觉分析和筛选程序来帮助理解这些数据。目前,最常用于可视化生物数据的方法之一是 Venn 图。然而,目前使用的 Venn 分析软件通常会给生物科学家带来多个问题,因为它只能分析有限数量的同时数据集。为了对基因组和蛋白质组数据流的下一代数据分析有更深入的了解,必须改进对多个高度复杂数据集之间的连接性的理解。我们描述了 VENNTURE 程序的开发,该程序以用户友好的方式方便地可视化多达六个数据集。该程序具有多种灵活的输出功能,其中可以轻松地将分组数据点导出到电子表格中。为了证明其独特的实验实用性,我们将 VENNTURE 应用于高度复杂的平行范例,即比较多个 G 蛋白偶联受体药物剂量磷酸蛋白质组学数据,在多个细胞生理环境中。VENNTURE 能够可靠且简单地将六个复杂数据集分解为易于识别的组,以便进行直接分析和数据输出。将 VENNTURE 应用于复杂的药理学数据集,其分析功能和易用性都得到了显著提高,优于目前可用的 Venn 图程序。VENNTURE 能够描绘出高度复杂的依赖于剂量的 G 蛋白偶联受体活性模式及其对生理细胞环境的依赖性。这项研究强调了该程序在药理学、基因组学和生物信息学等领域的潜力。