Metabolism Unit, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America.
PLoS One. 2013;8(1):e53388. doi: 10.1371/journal.pone.0053388. Epub 2013 Jan 7.
With the development of increasingly large and complex genomic and proteomic data sets, an enhancement in the complexity of available Venn diagram analytical programs is becoming increasingly important. Current freely available Venn diagram programs often fail to represent extra complexity among datasets, such as regulation pattern differences between different groups. Here we describe the development of VennPlex, a program that illustrates the often diverse numerical interactions among multiple, high-complexity datasets, using up to four data sets. VennPlex includes versatile output features, where grouped data points in specific regions can be easily exported into a spreadsheet. This program is able to facilitate the analysis of two to four gene sets and their corresponding expression values in a user-friendly manner. To demonstrate its unique experimental utility we applied VennPlex to a complex paradigm, i.e. a comparison of the effect of multiple oxygen tension environments (1-20% ambient oxygen) upon gene transcription of primary rat astrocytes. VennPlex accurately dissects complex data sets reliably into easily identifiable groups for straightforward analysis and data output. This program, which is an improvement over currently available Venn diagram programs, is able to rapidly extract important datasets that represent the variety of expression patterns available within the data sets, showing potential applications in fields like genomics, proteomics, and bioinformatics.
随着基因组和蛋白质组数据越来越大、越来越复杂,提高可用的 Venn 图分析程序的复杂性变得越来越重要。目前,免费提供的 Venn 图程序往往无法表示数据集之间的额外复杂性,例如不同组之间的调控模式差异。在这里,我们描述了 VennPlex 的开发,这是一个程序,可以用多达四个数据集来展示多个高复杂度数据集之间的经常出现的不同数值相互作用。VennPlex 具有多种灵活的输出功能,其中特定区域的分组数据点可以轻松导出到电子表格中。该程序能够以用户友好的方式方便地分析两个到四个基因集及其相应的表达值。为了展示其独特的实验应用,我们将 VennPlex 应用于一个复杂的范例,即比较多个氧张力环境(1-20%环境氧)对原代大鼠星形胶质细胞基因转录的影响。VennPlex 可以可靠地将复杂的数据集准确地分割成易于识别的组,以便进行简单的分析和数据输出。与目前可用的 Venn 图程序相比,该程序能够快速提取代表数据集内可用表达模式多样性的重要数据集,在基因组学、蛋白质组学和生物信息学等领域具有潜在应用。