CAT PETR：用于磷酸化和表达数据差异分析的图形用户界面

IF 0.9 4区数学 Q3 Mathematics

Statistical Applications in Genetics and Molecular Biology Pub Date : 2023-01-01 DOI:10.1101/2023.01.04.522665

Keegan Flanagan, S. Pelech, Y. Av‐Gay, K. Dao Duc

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引用次数: 0

摘要

摘要抗体微阵列数据提供了一种强大的高通量工具来监测细胞对扰动或基因操纵反应的整体变化。然而，尽管收集此类数据变得越来越容易获得，但由于缺乏特定的计算工具，其分析受到限制。在这里，我们介绍了CAT PETR，一个用户友好的网络应用程序，用于通过抗体微阵列收集的表达和磷酸化数据的差异分析。我们的应用程序通过提供各种数据输入选项和可视化来解决其他基于GUI的工具的局限性。为了说明其在真实数据上的能力，我们展示了CAT PETR既复制了以前的发现，又利用其先进的可视化和统计选项揭示了更多的见解。

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CAT PETR: a graphical user interface for differential analysis of phosphorylation and expression data

Abstract Antibody microarray data provides a powerful and high-throughput tool to monitor global changes in cellular response to perturbation or genetic manipulation. However, while collecting such data has become increasingly accessible, a lack of specific computational tools has made their analysis limited. Here we present CAT PETR, a user friendly web application for the differential analysis of expression and phosphorylation data collected via antibody microarrays. Our application addresses the limitations of other GUI based tools by providing various data input options and visualizations. To illustrate its capabilities on real data, we show that CAT PETR both replicates previous findings, and reveals additional insights, using its advanced visualization and statistical options.

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来源期刊

Statistical Applications in Genetics and Molecular Biology 生物-生化与分子生物学

CiteScore

1.20

自引率

11.10%

发文量

审稿时长

6-12 weeks

期刊介绍： Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.