Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada ; Emerging Viral Pathogens Section, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, Maryland, United States of America.
PLoS One. 2013 Nov 29;8(11):e80837. doi: 10.1371/journal.pone.0080837. eCollection 2013.
Kinome microarrays are comprised of peptides that act as phosphorylation targets for protein kinases. This platform is growing in popularity due to its ability to measure phosphorylation-mediated cellular signaling in a high-throughput manner. While software for analyzing data from DNA microarrays has also been used for kinome arrays, differences between the two technologies and associated biologies previously led us to develop Platform for Intelligent, Integrated Kinome Analysis (PIIKA), a software tool customized for the analysis of data from kinome arrays. Here, we report the development of PIIKA 2, a significantly improved version with new features and improvements in the areas of clustering, statistical analysis, and data visualization. Among other additions to the original PIIKA, PIIKA 2 now allows the user to: evaluate statistically how well groups of samples cluster together; identify sets of peptides that have consistent phosphorylation patterns among groups of samples; perform hierarchical clustering analysis with bootstrapping; view false negative probabilities and positive and negative predictive values for t-tests between pairs of samples; easily assess experimental reproducibility; and visualize the data using volcano plots, scatterplots, and interactive three-dimensional principal component analyses. Also new in PIIKA 2 is a web-based interface, which allows users unfamiliar with command-line tools to easily provide input and download the results. Collectively, the additions and improvements described here enhance both the breadth and depth of analyses available, simplify the user interface, and make the software an even more valuable tool for the analysis of kinome microarray data. Both the web-based and stand-alone versions of PIIKA 2 can be accessed via http://saphire.usask.ca.
激酶组微阵列由作为蛋白激酶磷酸化靶标的肽组成。由于其能够高通量地测量磷酸化介导的细胞信号转导，因此该平台越来越受欢迎。虽然用于分析 DNA 微阵列数据的软件也可用于激酶组阵列，但这两种技术之间的差异以及相关生物学特性之前促使我们开发了用于智能综合激酶组分析的平台（PIIKA），这是一种专门用于分析激酶组阵列数据的软件工具。在这里，我们报告了 PIIKA 2 的开发，它是一个经过重大改进的版本，在聚类、统计分析和数据可视化等领域具有新功能和改进。除了原始 PIIKA 的其他添加内容外，PIIKA 2 现在允许用户：评估样本组聚类在一起的程度；确定在样本组之间具有一致磷酸化模式的肽集；使用引导进行层次聚类分析；查看 t 检验中两组样本之间的假阴性概率和阳性及阴性预测值；轻松评估实验重现性；使用火山图、散点图和交互式三维主成分分析来可视化数据。PIIKA 2 中的新增功能还包括基于网络的界面，这使得不熟悉命令行工具的用户可以轻松提供输入并下载结果。总之，这里描述的添加和改进增强了可用分析的广度和深度，简化了用户界面，并使该软件成为分析激酶组微阵列数据的更有价值的工具。PIIKA 2 的基于网络和独立版本均可通过 http://saphire.usask.ca 访问。
