Vicentini R, Menossi M
Laboratório de Genoma Funcional, Centro de Biologia Molecular e Engenharia Genética, Universidade Estadual de Campinas, Campinas, SP, Brasil.
Braz J Med Biol Res. 2007 May;40(5):615-9. doi: 10.1590/s0100-879x2007000500003.
The pipeline for macro- and microarray analyses (PMmA) is a set of scripts with a web interface developed to analyze DNA array data generated by array image quantification software. PMmA is designed for use with single- or double-color array data and to work as a pipeline in five classes (data format, normalization, data analysis, clustering, and array maps). It can also be used as a plugin in the BioArray Software Environment, an open-source database for array analysis, or used in a local version of the web service. All scripts in PMmA were developed in the PERL programming language and statistical analysis functions were implemented in the R statistical language. Consequently, our package is a platform-independent software. Our algorithms can correctly select almost 90% of the differentially expressed genes, showing a superior performance compared to other methods of analysis. The pipeline software has been applied to 1536 expressed sequence tags macroarray public data of sugarcane exposed to cold for 3 to 48 h. PMmA identified thirty cold-responsive genes previously unidentified in this public dataset. Fourteen genes were up-regulated, two had a variable expression and the other fourteen were down-regulated in the treatments. These new findings certainly were a consequence of using a superior statistical analysis approach, since the original study did not take into account the dependence of data variability on the average signal intensity of each gene. The web interface, supplementary information, and the package source code are available, free, to non-commercial users at http://ipe.cbmeg.unicamp.br/pub/PMmA.
宏阵列和微阵列分析流程(PMmA)是一组带有网络界面的脚本,开发用于分析由阵列图像定量软件生成的DNA阵列数据。PMmA设计用于处理单色或双色阵列数据,并作为一个流程在五个类别(数据格式、标准化、数据分析、聚类和阵列图谱)中运行。它还可以作为BioArray软件环境(一个用于阵列分析的开源数据库)中的插件使用,或者在网络服务的本地版本中使用。PMmA中的所有脚本均用PERL编程语言开发,统计分析功能用R统计语言实现。因此,我们的软件包是一个与平台无关的软件。我们的算法能够正确选择近90%的差异表达基因,与其他分析方法相比表现出卓越的性能。该流程软件已应用于甘蔗在3至48小时低温处理下的1536个表达序列标签宏阵列公共数据。PMmA在这个公共数据集中鉴定出了30个先前未鉴定的冷响应基因。在处理中,14个基因上调,2个基因表达可变,另外14个基因下调。这些新发现无疑是使用了卓越统计分析方法的结果,因为原始研究没有考虑数据变异性对每个基因平均信号强度的依赖性。网络界面、补充信息和软件包源代码可供非商业用户免费从http://ipe.cbmeg.unicamp.br/pub/PMmA获取。