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首页> 外文期刊>Brazilian Journal of Medical and Biological Research >Pipeline for macro- and microarray analyses
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Pipeline for macro- and microarray analyses

机译:宏和微阵列分析的管道

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摘要

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.
机译:宏和微阵列分析(PMmA)管道是一组脚本,这些脚本具有开发用于分析由阵列图像定量软件生成的DNA阵列数据的Web界面。 PMmA设计用于单色或双色阵列数据,并作为五类(数据格式,规范化,数据分析,聚类和阵列图)中的管道工作。它也可以用作BioArray软件环境中的插件,用于阵列分析的开源数据库,或在Web服务的本地版本中使用。 PMmA中的所有脚本均以PERL编程语言开发,而统计分析功能则以R统计语言实现。因此,我们的软件包是与平台无关的软件。我们的算法可以正确选择几乎90%的差异表达基因,与其他分析方法相比,其表现出更高的性能。该流水线软件已应用于甘蔗暴露于寒冷3至48小时的1536个表达的序列标签宏阵列公共数据。 PMmA识别了30个冷响应基因,而该基因以前在这个公共数据集中没有被识别。在治疗中,有十四个基因上调,两个具有可变表达,另外十四个基因下调。这些新发现当然是使用高级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的平均信号强度的依赖性。非商业用户可以从http://ipe.cbmeg.unicamp.br/pub/PMmA免费获得Web界面,补充信息和程序包源代码。

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