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(Reference retrieved automatically from SciELO through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Pipeline for macro- and microarray analyses

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R. Vicentini ; M. Menossi
Total Authors: 2
Document type: Journal article
Source: Brazilian Journal of Medical and Biological Research; v. 40, n. 5, p. 615-619, 2007-04-13.

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 (AU)

FAPESP's process: 02/01167-1 - Development of molecular markers based on sugarcane ESTs for the selection of economically important characteristics
Grantee:Anete Pereira de Souza
Support type: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 03/07244-0 - Sugarcane transcriptom
Grantee:Glaucia Mendes Souza
Support type: Research Grants - Research Partnership for Technological Innovation - PITE