PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for ArabidopsisReportar como inadecuado

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BMC Bioinformatics

, 4:56

First Online: 07 November 2003Received: 08 August 2003Accepted: 07 November 2003


BackgroundTo date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis.

ResultsWe have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to i upload and populate microarray data into a database; ii integrate gene expression with enzymes of the pathways; iii generate pathway diagrams without building image files manually; iv visualize gene expressions for each pathway at enzyme, locus, and probe levels; and v perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways.

ConclusionPathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: i automatic generation of pathways associated with gene expression and ii statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiments.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-4-56 contains supplementary material, which is available to authorized users.

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Autor: Deyun Pan - Ning Sun - Kei-Hoi Cheung - Zhong Guan - Ligeng Ma - Matthew Holford - Xingwang Deng - Hongyu Zhao


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