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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-24679

Rehrauer, H; Zoller, S; Schlapbach, R (2007). MAGMA: analysis of two-channel microarrays made easy. Nucleic Acids Research, 35(Web Se):W86-90.

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Abstract

The web application MAGMA provides a simple and intuitive interface to identify differentially expressed genes from two-channel microarray data. While the underlying algorithms are not superior to those of similar web applications, MAGMA is particularly user friendly and can be used without prior training. The user interface guides the novice user through the most typical microarray analysis workflow consisting of data upload, annotation, normalization and statistical analysis. It automatically generates R-scripts that document MAGMA's entire data processing steps, thereby allowing the user to regenerate all results in his local R installation. The implementation of MAGMA follows the model-view-controller design pattern that strictly separates the R-based statistical data processing, the web-representation and the application logic. This modular design makes the application flexible and easily extendible by experts in one of the fields: statistical microarray analysis, web design or software development. State-of-the-art Java Server Faces technology was used to generate the web interface and to perform user input processing. MAGMA's object-oriented modular framework makes it easily extendible and applicable to other fields and demonstrates that modern Java technology is also suitable for rather small and concise academic projects. MAGMA is freely available at www.magma-fgcz.uzh.ch.

Citations

10 citations in Web of Science®
10 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Functional Genomics Center Zurich
08 University Research Priority Programs > Systems Biology / Functional Genomics
DDC:570 Life sciences; biology
610 Medicine & health
Language:English
Date:July 2007
Deposited On:28 Dec 2009 05:56
Last Modified:27 Nov 2013 23:14
Publisher:Oxford University Press
ISSN:0305-1048
Publisher DOI:10.1093/nar/gkm302
PubMed ID:17517778

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