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Nucleic Acids Research 2004 32(18):5349-5358; doi:10.1093/nar/gkh870
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Published online 8 October 2004

Nucleic Acids Research, Vol. 32 No. 18 © Oxford University Press 2004; all rights reserved

A dynamic, web-accessible resource to process raw microarray scan data into consolidated gene expression values: importance of replication

Nolwenn Le Meur*, Guillaume Lamirault, Audrey Bihouée, Marja Steenman, Hélène Bédrine-Ferran1, Raluca Teusan, Gérard Ramstein2 and Jean J. Léger

Ouest genopole, Institut du Thorax, Institut National de la Santé et de la Recherche Médicale (UMR 533), Faculté de Médecine, 44035 Nantes, France, 1 Centre National à la Recherche Scientifique (UMR 6061), Rennes, France and 2 Laboratoire d'Informatique de Nantes Atlantique, 44322 Nantes, France

* To whom correspondence should be addressed at INSERM U533, Faculté Médecine, BP 53508, 44035 Nantes cedex 1, France. Tel: +33 240412957; Fax: +33 240412950; Email: nolwenn.lemeur{at}nantes.inserm.fr

Received June 25, 2004; Revised August 17, 2004; Accepted September 17, 2004

We propose a freely accessible web-based pipeline, which processes raw microarray scan data to obtain experimentally consolidated gene expression values. The tool MADSCAN, which stands for MicroArray Data Suites of Computed ANalysis, makes a practical choice among the numerous methods available for filtering, normalizing and scaling of raw microarray expression data in a dynamic and automatic way. Different statistical methods have been adapted to extract reliable information from replicate gene spots as well as from replicate microarrays for each biological situation under study. A carefully constructed experimental design thus allows to detect outlying expression values and to identify statistically significant expression values, together with a list of quality controls with proposed threshold values. The integrated processing procedure described here, based on multiple measurements per gene, is decisive for reliably monitoring subtle gene expression changes typical for most biological events.


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