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Nucleic Acids Research 2006 34(5):1532-1539; doi:10.1093/nar/gkl058
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Published online 14 March 2006

© The Author 2006. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Article

A new method for gene discovery in large-scale microarray data

Kentaro Yano*, Kazuhide Imai1, Akifumi Shimizu and Takao Hanashita2

Plant Breeding Laboratory, Graduate School of Agriculture, Kyoto University Kyoto 606-8502 Japan 1Biomedical Group, Kaken Geneqs Inc. 2-6-7 Kazusa-kamatari, Kisarazu, Chiba 292-0818, Japan 2SI Engineering Group, Rikei Corporation 1-26-2, Nishi-Shinjuku, Shinjuku-ku, Tokyo 163-0535, Japan

*To whom correspondence should be addressed. Tel: +81 438 52 3947; Fax: +81 438 52 3948; Email: yanoken{at}kazusa.or.jp

Received December 3, 2005. Revised January 4, 2006. Accepted February 24, 2006.

Microarrays are an effective tool for monitoring genome-wide gene expression levels. In current microarray analyses, the majority of genes on arrays are frequently eliminated for further analysis because the changes in their expression levels (ratios) are considered to be not significant. This strategy risks failure to discover whole sets of genes related to a quantitative trait of interest, which is generally controlled by several loci that make various contributions. Here, we describe a high-throughput gene discovery method based on correspondence analysis with a new index for expression ratios [arctan (1/ratio)] and three artificial marker genes. This method allows us to quickly analyze the whole microarray dataset and discover up-/down-regulated genes related to a trait of interest. We employed an example dataset to show the theoretical advantage of this method. We then used the method to identify 88 cancer-related genes from a published microarray data from patients with breast cancer. This method also allows us to predict the phenotype of a given sample from the gene expression profile. This method can be easily performed and the result is also visible in 3D viewing software that we have developed.

Present addresses: Kentaro Yano, The NEDO Team of Applied Plant Genomics, Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba 292-0818, Japan

Akifumi Shimizu, Laboratory of Plant Genetics and Breeding, College of Bioresource Sciences, Nihon University, Kameino Fujisawa, Kanagawa 252-8501, Japan

The authors wish it to be known that, in their opinion, the first four authors should be regarded as joint First Authors


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