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Nucleic Acids Research 2005 33(8):2685-2696; doi:10.1093/nar/gki566
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Published online 12 May 2005

© The Author 2005. 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{at}oupjournals.org

Article

Identification of brassinosteroid-related genes by means of transcript co-response analyses

Janina Lisso, Dirk Steinhauser, Thomas Altmann1, Joachim Kopka and Carsten Müssig1,*

Max-Planck-Institut für Molekulare Pflanzenphysiologie Am Mühlenberg 1, D-14476 Golm, Germany 1Institut für Biochemie und Biologie, Genetik, Universität Potsdam Karl-Liebknecht-Strasse 24-25, Haus 26, D-14476 Golm, Germany

*To whom correspondence should be addressed. Tel: +49 331 567 8258; Fax: +49 331 567 8250; Email: muessig{at}mpimp-golm.mpg.de

Received February 14, 2005. Revised March 14, 2005. Accepted April 21, 2005.

The comprehensive systems-biology database (CSB.DB) was used to reveal brassinosteroid (BR)-related genes from expression profiles based on co-response analyses. Genes exhibiting simultaneous changes in transcript levels are candidates of common transcriptional regulation. Combining numerous different experiments in data matrices allows ruling out outliers and conditional changes of transcript levels. CSB.DB was queried for transcriptional co-responses with the BR-signalling components BRI1 and BAK1: 301 out of 9694 genes represented in the nasc0271 database showed co-responses with both genes. As expected, these genes comprised pathway-involved genes (e.g. 72 BR-induced genes), because the BRI1 and BAK1 proteins are required for BR-responses. But transcript co-response takes the analysis a step further compared with direct approaches because BR-related non BR-responsive genes were identified. Insights into networks and the functional context of genes are provided, because factors determining expression patterns are reflected in correlations. Our findings demonstrate that transcript co-response analysis presents a valuable resource to uncover common regulatory patterns of genes. Different data matrices in CSB.DB allow examination of specific biological questions. All matrices are publicly available through CSB.DB. This work presents one possible roadmap to use the CSB.DB resources.

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


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