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This article is part of the supplement: Eighth International Conference on Bioinformatics (InCoB2009): Computational Biology

Open Access Proceedings

Identification of cell cycle-related regulatory motifs using a kernel canonical correlation analysis

Je-Keun Rhee12, Je-Gun Joung3, Jeong-Ho Chang4, Zhangjun Fei35 and Byoung-Tak Zhang126*

Author Affiliations

1 Graduate Program in Bioinformatics, Seoul National University, Seoul 151-744, Korea

2 Center for Biointelligence Technology (CBIT), Seoul National University, Seoul 151-744, Korea

3 Boyce Thompson Institute for Plant Research, Cornell University, Ithaca, NY 14853, USA

4 Konan Technology Inc., Seoul 135-080, Korea

5 USDA Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA

6 School of Computer Science and Engineering, Seoul National University, Seoul 151-744, Korea

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BMC Genomics 2009, 10(Suppl 3):S29  doi:10.1186/1471-2164-10-S3-S29

Published: 3 December 2009

Abstract

Background

Gene regulation is a key mechanism in higher eukaryotic cellular processes. One of the major challenges in gene regulation studies is to identify regulators affecting the expression of their target genes in specific biological processes. Despite their importance, regulators involved in diverse biological processes still remain largely unrevealed. In the present study, we propose a kernel-based approach to efficiently identify core regulatory elements involved in specific biological processes using gene expression profiles.

Results

We developed a framework that can detect correlations between gene expression profiles and the upstream sequences on the basis of the kernel canonical correlation analysis (kernel CCA). Using a yeast cell cycle dataset, we demonstrated that upstream sequence patterns were closely related to gene expression profiles based on the canonical correlation scores obtained by measuring the correlation between them. Our results showed that the cell cycle-specific regulatory motifs could be found successfully based on the motif weights derived through kernel CCA. Furthermore, we identified co-regulatory motif pairs using the same framework.

Conclusion

Given expression profiles, our method was able to identify regulatory motifs involved in specific biological processes. The method could be applied to the elucidation of the unknown regulatory mechanisms associated with complex gene regulatory processes.