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


CONCLUSIONS
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.
Rhee
Je-Keun
JK
Graduate Program in Bioinformatics, Seoul National University, Seoul 151-744, Korea. jkrhee@bi.snu.ac.kr
Joung
Je-Gun
JG
Chang
Jeong-Ho
JH
Fei
Zhangjun
Z
Zhang
Byoung-Tak
BT
D002453:Cell Cycle; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D050436:Regulatory Elements, Transcriptional; D012441:Saccharomyces cerevisiae / Q000737:chemistry* / Q000166:cytology* / Q000235:genetics; D017422:Sequence Analysis, DNA