國家理論科學研究中心學術演講
2009 NCTS Seminar on Statistics
Speaker: Professor Wei-Sheng Wu 吳謂勝
(Department of Electrical Engineering, NCKU)
Time: Friday, November 20, 2009
(1) 11:30 am-1:00pm, group meeting
(2) 1:00-2:30pm, seminar
Topic: Systematic Identification of Yeast Cell Cycle
Transcription Factors Using Multiple Data Sources
Abstract:
Background Eukaryotic cell cycle is a complex process and is precisely
regulated at many levels. Many genes specific to the cell cycle are regulated
transcriptionally and are expressed just before they are needed. To
understand the cell cycle process, it is important to identify the cell cycle
transcription factors (TFs) that regulate the expression of cell
cycle-regulated genes.
Results
We developed a method to identify cell cycle TFs in yeast by integrating
current ChIP-chip, mutant, transcription factor binding site (TFBS), and cell
cycle gene expression data. We identified 17 cell cycle TFs, 12 of which are
known cell cycle TFs, while the remaining five (Ash1, Rlm1, Ste12, Stp1,
Tec1) are putative novel cell cycle TFs. For each cell cycle TF, we assigned
specific cell cycle phases in which the TF functions. We also identified 178
novel cell cycle-regulated genes, among which 59 have unknown functions, but
they may now be annotated as cell cycle-regulated genes. Most of our
predictions are supported by previous experimental or computational studies.
Furthermore, a high confidence TF-gene
regulatory matrix is derived as a byproduct of our method. Each TF-gene
regulatory relationship in this matrix is supported by at least three data
sources: gene expression, TFBS, and ChIP-chip or/and mutant data. We show
that our method performs better than four existing methods for identifying
yeast cell cycle TFs. Finally, an application of our method to different cell
cycle gene expression datasets suggests that our method is robust.
Conclusions
Our method is effective for identifying yeast cell cycle TFs and cell
cycle-regulated genes. Many of our predictions are validated by the
literature. Our study shows that integrating multiple data sources is a
powerful approach to studying complex biological systems.
Place:
交通大學綜合一館427室
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