Systematic identification of yeast cell cycle transcription factors using multiple data sources.
Systematic identification of yeast cell cycle transcription factors using multiple data sources.
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DOI：
10.1186/1471-2105-9-522
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发表时间：
2008-12-05
影响因子：
3
通讯作者：
Li WH
中科院分区：
文献类型：
--
作者：
Wu WS;Li WH
真核细胞周期是一个复杂的过程，在许多水平上受到精确调控。许多细胞周期特异性基因在转录上受到调节，并在需要它们之前表达。为了了解细胞周期过程，识别调节细胞周期调节基因表达的细胞周期转录因子（TF）非常重要。我们开发了一种方法，以确定细胞周期转录因子在酵母中整合当前的ChIP芯片，突变体，转录因子结合位点（TFBS），和细胞周期基因表达数据。我们鉴定了17个细胞周期转录因子，其中12个是已知的细胞周期转录因子，而其余5个（Ash 1，Rlm 1，Ste 12，Stp 1，Tec 1）是推定的新细胞周期转录因子。对于每个细胞周期TF，我们分配特定的细胞周期阶段，其中TF的功能和确定的时间滞后的TF发挥调节作用，其靶基因。我们还鉴定了178个新的细胞周期调控基因，其中59个功能未知，但它们现在可能被注释为细胞周期调控基因。我们的大多数预测都得到了以前的实验或计算研究的支持。此外，一个高置信度TF-基因调控矩阵是作为我们的方法的副产品。该矩阵中的每个TF-基因调控关系至少由三个数据源支持：基因表达、TFBS和ChIP-芯片或/和突变体数据。我们表明，我们的方法比现有的四种方法更好地识别酵母细胞周期TF。最后，我们的方法应用到不同的细胞周期基因表达数据集表明，我们的方法是强大的。我们的方法是有效的识别酵母细胞周期的TF和细胞周期调控基因。我们的许多预测都得到了文献的证实。我们的研究表明，整合多个数据源是研究复杂生物系统的一种强大方法。
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. 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 and identified the time lag for the TF to exert regulatory effects on its target genes. 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. 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.
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影响因子：
--
作者：
Andersson CR;Hvidsten TR;Isaksson A;Gustafsson MG;Komorowski J
通讯作者：
Komorowski J
DOI：
10.1073/pnas.2136632100
发表时间：
2003-12-23
影响因子：
11.1
作者：
Liao, JC;Boscolo, R;Roychowdhury, VP
通讯作者：
Roychowdhury, VP
影响因子：
14.9
作者：
Mewes, HW;Frishman, D;Weil, B
通讯作者：
Weil, B
影响因子：
10.5
作者：
McInerny, CJ;Partridge, JF;Breeden, LL
通讯作者：
Breeden, LL
影响因子：
5.8
作者：
de Lichtenberg, U;Jensen, LJ;Brunak, S
通讯作者：
Brunak, S