Gene Regulatory Sequences from Whole Chromosomes/genomes

全染色体/基因组的基因调控序列

• 批准号: 7148037
• 负责人: David LANDSMAN
• 金额: --
• 依托单位: NATIONAL LIBRARY OF MEDICINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7148037
• 关键词: Saccharomyces cerevisiae; cell cycle; cytogenetics; functional /structural genomics; fungal genetics; gene expression; gene expression profiling; genetic promoter element; genetic regulatory element; genetic transcription; genome; histones; microarray technology; microorganism culture; nucleic acid sequence; statistics /biometry

The recent availability of complete genome sequences is ushering in a new era in the analysis of gene regulation. The full genome sequence of the yeast Saccharomyces cerevisiae is ideal for such a study. Many yeast biochemical and genetic pathways are well understood, and its genome contains a manageable number of ~5900 genes arranged in a compact fashion. We have extracted the sequences of all potential yeast promoters in order to study genes whose transcription is co-regulated during the yeast cell cycle. We search the literature for co-expressed genes, identify sequence motifs which are shared among the promoters of these genes using a statistical method called Gibbs sampling and by using word frequency methods, and then search for this sequence motif in other yeast promoters using a other statistical methods. Our first analysis is on the replication dependent histones which are transcribed during S phase. We have identified a sequence motif shared among the nine histone promoters, and have pinpointed other genes which contain copies of this motif. As it has been shown previously that this motif is partly responsible for the cell cycle dependent expression of histones, we predict that these other genes are co-regulated with the histones. We have extended this analysis to other cell cycle regulated genes in a project where data from a gene microarray experiment was used to identify genes which were co-expressed during the yeast cell cycle. Our predictions about cell cycle regulation will help to better characterize known genes, and lead to suggestions about the functions of yet-unstudied open reading frames. Furthermore, such work may result in a better understanding of the complex regulatory networks that orchestrate correct quantitative and temporal patterns of gene expression. We are also analyzing human promoters using word counting statistics developed as a part of this collaboration.

在基因调控分析中，最新的完整基因组序列的可用性正在引入新时代。酿酒酵母的酵母糖疗法的完整基因组序列是此类研究的理想选择。许多酵母生化途径和遗传途径都已充分了解，其基因组包含以紧凑的方式排列的〜5900个基因。我们提取了所有潜在酵母启动子的序列，以研究在酵母细胞周期中转录的基因。我们使用称为gibbs采样的统计方法并使用单词频率方法来搜索文献中的共同基因，识别这些基因启动子之间共享的序列基序，然后使用其他统计方法在其他酵母启动子中搜索此序列基序。我们的首次分析是关于在S期间转录的复制依赖性组蛋白。我们已经确定了在九个组蛋白启动子之间共有的序列基序，并确定了包含该基序副本的其他基因。正如先前表明的那样，该基序是组蛋白的细胞周期表达的部分原因，我们预测这些其他基因与组蛋白共同调节。我们已经将此分析扩展到了一个项目中的其他细胞周期基因，在该项目中，使用基因微阵列实验的数据来鉴定在酵母细胞周期中共表达的基因。我们对细胞周期调节的预测将有助于更好地表征已知基因，并就尚未研究的开放式阅读帧的功能提出建议。此外，这种工作可能会更好地理解复杂的调节网络，这些网络策划了正确的基因表达定量和时间模式。我们还使用开发的单词计数统计数据来分析人类启动子，作为这项合作的一部分。