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.

最近完整基因组序列的出现正在开创基因调控分析的新时代。酿酒酵母的完整基因组序列非常适合此类研究。许多酵母生化和遗传途径已被充分了解，其基因组包含数量可管理的约 5900 个以紧凑方式排列的基因。我们提取了所有潜在酵母启动子的序列，以便研究在酵母细胞周期中转录受到共同调控的基因。我们搜索共表达基因的文献，使用称为吉布斯采样的统计方法和使用词频方法来识别这些基因的启动子之间共享的序列基序，然后使用其他酵母启动子在其他酵母启动子中搜索该序列基序。统计方法。我们的第一个分析是在 S 期转录的复制依赖性组蛋白。我们已经鉴定了九个组蛋白启动子之间共享的序列基序，并确定了包含该基序拷贝的其他基因。正如之前所表明的，该基序部分负责组蛋白的细胞周期依赖性表达，我们预测这些其他基因与组蛋白共同调节。我们在一个项目中将此分析扩展到其他细胞周期调节基因，其中使用基因微阵列实验的数据来识别在酵母细胞周期中共表达的基因。我们对细胞周期调控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阅读框的功能提出建议。此外，此类工作可能会导致更好地理解复杂的调控网络，这些网络协调正确的基因表达定量和时间模式。我们还使用作为此次合作的一部分开发的字数统计数据来分析人类发起人。