COMPUTATIONAL ANALYSIS OF GENOME EXPRESSION DATA

基因组表达数据的计算分析

• 批准号: 6387062
• 负责人: MICHAEL Q ZHANG
• 金额: $ 29.93万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-01 至 2003-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6387062
• 关键词: computer data analysis; computer program /software; computer system design /evaluation; gene expression; genetic promoter element; genome; method development; molecular biology information system; nucleic acid sequence

The primary objective of this project is the development and the application of computational methods for analysis and interpretation of large-scale gene expression data. The use of high-density DNA arrays to monitor gene expression at a genome- wide scale constitutes a fundamental advance in biology. In particular, the dynamic expression patterns of all genes in the entire genome of an organism can be interrogated using sequential microarray hybridization of cDNA libraries. It is well known that complex gene expression patterns result from dynamic interacting networks of genes in the genetic regulatory circuitry. Hierarchical and modular organization of regulatory DNA sequence elements is important for combinatorial control and regulation of gene expression. In order to meet the challenge of interpretation of massive gene expression data that are currently being generated as a result of the availability of complete genome sequences and DNA chips, we propose to develop computational methods and to test them in actual analyses of such large-scale experimental data. This proposal is concerned with two specific aims: namely, (1) developing promoter databases and flexible computational tools that can efficiently facilitate transcriptome analysis; (2) solving real biological problems by collaborating with leading bench-scientists on studies of regulatory cis-elements and the mechanism of transcriptional regulation in specific biological systems and/or processes. We believe these two aims are inseparable, without closely joining forces between computational and experimental biologists, our ability of attacking complex biological problems will be severely limited. This proposal is likely to contribute to our understanding of the two related circuitry: one that is hard coded as a genetic program in the gene promoter architecture and the other that is wired in space-time as a linked network by interacting gene transcripts and products, both can be activated or manifested in response to specific signals. In turn, the results will help us to understand normal biological processes such as cell cycle, homeostasis, growth and development as well as pathological manifestations such as metabolic diseases, developmental abnormalities and cancer.

该项目的主要目标是开发和应用用于分析和解释大规模基因表达数据的计算方法。 使用高密度 DNA 阵列在全基因组范围内监测基因表达构成了生物学的根本性进步。 特别是，可以使用 cDNA 文库的连续微阵列杂交来询问生物体整个基因组中所有基因的动态表达模式。 众所周知，复杂的基因表达模式是由基因调控回路中基因的动态相互作用网络产生的。 调控 DNA 序列元件的分层和模块化组织对于基因表达的组合控制和调控非常重要。 为了应对解释目前由于完整基因组序列和 DNA 芯片而产生的大量基因表达数据的挑战，我们建议开发计算方法并在如此大规模的实际分析中对其进行测试实验数据。 该提案涉及两个具体目标：（1）开发启动子数据库和灵活的计算工具，以有效促进转录组分析； （2）通过与领先的基础科学家合作研究特定生物系统和/或过程中的顺式调控元件和转录调控机制来解决实际的生物学问题。 我们相信这两个目标是密不可分的，如果计算生物学家和实验生物学家之间没有紧密的合作，我们解决复杂生物学问题的能力将受到严重限制。 这一提议可能有助于我们对两个相关电路的理解：一个是硬编码为基因启动子架构中的遗传程序，另一个是通过相互作用的基因转录本和产物作为链接网络连接在时空中，两者都可以响应特定信号而被激活或显现。 反过来，这些结果将帮助我们了解正常的生物过程，如细胞周期、稳态、生长和发育，以及病理表现，如代谢疾病、发育异常和癌症。