Inferring regulatory circuitry from microarray data

从微阵列数据推断调节电路

• 批准号: 7242590
• 负责人: Harmen J Bussemaker
• 金额: $ 35.23万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-13 至 2009-05-11
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7242590
• 关键词: Inferring; regulatory; circuitry; microarray; data

DESCRIPTION (provided by applicant): It is a major challenge to extract useful biological knowledge from the large amounts of data that are currently being generated by genome sequencing projects and related technologies such as DNA microarrays. The surprisingly low number of protein-encoding genes found in the human genome unscores the importance of gene expression regulation as a determinant of organismal complexity. Failure of regulatory mechanisms plays a role in many human diseases. The general aim of our research is to further develop regression approaches as a new paradigm for the analysis of functional genomics data. Using simple models based on the molecular mechanisms that control transcription initiation, mRNA turnover, and chromatin remodeling we will analyze genomic sequences as well microarray data for mRNA expression and transcription factor binding. The research proposed here will build upon the success of REDUCE, our motif-based regression analysis tool for discovering cis-regulatory elements in non-coding DNA and inferring the activity of the regulatory factors binding to these elements. The fact that a single genome-wide mRNA expression pattern can be analyzed in isolation makes it possible to model the environmental condition dependence of regulatory processes. Specific aims are to: (1) Increase the statistical power of REDUCE to detect degenerate motifs by incorporating algorithms based on suffix trees, position-specific scoring matrices, and gene-specific error estimation. We will also explore the use of comparative genomics to restrict the search for motifs to conserved regions; (2) Associate transcription factors with their functional target genes in S. cerevisiae through intergrated analysis of genomewide transcription factor binding data and a large library of mRNA expression data; (3) Uncover synergistic and competitive interactions between transcription factors through multivariate regression analysis of mRNA expression data in which such interaction are modeled explicitly. We will also analyze the possible context dependence of these interactions; (4) Characterize cis-regulatory modules in Drosophila and their target genes by combining hidden Markov modeling of clustered transcription factor binding sites in non-coding DNA with regression analysis of mRNA expression data.

描述（由申请人提供）：从目前由基因组测序项目和相关技术（如DNA微阵列）产生的大量数据中提取有用的生物学知识是一个主要挑战。在人类基因组中发现的蛋白质编码基因数量少得惊人，这表明基因表达调控作为生物体复杂性决定因素的重要性。调控机制的失效在许多人类疾病中起着重要作用。我们研究的总体目标是进一步发展回归方法，作为功能基因组学数据分析的新范式。使用基于控制转录起始、mRNA周转和染色质重塑的分子机制的简单模型，我们将分析基因组序列以及mRNA表达和转录因子结合的微阵列数据。本文提出的研究将建立在REDUCE的成功基础上，REDUCE是我们基于基序的回归分析工具，用于发现非编码DNA中的顺式调控元件并推断与这些元件结合的调控因子的活性。事实上，一个单一的全基因组mRNA表达模式可以在隔离分析，使其有可能模拟环境条件依赖性的调节过程。具体目标是：(1)通过结合基于后缀树、位置特异性评分矩阵和基因特异性误差估计的算法，提高REDUCE检测退化基序的统计能力。我们还将探索使用比较基因组学来限制对保守区域的基序的搜索；(2)通过综合分析全基因组转录因子结合数据和大量mRNA表达数据，将酿酒酵母中的转录因子与其功能靶基因关联起来；(3)通过对mRNA表达数据进行多元回归分析，揭示转录因子之间的协同和竞争相互作用，并对这种相互作用进行明确建模。我们还将分析这些相互作用可能的上下文依赖性；(4)将非编码DNA聚集转录因子结合位点的隐马尔可夫模型与mRNA表达数据的回归分析相结合，对果蝇的顺式调控模块及其靶基因进行表征。