Inferring regulatory circuitry from microarray data

从微阵列数据推断调节电路

• 批准号: 6934499
• 负责人: Harmen J Bussemaker
• 金额: $ 35.02万
• 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 2008-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6934499
• 关键词: Drosophilidae; Saccharomyces cerevisiae; arthropod genetics; bioengineering /biomedical engineering; bioinformatics; computational biology; computer data analysis; functional /structural genomics; fungal genetics; gene expression; genetic regulatory element; messenger RNA; microarray technology; protein binding; transcription factor

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（例如DNA微阵列）中生成的大量数据中提取有用的生物学知识是一个主要挑战。人类基因组中发现的蛋白质编码基因数量惊人的较少，它使基因表达调节作为生物复杂性的决定因素的重要性。监管机制的失败在许多人类疾病中起作用。我们研究的总体目的是进一步开发回归方法，作为用于分析功能基因组数据的新范式。使用基于控制转录起始，mRNA离职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微阵列数据的mRNA表达和转录因子结合。这里提出的研究将基于我们基于基序的回归分析工具的成功，以发现非编码DNA中的顺式调节元件，并推断调节因子与这些元素结合的活性。可以分离单个基因组mRNA表达模式的事实使得可以对调节过程的环境条件依赖性进行建模。具体目的是：（1）通过合并基于后缀树，特定位置的评分矩阵和基因特异性误差估计的算法来提高减少的统计能力以检测退化基序。我们还将探讨使用比较基因组学来限制对图案的搜索到保守区域的搜索； （2）通过综合基因组转录因子结合数据的分析和大量的mRNA表达数据将转录因子与其功能靶基因相关联在酿酒酵母中的功能靶基因； （3）通过对MRNA表达数据进行多元回归分析，发现转录因子之间的协同和竞争相互作用，其中这种相互作用是明确建模的。我们还将分析这些相互作用的可能依赖性； （4）通过结合非编码DNA中的簇转录因子结合位点的隐藏Markov建模与mRNA表达数据的回归分析来表征果蝇及其靶基因中的顺式调节模块。