Predictive Learning of Transcriptional Networks

描述（由申请人提供）：我们建议开发和应用一套全面的计算和实验方法，以便在序列水平上充分表征哺乳动物基因表达的调控。我们的方法的核心是一个信息理论框架，用于从大规模基因表达数据和基因组序列信息中灵敏和高度特异性地识别DNA和RNA调控元件。我们将开发和应用基于网络水平保守的非对齐方法，以确定哺乳动物基因组对之间保守的调控元件的全面目录。这些高置信度的预测，然后将被用来确定远端调控元件组成的转录因子结合位点的集群。贝叶斯网络学习算法将被用来学习上下文相关的和组合的规则，通过这些规则，所发现的元件在局部启动子/3 'UTR内以及通过远端调控模块（如增强子和沉默子）影响基因表达。我们提出了一种基于噬菌体展示选择的微阵列分析的通用方法，以快速有效地鉴定与我们期望鉴定的数百种新型DNA和RNA调控元件特异性相互作用的蛋白质反式因子。拟议中的研究将显著推进调控网络的特征化速度和规模--不仅在人类中，而且在一系列其他具有生物医学和工业重要性的复杂基因组中。 公共卫生相关性：这项拟议中的研究将产生工具，使生物学家能够理解人类基因组中协调基因表达模式的调控代码。这项研究的重点是伴随人类癌症的基因表达畸变。因此，它有望显着推进我们对癌症表型的基本理解，对治疗具有潜在的重要意义。

DESCRIPTION (provided by applicant): We propose to develop and apply a comprehensive set of computational and experimental methods in order to fully characterize the regulation of mammalian gene expression at the sequence level. At the core of our approach is an information- theoretic framework for sensitive and highly specific identification of DNA and RNA regulatory elements from large-scale gene expression data and genomic sequence information. We will develop and apply a non-alignment based approach based on network-level conservation in order to identify comprehensive catalogues of regulatory elements conserved between pairs of mammalian genomes. These high-confidence predictions will then be used in order to identify distal regulatory elements composed of clusters of transcription factor binding sites. A Bayesian network learning algorithm will be employed to learn the context-dependent and combinatorial rules by which the discovered elements function to affect gene expression-both within local promoters/3'UTRs and through distal regulatory modules such as enhancers and silencers. We propose a versatile approach based on microarray profiling of phage- display selections in order to rapidly and efficiently identify the protein trans factors that specifically interact with the hundreds of novel DNA and RNA regulatory elements we expect to identify. The proposed research will significantly advance the rate and scale at which regulatory networks are characterized-both in humans, but also across a range of other complex genomes of biomedical and industrial importance. PUBLIC HEALTH RELEVANCE: The proposed research will yield tools that enable biologists to understand the regulatory code that orchestrates gene expression patterns in the human genome. The research is focused on aberrations of gene expression that accompany human cancers. As such, it promises to significantly advance our basic understanding of the cancer phenotype, with potentially important implications for therapy.