Computational Methods for Directed Functional Genomics

描述（由申请人提供）：基因功能（或表型）的计算预测可以通过将注意力集中在可能实验的子集上来减少实验问题的规模。当前函数注释数据库-例如，具有基因本体论（GO）功能的基因的酵母基因组数据库（SGD）注释是非常重要的资源，但并不是设计用于主持计算预测。尽管SGD和几个其它注释数据库这样标记预测，但它们不提供置信度的测量。定量预测的必要性是存在的，与定性的“有人这么说”的预测不同。概率评分系统可能是最有用的，其中分数传达了真实性的概率。 我们将通过开发用于预测功能和表型的概率模型来生成概率预测。出于数据可用性的原因，我们使用S。cerevisiae和C. elegans作为模型系统。 我们还将生成概率模型来预测蛋白质和遗传相互作用。我们将以几种方式利用蛋白质和遗传相互作用的概率网络。我们将应用通信理论（2-末端网络可靠性）的思想，从概率蛋白质网络预测蛋白质复合物的新成员。我们将开发计算方法来指导有效地发现S.酿酒酵母，作为一个模型，指导未来的高通量研究后生动物。我们将利用概率合成致命的相互作用网络，以确定药物的作用机制。 我们将向更广泛的生物医学界传播预测。我们提出了一个分布式定量预测资源的启发分布式基因组注释DAS系统。我们将适应以前开发的界面浏览，搜索和检索概率注释，以提高其效用。 在目标1中。我们开发、应用并验证了预测S. cerevisiae和C.优雅 在目标2中。我们利用概率网络的蛋白质和遗传相互作用的S。酿酒酵母来阐明网络结构，指导功能基因组实验，并揭示药物作用机制。 在目标，3。我们在一个简单的、通用的、分布式的软件框架内传播概率预测，用于共享和浏览定量预测。

DESCRIPTION (provided by applicant): Computational prediction of gene function (or phenotype) can reduce the scale of an experimental problem by focusing attention on a subset of possible experiments. Current function annotation databases-e.g., the Saccharomyces Genome Database (SGD) annotation of genes with Gene Ontology (GO) functions-are critically important resources, but were not designed to host computational predictions Although SGD and several other annotation databases label predictions as such, they provide no measures of confidence. The need exists for quantitative predictions, as distinct from qualitative "somebody said so" predictions. Probabilistic scoring systems, in which the score communicates the probability of veracity, are likely to be the most useful. We will generate probabilistic predictions by developing probabilistic models for predicting function and phenotype. For reasons of data availability, we use S. cerevisiae and C. elegans as model systems. We will also generate probabilistic models to predict protein and genetic interactions. We will exploit probabilistic networks of protein and genetic interaction in several ways. We will apply ideas from communication theory (2-terminal network reliability) to predict new members of protein complexes from probabilistic protein networks. We will develop computational methods to guide efficient discovery of genetic interactions in S. cerevisiae, as a model for guiding future high-throughput studies in metazoans. We will exploit probabilistic synthetic lethal interaction networks to identify drug mechanism of action. We will disseminate predictions to the broader biomedical community. We propose a distributed quantitative prediction resource inspired by the DAS system of distributed genome annotation. We will adapt previously developed interfaces for browsing, searching, and retrieving probabilistic annotations to enhance their utility. In Aim 1. we develop, apply, and validate methods for predicting function, phenotype, physical and genetic interaction in S. cerevisiae and C. elegans. In Aim 2. we exploit probabilistic networks of protein and genetic interaction in S. cerevisiae to elucidate network structure, to guide functional genomic experiments, and to reveal drug mechanism of action. In Aim, 3. we disseminate probabilistic predictions within a simple, generic, distributed software framework for sharing and browsing quantitative predictions.