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的实验的子集上来缩小实验问题的规模。当前的功能注释数据库--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，但并不是为托管计算预测而设计的，尽管SGD和其他几个注释数据库这样标记预测，但它们不提供置信度测量。定量预测的必要性是存在的，这与定性的“有人这么说”的预测截然不同。概率评分系统很可能是最有用的，在这种系统中，分数传达了准确性的概率。我们将通过开发预测功能和表型的概率模型来生成概率预测。出于数据可获得性的原因，我们使用酿酒酵母和线虫作为模型系统。 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(双端网络可靠性)的思想从概率蛋白质网络中预测蛋白质复合体的新成员。我们将开发计算方法来指导有效地发现酿酒酵母中的遗传相互作用，作为指导未来后生动物高通量研究的模型。我们将利用概率合成致死相互作用网络来识别药物的作用机制。 我们将向更广泛的生物医学界传播预测。受分布式基因组标注DAS系统的启发，我们提出了一种分布式定量预测资源。我们将调整以前开发的用于浏览、搜索和检索概率注释的界面，以增强它们的实用性。 在目标1中，我们开发、应用和验证了预测酿酒酵母和线虫的功能、表型、物理和遗传相互作用的方法。 在目标2中，我们利用酿酒酵母中蛋白质和遗传相互作用的概率网络来阐明网络结构，指导功能基因组实验，并揭示药物的作用机制。 在AIM中，3.我们在一个简单、通用、分布式的软件框架内传播概率预测，以便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.