Rattus norvegicus, or the rat, has been widely used as animal models for a diversity of human diseases in the last 150 years. The rat, as a disease model, has the advantage of relatively large body size and highly similar physiology to humans. In drug discovery, rat models are routinely used in drug efficacy and toxicity assessments. To facilitate molecular pharmacology studies in rats, we present the predicted rat interactome database (PRID), which is a database of high-quality predicted functional gene interactions with balanced sensitivity and specificity. PRID integrates functional gene association data from 10 public databases and infers 305 939 putative functional associations, which are expected to include 13.02% of all rat protein interactions, and 52.59% of these function associations may represent protein interactions. This set of functional interactions may not only facilitate hypothesis formulation in molecular mechanism studies, but also serve as a reference interactome for users to perform gene set linkage analysis (GSLA), which is a web-based tool to infer the potential functional impacts of a set of changed genes observed in transcriptomics analyses. In a case study, we show that GSLA based on PRID may provide more precise and informative annotations for investigators to understand the physiological mechanisms underlying a phenotype and lead investigators to testable hypotheses for further studies. Widely used functional annotation tools such as Gene Ontology (GO) analysis, and Database for Annotation, Visualization and Integrated Discovery (DAVID) did not provide similar insights.

中文翻译：

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预测大鼠相互作用组数据库和基因集连锁分析

褐家鼠在过去的 150 年中，老鼠或老鼠已被广泛用作多种人类疾病的动物模型。大鼠作为疾病模型，具有体型较大、生理机能与人类高度相似的优点。在药物发现中，大鼠模型通常用于药物功效和毒性评估。为了促进大鼠的分子药理学研究，我们提出了预测的大鼠相互作用组数据库 (PRID)，这是一个具有平衡敏感性和特异性的高质量预测功能基因相互作用的数据库。PRID 整合了来自 10 个公共数据库的功能基因关联数据，推断出 305 939 个推定的功能关联，预计将包括所有大鼠蛋白质相互作用的 13.02%，而这些功能关联中的 52.59% 可能代表蛋白质相互作用。这组功能相互作用不仅可以促进分子机制研究中的假设制定，而且还可以作为用户进行基因集连锁分析 (GSLA) 的参考相互作用组，这是一种基于网络的工具，用于推断一个基因组的潜在功能影响。在转录组学分析中观察到的一组改变的基因。在一个案例研究中，我们表明基于 PRID 的 GSLA 可以为研究人员提供更精确和信息丰富的注释，以了解表型背后的生理机制，并引导研究人员为进一步研究提出可检验的假设。广泛使用的功能注释工具，如基因本体 (GO) 分析和注释、可视化和集成发现数据库 (DAVID) 没有提供类似的见解。