Algorithm and genome-wide database of functional siRNAs

描述（由申请人提供）：尽管最近完成了人类基因组计划，但表面上更困难的后基因组挑战将是所有人类基因的功能注释，并将这些信息整合到可操作的基于细胞的模型中。不幸的是，目前这是具有挑战性的，主要是因为缺乏可靠的实验和生物信息学工具集来快速描述和描述基因功能。RNA干扰（RNAi）已被证明是一种非常有效和通用的实验工具，可以特异性地降低目标基因的表达，从而允许在哺乳动物细胞中丧失功能的遗传筛选。尽管取得了这些成功，但高通量（HT） RNAi筛选在技术上具有挑战性，并且存在显著的局限性。为了解决这些问题，并扩大之前的项目资金，我们开发了一个新的实验平台，在全基因组范围内识别功能性shrna。拟议项目的最终目标是开发并在公共领域提供具有最小脱靶效应的功能验证（FV） shrna的全基因组数据库和用于预测有效shrna的软件。在第二阶段，我们建议从RefSeq数据库中选择20,000个人类基因开发FV shRNA数据集。我们将与罗切斯特大学和犹他大学的生物信息学顾问合作，开发和维护FV shRNA数据库和算法，用于预测最有效的sirna。然后，我们将扩展该计划，包括开发包含无脱靶活性的FV小鼠全基因组shrna的数据库。FV shRNA数据库将用于开发和作为商业产品发布克隆到慢病毒载体的FV shRNA文库。利用FV siRNA文库进行遗传筛选有可能大大简化基因功能的验证，并对人类疾病机制的分子解剖产生重大影响。这些试剂在确定治疗干预的新靶点和开发越来越相关的药物发现范例方面具有相当大的前景。因此，我们预计这些工具集将显著提高HT RNAi筛选的效率、经济性和易用性，并将为基础研究人员提供现有市售试剂的首选、成本效益高的替代方案。该项目的最终目标是开发和商业化新的、强大的研究生物信息学工具：一个功能验证的、全基因组的人类和小鼠shrna数据库，以及用于预测功能shrna的算法。我们建议应用这些工具来开发全基因组功能验证的siRNA文库，用于高通量发现新的药物靶点。所开发的生物信息学工具和技术将显著提高与多种人类疾病机制的分子解剖和新药物开发相关的转化研究的效率，因此对改进药物发现研究具有重要意义。

DESCRIPTION (provided by applicant): Despite the recent completion of the human genome project, an ostensibly more difficult post-genomic challenge will be the functional annotation of all human genes and integration of this information into an operational cell-based model. Unfortunately, this is at present challenging, primarily due to the absence of reliable experimental and bioinformatic toolsets to rapidly delineate and describe gene function en masse. RNA interference (RNAi) has proven to be an extremely potent and versatile experimental tool to specifically reduce expression of targeted genes, allowing for loss-of-function genetic screens in mammalian cells. Despite these successes, high-throughput (HT) RNAi screening is technically challenging and significant limitations in the technology exist. To address these issues, and to expand on previous program funding, we have developed a novel experimental platform to identify functional shRNAs at a genome-wide scale. The ultimate goal of the proposed project is to develop and make available in public domains a genome-wide database of functionally validated (FV) shRNAs with minimum off-target effects and software for prediction of effective shRNAs. Under Phase II, we propose to develop a FV shRNA data set for 20,000 human genes selected from the RefSeq database. In collaboration with our bioinformatics consultants at University of Rochester and University of Utah, we will develop and maintain a FV shRNA database and algorithm for prediction of the most efficient siRNAs. Then, we will extend this program to include the development of databases comprising a genome-wide FV mouse shRNAs without off-target activity. The FV shRNA databases will be used to develop and release as a commercial product FV shRNA libraries cloned into lentiviral vectors. Genetic screens with FV siRNA libraries have the potential to greatly simplify validation of gene function and significantly impact the molecular dissection of human disease mechanisms. These reagents harbor considerable promise to identify new targets for therapeutic intervention, and the development of increasingly relevant paradigms for drug discovery. As a result, we foresee that these toolsets will significantly improve the efficiency, economy, and ease of performing HT RNAi screens, and will provide basic researchers with preferred, cost-effective alternatives to existing commercially available reagents. The ultimate goal of the proposed project is to develop and make commercially available new, powerful research bioinformatics tools: a database of functionally validated, genome-wide human and mouse shRNAs and algorithms for prediction of functional shRNAs. We propose to apply these tools to develop genome-wide functionally validated siRNA libraries designed for high-throughput discovery of novel drug targets. The developed bioinformatics tools and technologies will significantly improve the efficiency of translational research related to molecular dissection of diverse human disease mechanisms, development of new pharmaceuticals, and therefore, have major implications for improving drug discovery research.