Algorithm and genome-wide database of functional siRNAs

描述(申请人提供)：尽管最近完成了人类基因组计划，但一个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一个可操作的基于细胞的模型中。不幸的是，这在目前是具有挑战性的，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整体的基因功能。RNA干扰(RNAi)已被证明是一种极其有效和多功能的实验工具，可以特异性地减少靶基因的表达，从而允许哺乳动物细胞失去功能的遗传屏蔽。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，而且该技术存在重大限制。为了解决这些问题，并扩大以前的计划资金，我们开发了一个新的实验平台，以在全基因组范围内识别功能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全基因组功能验证(FV)shRNA数据库和用于预测有效shRNA的软件。在第二阶段，我们建议为从RefSeq数据库中选择的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，以预测最有效的siRNA。然后，我们将扩展这一计划以包括数据库的开发，该数据库包括没有脱靶活性的全基因组FV小鼠shRNAs。FV shRNA数据库将被用于开发和发布克隆到慢病毒载体中的FV shRNA文库作为商业化产品。带有FV siRNA文库的遗传筛选有可能极大地简化基因功能的验证，并显著影响人类疾病机制的分子解剖。这些试剂在确定治疗干预的新靶点以及开发与药物发现越来越相关的范例方面有着相当大的希望。因此，我们预计这些工具箱将显著提高进行HTRNAi筛查的效率、经济性和简便性，并将为基础研究人员提供首选的、成本效益高的替代现有商业试剂的方法。拟议项目的最终目标是开发和提供商业上可用的新的、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的数据库，以及用于预测功能性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，旨在高通量发现新的药物靶点。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相关的翻译研究的效率，并开发新的药物，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.