Algorithm and genome-wide database of functional siRNAs

描述（由申请人提供）：尽管人类基因组计划最近完成，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和将此信息整合到基于细胞的操作模型中。不幸的是，这是目前具有挑战性的，主要是由于缺乏可靠的实验和生物信息学的工具集，以快速描绘和描述基因功能的基因组。RNA干扰（RNAi）已被证明是一种非常有效和通用的实验工具，可以特异性地降低靶基因的表达，从而在哺乳动物细胞中进行功能丧失的遗传筛选。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助，我们开发了一种新的实验平台，以确定在全基因组范围内的功能性shRNA。拟议项目的最终目标是开发并在公共领域提供一个功能验证（FV）shRNA的全基因组数据库，具有最小的脱靶效应和预测有效shRNA的软件。在第二阶段，我们计划为从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州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，包括开发包含全基因组FV小鼠shRNA而没有脱靶活性的数据库。FV shRNA数据库将用于开发和发布克隆到慢病毒载体中的商业产品FV shRNA文库。用FV siRNA文库进行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。这些试剂具有相当大的希望，以确定新的治疗干预的目标，和药物发现越来越相关的范例的发展。因此，我们预见这些工具集将显著提高进行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、具有成本效益的替代品。拟议项目的最终目标是开发和商业化提供新的，强大的研究生物信息学工具：功能验证的，全基因组的人类和小鼠的shRNA和算法的数据库预测功能的shRNA。我们建议应用这些工具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。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、新药开发相关的转化研究的效率，因此，对改善药物发现研究具有重大意义。

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.