Novel Approaches to Mammalian MicroRNA Target Prediction

哺乳动物 MicroRNA 目标预测的新方法

• 批准号: 8513374
• 负责人: YE DING
• 金额: $ 58.58万
• 依托单位: WADSWORTH CENTER
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-18 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8513374
• 关键词: 3' Untranslated Regions; Algorithms; Apoptosis; Arts; Binding; Binding Sites; Biochemical; Bioinformatics; Biological Assay; Biological Process; Biology; Cancer cell line; Code; Cognitive; Communities; Complex; Computer software; Data; Data Analyses; Databases; Development; Dimensions; Functional RNA; Gene Expression; Gene Expression Regulation; Genes; Goals; Health; Human; Immunoprecipitation; Internet; Malignant Neoplasms; Mediating; Messenger RNA; Methodology; Methods; MicroRNAs; Molecular Biology; Mus; Ontology; Outcome; Pathway interactions; Performance; Physiology; Positioning Attribute; Proteins; RNA; RNA Degradation; RNA Sequences; Regulation; Reporter; Research; Resources; Site; Software Tools; Statistical Models; Stem Cell Development; Stem cells; Structure; Systems Biology; Testing; Therapeutic; Thermodynamics; Translational Repression; Tumor Suppressor Genes; Validation; Virus; base; computerized tools; crosslink; data modeling; deep sequencing; human disease; improved; insight; interdisciplinary approach; novel; novel strategies; robot assistance; statistics; tool; user friendly software; virology; web services

DESCRIPTION (provided by applicant): MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate messenger RNAs (mRNAs) post-transcriptionally. miRNAs induce target messenger RNA degradation or translational inhibition, predominantly through cognitive binding sites in the 3 untranslated regions (3UTRs). Accurate identification of miRNA targets is essential for the understanding of their functions. Target identification has been a major challenge for understanding this recently recognized exciting dimension of gene regulation. To this end, computational target prediction methods have proven to be valuable. However, current prediction methods suffer from a number of limitations that hinder progress. Recently, biochemical purification and high throughput deep-sequencing have become feasible. Furthermore, we have developed a high-throughput methodology to experimentally test thousands of miRNA-3UTR interactions, and have acquired mRNA/miRNA expression data on hundreds of cancer cell lines. These experimental advances have presented us the opportunity to develop state-of-the-art miRNA target prediction algorithms. In this application, we propose to adapt an interdisciplinary approach to develop novel miRNA-target prediction algorithms to overcome the limitations of current prediction algorithms. Our complementary expertise in RNA Bioinformatics and Statistics and large scale experimental testing have placed us in a unique position to pursue the following specific aims: 1) Develop a target prediction algorithm based on deep sequencing data from biochemically purified miRNA-target complexes; 2) Perform large-scale miRNA-3UTR reporter assays to derive functional miRNA-target interactions; 3) Develop algorithms and perform analyses and validation for more informative predictions on the level of miRNA-mediated regulation and miRNA-regulated pathways and networks; 4) Develop user-friendly software tools and database for distribution to the scientific community. This project will advance our understanding of miRNA-mediated gene regulation in molecular biology and systems biology, facilitate development of miRNA-based therapeutics, and in turn benefit human health.

描述（由申请人提供）：MicroRNAs （miRNAs）是一类丰富的小非编码rna，在转录后调控信使rna （mrna）。miRNAs诱导目标信使RNA降解或翻译抑制，主要通过3个非翻译区（3UTRs）的认知结合位点。准确鉴定miRNA靶点对于了解其功能至关重要。靶标鉴定一直是理解这一最近认识到的令人兴奋的基因调控方面的主要挑战。为此，计算目标预测方法已被证明是有价值的。然而，目前的预测方法受到一些限制，阻碍了进展。近年来，生物化学纯化和高通量深度测序已成为可能。此外，我们开发了一种高通量方法来实验测试数千种miRNA- 3utr相互作用，并获得了数百种癌细胞系的mRNA/miRNA表达数据。这些实验进展为我们提供了开发最先进的miRNA靶标预测算法的机会。在这个应用中，我们建议采用跨学科的方法来开发新的mirna靶标预测算法，以克服当前预测算法的局限性。我们在RNA生物信息学和统计学以及大规模实验测试方面的互补专业知识使我们处于独特的位置，可以追求以下具体目标：1)基于生化纯化的mirna -靶标复合物的深度测序数据开发靶标预测算法；2)进行大规模miRNA-3UTR报告基因分析，以获得功能性mirna -靶标相互作用；3)开发算法并进行分析和验证，以对mirna介导的调控水平和mirna调控的途径和网络进行更有信息的预测；4)开发易于使用的软件工具和数据库，以便向科学界分发。该项目将促进我们对分子生物学和系统生物学中mirna介导的基因调控的理解，促进基于mirna的治疗方法的发展，进而造福人类健康。