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Novel Approaches to Mammalian MicroRNA Target Prediction

哺乳动物 MicroRNA 目标预测的新方法

基本信息

批准号：
8837027
负责人：
YE DING
金额：
$ 60.71万
依托单位：
WADSWORTH CENTER
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-07-18 至 2017-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8837027
关键词：
3&apos Untranslated Regions Algorithms Apoptosis Arts Bayesian Modeling 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 Gene Expression Gene Expression Regulation Genes Goals Health High-Throughput Nucleotide Sequencing 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 Untranslated RNA Validation Virus base computerized tools crosslink crosslinking and immunoprecipitation sequencing data modeling deep sequencing human disease improved insight interdisciplinary approach novel novel strategies robot assistance statistics therapeutic miRNA tool transcriptome sequencing user friendly software virology web services

项目摘要

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）：MicroRNA (miRNA) 是一类丰富的小非编码 RNA，可在转录后调节信使 RNA (mRNA)。 miRNA 主要通过 3 个非翻译区 (3UTR) 中的认知结合位点诱导靶标信使 RNA 降解或翻译抑制。准确识别 miRNA 靶点对于理解其功能至关重要。目标识别一直是理解最近认识到的令人兴奋的基因调控维度的主要挑战。为此，计算目标预测方法已被证明是有价值的。然而，当前的预测方法存在许多阻碍进展的限制。最近，生化纯化和高通量深度测序已变得可行。此外，我们开发了一种高通量方法来通过实验测试数千种 miRNA-3UTR 相互作用，并获取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 目标预测算法的机会。在此应用中，我们建议采用跨学科方法来开发新型 miRNA 目标预测算法，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地位，可以实现以下具体目标： 1) 基于来自生化纯化的 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 报告基因检测，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 介导的调控以及 miRNA 调控的途径和网络水平进行更丰富的预测； 4) 开发用户友好的软件工具和数据库以分发给科学界。该项目将增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 的疗法的开发，进而造福人类健康。