Novel Approaches to Mammalian MicroRNA Target Prediction

哺乳动物 MicroRNA 目标预测的新方法

• 批准号: 8656366
• 负责人: YE DING
• 金额: $ 61.33万
• 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/8656366
• 关键词: 3' 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; Functional RNA; 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; 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）：微小RNA（miRNA）是一类丰富的小的非编码RNA，其在转录后调节信使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信使RNA降解或翻译抑制。准确鉴定miRNA靶点对于理解其功能至关重要。目标识别一直是一个重大的挑战，了解这个最近公认的令人兴奋的基因调控方面。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限制，阻碍了进展。最近，生化纯化和高通量深度测序已经变得可行。此外，我们还开发了一种高通量的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。 在本申请中，我们建议采用跨学科的方法来开发新的miRNA靶点预测算法，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一个独特的位置，可以追求以下特定目标：1）基于生物化学纯化的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，以获得关于miRNA介导的调节和miRNA调节的途径和网络水平的更多信息预测; 4）开发用户友好的软件工具和数据库，以分发给科学界。该项目将促进我们在分子生物学和系统生物学中对miRNA介导的基因调控的理解，促进基于miRNA的治疗方法的开发，从而有益于人类健康。