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Methods for RNA structural analysis using computation and structure mapping exper

使用计算和结构作图实验进行 RNA 结构分析的方法

基本信息

批准号：
8995224
负责人：
Sharon Aviran
金额：
$ 23.3万
依托单位：
UNIVERSITY OF CALIFORNIA AT DAVIS
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：
2012-07-23 至 2017-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8995224
关键词：
Acylation Address Algorithmic Software Algorithms Base Pairing Bioinformatics Biological Assay Biology Biomedical Engineering Biotechnology Blood capillaries Caenorhabditis elegans Chemical Structure Chemicals Chemistry Complement Complex Computing Methodologies Coupled Couples Coupling Data Data Set Engineering Enzymes Future Generations Glean Goals High-Throughput Nucleotide Sequencing Hydroxyl Radical Knowledge Link Maps Measurement Methodology Methods MicroRNAs Molecular Molecular Conformation Nucleotides Phase Primer Extension Property Protocols documentation RNA RNA Conformation Research Research Infrastructure Resolution Sampling Statistical Methods Statistical Models Structure Structure-Activity Relationship Students System Techniques Technology Testing Therapeutic Time Training Untranslated RNA Work base biological systems capillary case-by-case basis computer framework cost effective data integration design experience genome-wide high throughput analysis improved laboratory experience meetings next generation next generation sequencing novel research study sequencing platform skills sound tool transcriptome transcriptome sequencing

项目摘要

Project Summary Strong links between RNA structure and function, fast-paced discoveries of novel RNAs, and a growing use of RNAs in biomedical engineering underscore a pressing need to analyze RNA structural dynamics rapidly and accurately. Yet, available methods are either labor intensive and technologically complex, or rely on low- accuracy computation-based prediction. We, and two other groups, have recently begun addressing this need by coupling RNA structure mapping experiments to high-throughput sequencing platforms, to enable the generation of genome-scale structural information (Wan et al. 2011). Structure mapping is a classical approach that uses chemicals or enzymes to discriminate between paired and unpaired nucleotides, and which has recently gained widespread use, following improvements to its quality and utility. However, the method does not reveal base-pairs identities and cannot directly resolve secondary structure. Nonetheless, computational approaches can greatly benefit from this wealth of information through its proper interpretation and use. We propose to complement these advances by developing a computational framework that will improve our ability to infer RNA structural dynamics from structure mapping experiments. We will build on our previous work on a statistical method that automatically recovers structural information from chemical mapping data, which we applied to data obtained from a new assay that couples SHAPE chemistry to next-generation sequencing. We propose to extend it into a complete and statistically sound algorithmic framework for analysis of chemical mapping data and for subsequent data integration into computational prediction of RNA structure dynamics. In the R00 phase, we will design efficient algorithms that, when combined with large-scale mapping measurements, will facilitate reliable and high-throughput assessment of the impact of sequence on structure and function. The K99 phase will provide the training and experience to pursue research in the R00 phase. Specific Aim K99.1: Develop experimental expertise in chemical structure mapping assays. This will complement my computational skills and allow me to efficiently test the tools we will develop in the R00 phase. Specific Aim K99.2: Extend and further investigate our method for analysis of chemical structure mapping data. This aim includes two projects that are outlined in the proposal, one that will enable de novo and genome-wide mapping and one that will inform users of systematic inter-platform information differences. Specific Aim R00.1: Develop algorithms and software for integrating structure mapping data into ensemble-based approaches to analyzing RNA structural dynamics. This will improve the quality and resolution of computation-based structural analysis. Specific Aim R00.2: Apply the developed tools to three biological systems, to provide a proof of principle for the tools' utility. This will demonstrate the potential of the developed tools to substitute current approaches and to advance future RNA engineering efforts.

项目摘要 RNA结构和功能之间的紧密联系，新RNA的快速发现，以及对生物医学工程中的RNA强调了快速分析RNA结构动力学和准确地说。然而，可用的方法要么是劳动密集型和技术复杂的，要么依赖于低成本的基于精确度计算的预测。我们和另外两个小组最近已经开始解决这一需求通过将RNA结构映射实验耦合到高通量测序平台，使基因组规模结构信息的生成(Vanet al.2011年)。结构映射是一种经典的方法它使用化学物质或酶来区分配对和未配对的核苷酸，并且它已经随着其质量和用途的改进，最近获得了广泛的使用。然而，该方法做到了不能揭示碱基对的身份，也不能直接解析二级结构。尽管如此，计算通过正确地解释和使用这些丰富的信息，方法可以极大地受益。我们建议通过开发一个计算框架来补充这些进展，该框架将改善我们从结构映射实验中推断RNA结构动力学的能力。我们将在我们的以前在统计方法方面的工作是从化学图谱中自动恢复结构信息数据，我们应用于从一种将形状化学与下一代相关联的新测试中获得的数据测序。我们建议将其扩展为一个完整的、统计上可靠的分析算法框架以及用于将后续数据集成到RNA结构的计算预测中动力学。在R00阶段，我们将设计高效的算法，当与大规模地图绘制相结合时测量，将有助于可靠和高通量地评估序列对结构的影响和功能。K99阶段将提供在R00阶段进行研究的培训和经验。具体目标K99.1：发展化学结构图谱分析方面的实验专业知识。这将是补充我的计算技能，并允许我有效地测试我们将在R00阶段开发的工具。具体目标K99.2：扩展和进一步研究我们的化学结构分析方法映射数据。这一目标包括提案中概述的两个项目，其中一个将使从头开始。以及全基因组绘图，并将告知用户系统性的平台间信息差异。具体目标R00.1：开发将结构映射数据集成到基于系综的RNA结构动力学分析方法。这将提高质量和基于计算的结构分析的解析。具体目标R00.2：将开发的工具应用于三个生物系统，以提供证据工具效用的原则。这将证明所开发的工具有潜力取代当前的方法，并推进未来的RNA工程努力。