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

• 批准号: 8791915
• 负责人: Sharon Aviran
• 金额: $ 24.27万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-23 至 2016-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8791915
• 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; Gene Expression Profile; 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; Solutions; 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; meetings; next generation; next generation sequencing; novel; research study; skills; sound; tool; transcriptome sequencing

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结构与功能之间的牢固联系，新型RNA的快节奏发现以及越来越多的使用 生物医学工程中的RNA强调了迅速分析RNA结构动力学的紧迫需求 准确。但是，可用的方法要么是劳动密集型且技术复杂，要么依赖于低 - 基于计算的准确性预测。我们和另外两个小组最近开始满足这一需求 通过将RNA结构映射实验耦合到高通量测序平台，以实现 基因组规模结构信息的产生（Wan等，2011）。结构映射是一种经典的方法 使用化学物质或酶区分成对和未配对的核苷酸，并且具有 在改善其质量和实用性之后，最近获得了广泛使用。但是，该方法确实 没有揭示碱基对身份，也无法直接解决二级结构。尽管如此，计算 方法可以通过其适当的解释和使用从这些信息中受益匪浅。 我们建议通过开发一个可以改善的计算框架来补充这些进步 我们从结构映射实验中推断RNA结构动力学的能力。我们将在我们的基础上建立 先前关于一种统计方法的工作，该方法自动从化学映射中恢复结构信息 数据，我们应用于从新测定中获得的数据，该数据将化学塑造到下一代 测序。我们建议将其扩展到一个完整且统计上声音的算法框架以进行分析 化学映射数据以及随后的数据集成到RNA结构的计算预测中 动力学。在R00阶段，我们将设计有效的算法，当与大规模映射结合使用时 测量值将有助于对序列对结构的影响的可靠和高通量评估 和功能。 K99阶段将提供培训和经验，以在R00阶段进行研究。 特定目标K99.1：开发化学结构映射测定法的实验专业知识。这会 补充我的计算技能，并让我有效地测试我们在R00阶段将开发的工具。 特定目标K99.2：扩展并进一步研究我们分析化学结构的方法 映射数据。该目标包括提案中概述的两个项目，一个项目将使从头开始 以及全基因组映射，以及将为用户提供系统的平台间信息差异的映射。 特定AIM R00.1：开发算法和软件，用于将结构映射数据集成到 基于合奏的方法来分析RNA结构动力学。这将提高质量和 基于计算的结构分析的分辨率。 特定目标R00.2：将开发的工具应用于三个生物系统，以提供证明 工具实用程序的原则。这将证明已开发工具替代电流的潜力 接近并推进未来的RNA工程工作。