Methods for RNA structural analysis using computation and structure mapping exper

描述（由申请人提供）：RNA结构和功能之间的紧密联系，新型RNA的快速发现，以及RNA在生物医学工程中的日益增长的使用，都强调了快速准确地分析RNA结构动力学的迫切需要。然而，可用的方法要么是劳动密集型和技术复杂的，要么依赖于低精度的基于计算的预测。我们和另外两个小组最近开始通过将RNA结构图谱实验与高通量测序平台结合来解决这一需求，以生成基因组规模的结构信息（Wan et al. 2011）。结构图谱是一种经典的方法，它使用化学物质或酶来区分配对和未配对的核苷酸，并且随着其质量和实用性的改进，最近得到了广泛的使用。然而，该方法不能揭示碱基对身份，也不能直接解析二级结构。尽管如此，通过正确的解释和使用，计算方法可以从这些丰富的信息中受益匪浅。我们建议通过开发一个计算框架来补充这些进展，该框架将提高我们从结构映射实验中推断RNA结构动力学的能力。我们将建立在我们以前的工作的统计方法，自动恢复结构信息的化学映射数据，我们应用于从一个新的分析，耦合SHAPE化学下一代测序获得的数据。我们建议将其扩展为一个完整的和统计上合理的算法框架，用于分析化学映射数据和随后的数据集成到RNA结构动力学的计算预测。在R00阶段，我们将设计有效的算法，当与大规模映射测量相结合时，将有助于可靠和高通量评估序列对结构和功能的影响。K99阶段将为R00阶段的研究提供培训和经验。具体目标K99.1：培养化学结构图谱分析方面的实验专业知识。这将补充我的计算技能，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：扩展并进一步研究我们的化学结构图数据分析方法。这一目标包括提案中概述的两个项目，一个将使从头和全基因组绘图成为可能，另一个将使用户了解系统的平台间信息差异。具体目标R00.1：开发算法和软件，将结构映射数据整合到基于集成的方法中，以分析RNA结构动力学。这将提高基于计算的结构分析的质量和分辨率。具体目标R00.2：将开发的工具应用于三个生物系统，为工具的实用性提供原理证明。这将证明开发工具的潜力，以取代目前的方法，并推动未来的RNA工程的努力。 公共卫生相关性：RNA结构和功能之间的紧密联系，最近新RNA的快速发现，以及RNA在生物医学工程中的越来越多的应用，都迫切需要快速准确地分析RNA结构动力学。然而，现有的方法要么是劳动密集型和技术复杂的，或者依赖于低精度的基于计算的预测。我们将利用最近改进的RNA结构表征分析的通量和准确性，并建立在现有的解决方案，我们已经开发了分析这样的测定，将创建一个平台和一般的基础设施，用于高通量分析RNA二级结构使用这些数据，以提高目前的计算结构预测能力。

DESCRIPTION (provided by applicant): 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 toos to substitute current approaches and to advance future RNA engineering efforts. PUBLIC HEALTH RELEVANCE: Strong links between RNA structure and function, recent 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 will leverage recent improvements to the throughput and accuracy of RNA structure characterization assays and, building on an existing solution we have developed for analysis of such assays, will create a platform and general infrastructure for high-throughput analysis of RNA secondary structure using these data, to improve upon current computational structure prediction capabilities.