Methods for RNA structural analysis using computation and structure mapping exper

项目摘要 RNA结构和功能之间的紧密联系，新RNA的快速发现，以及越来越多的使用 生物医学工程中的RNA强调了快速分析RNA结构动力学的迫切需要， 准确地然而，现有的方法要么是劳动密集型和技术复杂的，要么依赖于低- 基于计算的准确性预测。我们和另外两个团体最近已开始解决这一需要 通过将RNA结构作图实验与高通量测序平台相结合， 基因组规模的结构信息的生成（Wan et al. 2011）。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， 最近得到了广泛的使用，随着其质量和实用性的提高。然而，该方法 不显示碱基对身份，不能直接解析二级结构。然而，计算 通过适当的解释和使用，各种方法可大大受益于这一丰富的信息。 我们建议通过开发一个计算框架来补充这些进展， 我们从结构绘图实验中推断RNA结构动力学的能力。我们将建立在我们的 以前的工作，统计方法，自动恢复结构信息，从化学映射 数据，我们将其应用于从将SHAPE化学与下一代 测序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进行分析 以及随后的数据集成到RNA结构的计算预测中 动力学在R 00阶段，我们将设计有效的算法，当与大规模映射相结合时， 测量，将有助于可靠和高通量评估序列对结构的影响 和功能K99阶段将为R 00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R 00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 和全基因组图谱，以及一个将告知用户系统的平台间信息差异。 具体目标R00.1：开发算法和软件，用于将结构映射数据整合到 基于集合的方法来分析RNA结构动力学。这将提高质量， 基于计算的结构分析的解决方案。 具体目标R00.2：将开发的工具应用于三个生物系统，以提供 工具的使用原则。这将证明所开发的工具替代现有工具的潜力 方法和推进未来的RNA工程的努力。

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.