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：
8354539
负责人：
Sharon Aviran
金额：
$ 10.32万
依托单位：
UNIVERSITY OF CALIFORNIA BERKELEY
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 2013-12-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8354539
关键词：
Acylation Address Algorithms Base Pairing Bioinformatics Biological Assay Biology Biomedical Engineering Biotechnology Blood capillaries Caenorhabditis elegans Chemical Structure Chemicals Chemistry Complement Complex Computer software Computing Methodologies Coupled Couples Coupling Data Data Set Engineering Enzymes Functional RNA Future Gene Expression Profile Generations Genome Glean Goals 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 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 novel research study skills sound tool

项目摘要

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.

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且技术复杂，要么依赖于低准确度的基于计算的预测。我们和其他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相结合来满足这一需求，以生成基因组规模的结构信息（Wan 等人，2011）。结构作图是一种经典方法，使用化学物质或酶来区分配对和未配对的核苷酸，随着其质量和实用性的改进，该方法最近得到了广泛的应用。然而，该方法不揭示碱基对身份，并且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作图实验推断 RNA 结构动力学的能力。我们将在之前的工作基础上建立一种统计方法，该方法可以自动从化学图谱数据中恢复结构信息，并将其应用于从将 SHAPE 化学与下一代测序相结合的新测定中获得的数据。我们建议将其扩展为一个完整且统计上合理的算法框架，用于分析化学图谱数据以及将后续数据集成到 RNA 结构动力学的计算预测中。在R00阶段，我们将设计高效的算法，与大规模绘图测量相结合，将有助于对序列对结构和功能的影响进行可靠和高通量的评估。 K99 阶段将为 R00 阶段的研究提供培训和经验。具体目标 K99.1：培养化学结构图分析方面的实验专业知识。这将补充我的计算技能，并使我能够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谱数据分析方法。这一目标包括提案中概述的两个项目，一个项目将实现从头和全基因组绘图，另一个项目将告知用户系统性的平台间信息差异。具体目标 R00.1：开发算法和软件，将结构作图数据集成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。这将证明所开发的工具具有替代当前方法并推进未来 RNA 工程工作的潜力。公共健康相关性：RNA 结构和功能之间的密切联系、新型 RNA 的最新发现以及 RNA 在生物医学工程中的日益增长的使用，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且技术复杂，要么依赖于低准确度的基于计算的预测。我们将利用最近对 RNA 结构表征测定的通量和准确性的改进，并在我们为分析此类测定而开发的现有解决方案的基础上，将创建一个平台和通用基础设施，用于使用这些数据对 RNA 二级结构进行高通量分析，以改进当前的计算结构预测能力。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes.

RNA 结构分析实验的统计建模可以简化结构景观的重建。

DOI：
发表时间：
2018-02-09
期刊：
影响因子：
16.6
作者：
Li, Hua;Aviran, Sharon
通讯作者：
Aviran, Sharon

SHAPE-Seq: High-Throughput RNA Structure Analysis.

SHAPE-Seq：高通量 RNA 结构分析。

DOI：
发表时间：
2012-12-01
期刊：
影响因子：
0
作者：
Mortimer, Stefanie A;Trapnell, Cole;Aviran, Sharon;Pachter, Lior;Lucks, Julius B
通讯作者：
Lucks, Julius B

Metrics for rapid quality control in RNA structure probing experiments.

RNA 结构探测实验中快速质量控制的指标。

DOI：
发表时间：
2016-12-01
期刊：
影响因子：
0
作者：
Choudhary, Krishna;Shih, Nathan P;Deng, Fei;Ledda, Mirko;Li, Bo;Aviran, Sharon
通讯作者：
Aviran, Sharon

FoldAtlas: a repository for genome-wide RNA structure probing data.

FoldAtlas：全基因组 RNA 结构探测数据的存储库。

DOI：
发表时间：
2017
期刊：
影响因子：
0
作者：
Norris, Matthew;Kwok, Chun Kit;Cheema, Jitender;Hartley, Matthew;Morris, Richard J;Aviran, Sharon;Ding, Yiliang
通讯作者：
Ding, Yiliang