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.

项目概要 RNA结构和功能之间的紧密联系、新型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 结构动力学和准确。然而，可用的方法要么是劳动密集型且技术复杂，要么依赖于低基于精度计算的预测。我们和其他两个团体最近开始解决这一需求通过将RNA结构图谱实验与高通量测序平台相结合，使生成基因组规模的结构信息（Wan et al. 2011）。结构映射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，并且具有随着其质量和实用性的改进，最近获得了广泛的使用。然而，该方法确实不揭示碱基对身份，也不能直接解析二级结构。尽管如此，计算通过正确的解释和使用，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步，该框架将改进我们从结构作图实验推断 RNA 结构动力学的能力。我们将在我们的基础上再接再厉先前关于从化学图谱中自动恢复结构信息的统计方法的工作数据，我们将其应用于从将 SHAPE 化学与下一代耦合的新测定中获得的数据测序。我们建议将其扩展为一个完整且统计上合理的分析算法框架化学图谱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，我们将设计高效的算法，与大规模建图相结合测量，将有助于对序列对结构的影响进行可靠和高通量的评估和功能。 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，让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，其中一个项目将使从头开始以及全基因组图谱，它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集成的方法来分析 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。具体目标 R00.2：将开发的工具应用于三个生物系统，以提供证明工具实用性的原则。这将证明所开发的工具替代当前工具的潜力方法并推进未来的 RNA 工程工作。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.

PATTERNA：通过结构数据签名在转录组范围内搜索功能性 RNA 元素。

DOI：
发表时间：
2018-03-01
期刊：
影响因子：
12.3
作者：
Ledda, Mirko;Aviran, Sharon
通讯作者：
Aviran, Sharon

Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions.

RNA 结构分析数据的比较和综合分析：当前实践和新出现的问题。

DOI：
发表时间：
2017-03
期刊：
影响因子：
0
作者：
Choudhary, Krishna;Deng, Fei;Aviran, Sharon
通讯作者：
Aviran, Sharon

SEQualyzer: interactive tool for quality control and exploratory analysis of high-throughput RNA structural profiling data.

SEQualyzer：用于高通量 RNA 结构分析数据的质量控制和探索性分析的交互式工具。

DOI：
发表时间：
2017-02-01
期刊：
影响因子：
0
作者：
Choudhary, Krishna;Ruan, Luyao;Deng, Fei;Shih, Nathan;Aviran, Sharon
通讯作者：
Aviran, Sharon

Probing of RNA structures in a positive sense RNA virus reveals selection pressures for structural elements.

对RNA病毒积极意义的RNA结构的探索揭示了结构元件的选择压力。

DOI：
发表时间：
2018-03-16
期刊：
影响因子：
14.9
作者：
Watters, Kyle E;Choudhary, Krishna;Aviran, Sharon;Lucks, Julius B;Perry, Keith L;Thompson, Jeremy R
通讯作者：
Thompson, Jeremy R

Automated Recognition of RNA Structure Motifs by Their SHAPE Data Signatures.

通过形状数据签名自动识别 RNA 结构基序。