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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.

项目总结