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 i ncludes 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.

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.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Transition Award (R00)
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Special Emphasis Panel (NSS)
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Pazin, Michael J
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University of California Davis
Biomedical Engineering
Schools of Engineering
United States
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Li, Hua; Aviran, Sharon (2018) Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes. Nat Commun 9:606
Ledda, Mirko; Aviran, Sharon (2018) PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures. Genome Biol 19:28
Watters, Kyle E; Choudhary, Krishna; Aviran, Sharon et al. (2018) Probing of RNA structures in a positive sense RNA virus reveals selection pressures for structural elements. Nucleic Acids Res 46:2573-2584
Vaziri, Sana; Koehl, Patrice; Aviran, Sharon (2018) Extracting information from RNA SHAPE data: Kalman filtering approach. PLoS One 13:e0207029
Radecki, Pierce; Ledda, Mirko; Aviran, Sharon (2018) Automated Recognition of RNA Structure Motifs by Their SHAPE Data Signatures. Genes (Basel) 9:
Choudhary, Krishna; Deng, Fei; Aviran, Sharon (2017) Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions. Quant Biol 5:3-24
Norris, Matthew; Kwok, Chun Kit; Cheema, Jitender et al. (2017) FoldAtlas: a repository for genome-wide RNA structure probing data. Bioinformatics 33:306-308
Li, Bo; Tambe, Akshay; Aviran, Sharon et al. (2017) PROBer Provides a General Toolkit for Analyzing Sequencing-Based Toeprinting Assays. Cell Syst 4:568-574.e7
Choudhary, Krishna; Ruan, Luyao; Deng, Fei et al. (2017) SEQualyzer: interactive tool for quality control and exploratory analysis of high-throughput RNA structural profiling data. Bioinformatics 33:441-443
Deng, Fei; Ledda, Mirko; Vaziri, Sana et al. (2016) Data-directed RNA secondary structure prediction using probabilistic modeling. RNA 22:1109-19

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