376466 Systematic Analysis, Optimization and Extension of High-Throughput RNA Structure Probing with Shape-Seq

Monday, November 17, 2014: 12:48 PM
214 (Hilton Atlanta)
David Loughrey, School of Chemical & Biomolecular Engineering, Cornell University, Ithaca, NY and Julius B. Lucks, School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY

RNA structure is a primary determinant of its function, and there is great interest in methods that can characterize RNA structures in high-throughput. Recently, techniques that merge chemical probing of RNA structure with next generation sequencing have created breakthroughs in the throughput and scale of RNA structure characterization. However, little work has been done to examine the effects of library preparation and sequencing steps of these techniques on the measured structural information, which is critical for accurate data interpretation. Here, we present a thorough analysis and optimization of the SHAPE-Seq technique that combines Selective 2’-Hydroxyl Acylation Analyzed by Primer Extension (SHAPE), with next generation sequencing. We assess reactivity data generated by SHAPE-Seq over a benchmark panel of RNAs to that generated by the QuSHAPE capillary electrophoresis-based method. We also optimize the steps of SHAPE-Seq associated with library preparation and sequencing, and show that SHAPE-Seq provides highly reproducible nucleotide-resolution reactivity data over a wide range of structural contexts, with no apparent biases. We also present SHAPE-Seq v2.0, which uses a ‘universal’ reverse-transcriptase priming strategy to reveal reactivity information for every nucleotide in an RNA without having to use or introduce a specific reverse transcriptase priming site within the RNA sequence itself. We show that SHAPE-Seq v2.0 is also highly reproducible, and that incorporating SHAPE-Seq v2.0 reactivity data as constraints in structural prediction algorithms yields predictions on par with those generated using data from other SHAPE methods. This work demonstrates that the SHAPE-Seq methods do not bias the measurement of structure-dependent chemical reactivity data. We anticipate SHAPE-Seq v2.0 to be broadly applicable to understanding the RNA sequence-structure relationship at the heart of some of life’s most fundamental processes.

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