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New regulatory roles continue to emerge for both natural and engineered RNAs, many of which have specific structures essential to their function. This highlights a growing need to develop technologies that enable rapid and accurate characterization of structural features within complex RNA populations. Yet, available techniques that are reliable are vastly limited by technological constraints, whereas the accuracy of popular computational methods is generally poor. These limitations pose a major barrier to comprehensive determination of RNA structure from sequence.

Recently, novel high-throughput approaches to RNA structure determination have emerged. These facilitate parallel and high-resolution measurements of structural information for a multitude of distinct RNAs in a single experiment, thus generating unprecedented amounts of structural data (aka “genomic big data”). One of these techniques, SHAPE-Seq, was the first to couple a chemistry-based approach to structure determination with next-generation DNA sequencing of its products. Following sequencing, an algorithmic pipeline recovers the desired information from noisy experimental data, representing a new approach to rapid, consistent, and automated analysis of this wealth of information.

In this talk, I will describe SHAPE-Seq and its analysis method, which relies on a novel probabilistic model of this emerging family of techniques. I will then discuss recent breakthroughs in experimental structure determination along with the informatics challenges they present. I will conclude with reviewing recent statistical inference approaches to informing computational structure prediction from these experimental data.