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Transcending the prediction paradigm: novel applications of SHAPE to RNA function and evolution
Author(s) -
Kutchko Katrina M.,
Laederach Alain
Publication year - 2016
Publication title -
wiley interdisciplinary reviews: rna
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.225
H-Index - 71
eISSN - 1757-7012
pISSN - 1757-7004
DOI - 10.1002/wrna.1374
Subject(s) - rna , nucleic acid structure , computational biology , nucleic acid secondary structure , context (archaeology) , non coding rna , computer science , riboswitch , function (biology) , biology , genetics , gene , paleontology
Selective 2′‐hydroxyl acylation analyzed by primer extension ( SHAPE ) provides information on RNA structure at single‐nucleotide resolution. It is most often used in conjunction with RNA secondary structure prediction algorithms as a probabilistic or thermodynamic restraint. With the recent advent of ultra‐high‐throughput approaches for collecting SHAPE data, the applications of this technology are extending beyond structure prediction. In this review, we discuss recent applications of SHAPE data in the transcriptomic context and how this new experimental paradigm is changing our understanding of these experiments and RNA folding in general. SHAPE experiments probe both the secondary and tertiary structure of an RNA , suggesting that model‐free approaches for within and comparative RNA structure analysis can provide significant structural insight without the need for a full structural model. New methods incorporating SHAPE at different nucleotide resolutions are required to parse these transcriptomic data sets to transcend secondary structure modeling with global structural metrics. These ‘multiscale’ approaches provide deeper insights into RNA global structure, evolution, and function in the cell. WIREs RNA 2017, 8:e1374. doi: 10.1002/wrna.1374 This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution RNA Evolution and Genomics > Computational Analyses of RNA