Literature DB >> 27064082

Characterizing RNA structures in vitro and in vivo with selective 2'-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq).

Kyle E Watters1, Angela M Yu2, Eric J Strobel1, Alex H Settle1, Julius B Lucks3.   

Abstract

RNA molecules adopt a wide variety of structures that perform many cellular functions, including, among others, catalysis, small molecule sensing, and cellular defense. Our ability to characterize, predict, and design RNA structures are key factors for understanding and controlling the biological roles of RNAs. Fortunately, there has been rapid progress in this area, especially with respect to experimental methods that can characterize RNA structures in a high throughput fashion using chemical probing and next-generation sequencing. Here, we describe one such method, selective 2'-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq), which measures nucleotide resolution flexibility information for RNAs in vitro and in vivo. We outline the process of designing and performing a SHAPE-Seq experiment and describe methods for using experimental SHAPE-Seq data to restrain computational folding algorithms to generate more accurate predictions of RNA secondary structure. We also provide a number of examples of SHAPE-Seq reactivity spectra obtained in vitro and in vivo and discuss important considerations for performing SHAPE-Seq experiments, both in terms of collecting and analyzing data. Finally, we discuss improvements and extensions of these experimental and computational techniques that promise to deepen our knowledge of RNA folding and function.
Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  In vivo RNA structure; RNA; RNA structure prediction; RNA structure probing; RNA-ligand interactions; SHAPE; SHAPE-Seq

Mesh:

Substances:

Year:  2016        PMID: 27064082      PMCID: PMC4921265          DOI: 10.1016/j.ymeth.2016.04.002

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


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