Modeling RNA secondary structure folding ensembles using SHAPE mapping data

Aleksandar Spasic,Sarah M Assmann,Philip C Bevilacqua,David H Mathews

doi:10.1093/nar/gkx1057

Abstract

RNA secondary structure prediction is widely used for developing hypotheses about the structures of RNA sequences, and structure can provide insight about RNA function. The accuracy of structure prediction is known to be improved using experimental mapping data that provide information about the pairing status of single nucleotides, and these data can now be acquired for whole transcriptomes using high-throughput sequencing. Prior methods for using these experimental data focused on predicting structures for sequences assuming that they populate a single structure. Most RNAs populate multiple structures, however, where the ensemble of strands populates structures with different sets of canonical base pairs. The focus on modeling single structures has been a bottleneck for accurately modeling RNA structure. In this work, we introduce Rsample, an algorithm for using experimental data to predict more than one RNA structure for sequences that populate multiple structures at equilibrium. We demonstrate, using SHAPE mapping data, that we can accurately model RNA sequences that populate multiple structures, including the relative probabilities of those structures. This program is freely available as part of the RNAstructure software package.

Highlights

RNA has many important roles in the cell besides being a simple carrier of genetic information [1]
RNA sequences are an essential part of translation through tRNA and rRNAs [2], they play a role in regulating gene expression through microRNAs and small interfering RNAs [3], and in RNA processing through small nuclear
Knowledge of RNA structure is key in understanding the intermolecular interactions of small RNAs with their mRNA targets, as well as in understanding RNAprotein interactions

Summary

Introduction

RNA has many important roles in the cell besides being a simple carrier of genetic information [1]. RNA structure can be mapped experimentally at the level of individual nucleotides using enzymatic or chemical methods [9,10]. Recent work focused on acquiring these experimental mapping data for entire transcriptomes [15,16,17] and on acquiring these data in vivo [18,19,20] While these and other chemical mapping data provide information about the likelihood that a nucleotide is base paired, they do not directly provide the structure of the RNA. The mapping data have been used to restrain secondary structure prediction These structural models provide important insights into RNA structure and function

Methods

Results

Conclusion