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This article is part of the supplement: SNP-SIG 2011: Identification and annotation of SNPs in the context of structure, function and disease

Open Access Proceedings

Evaluating our ability to predict the structural disruption of RNA by SNPs

Justin Ritz, Joshua S Martin and Alain Laederach*

  • * Corresponding author: Alain Laederach alain@unc.edu

  • † Equal contributors

Author Affiliations

Department of Biology, University of North Carolina, Chapel Hill, NC, 27599, USA

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BMC Genomics 2012, 13(Suppl 4):S6  doi:10.1186/1471-2164-13-S4-S6

Published: 18 June 2012

Abstract

The structure of RiboNucleic Acid (RNA) has the potential to be altered by a Single Nucleotide Polymorphism (SNP). Disease-associated SNPs mapping to non-coding regions of the genome that are transcribed into RiboNucleic Acid (RNA) can potentially affect cellular regulation (and cause disease) by altering the structure of the transcript. We performed a large-scale meta-analysis of Selective 2'-Hydroxyl Acylation analyzed by Primer Extension (SHAPE) data, which probes the structure of RNA. We found that several single point mutations exist that significantly disrupt RNA secondary structure in the five transcripts we analyzed. Thus, every RNA that is transcribed has the potential to be a “RiboSNitch;” where a SNP causes a large conformational change that alters regulatory function. Predicting the SNPs that will have the largest effect on RNA structure remains a contemporary computational challenge. We therefore benchmarked the most popular RNA structure prediction algorithms for their ability to identify mutations that maximally affect structure. We also evaluated metrics for rank ordering the extent of the structural change. Although no single algorithm/metric combination dramatically outperformed the others, small differences in AUC (Area Under the Curve) values reveal that certain approaches do provide better agreement with experiment. The experimental data we analyzed nonetheless show that multiple single point mutations exist in all RNA transcripts that significantly disrupt structure in agreement with the predictions.