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

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.

QuShape: Rapid, accurate, and best-practices quantification of nucleic acid probing information, resolved by capillary electrophoresis

“…There is a critical balance to be struck between processing speed, pipeline simplicity, and degree of automation. For example, some high-throughput processing approaches yield sequence misalignments and integration errors (as reported by Leonard et al 2012;Ritz et al 2012). The ShapeFinder package ) is the most widely used among current software tools and ultimately yields final data sets of high quality.…”

Multiple conformations are a conserved and regulatory feature of the RB1 5′ UTR

“…A riboSNitch is broadly defined as an element in a noncoding RNA or an untranslated region (UTR) of an mRNA where a single nucleotide variant (SNV) results in a functionally important structural rearrangement (Halvorsen et al 2010;Martin et al 2012;Ritz et al 2012;Lokody 2014;Wan et al 2014). It is similar to a bacterial riboswitch, where binding of a small molecule results in a conformational rearrangement and gene regulation (Mandal et al 2003;Tucker and Breaker 2005;Weinberg et al 2007.…”

Describing the Structural Diversity within an RNA’s Ensemble

“…The correlation coefficient between the pairing probability (CC) measures the similarity between the ensemble of two sequences of the same length [24]. The CC has been used quite effectively in identifying single nucleotide polymorphisms that could result in disease phenotypes [79]. The relationship between 1−CC and the ensemble defect (A) and average Shannon entropy (B) for the 'unstructured' 5' UTR of the human FTL gene for all possible single-nucleotide polymorphisms.…”