RNA snp: Efficient Detection of Local RNA Secondary Structure Changes Induced by SNP s

Abstract

Structural characteristics are essential for the functioning of many noncoding RNAs and cis-regulatory elements of mRNAs. SNPs may disrupt these structures, interfere with their molecular function, and hence cause a phenotypic effect. RNA folding algorithms can provide detailed insights into structural effects of SNPs. The global measures employed so far suffer from limited accuracy of folding programs on large RNAs and are computationally too demanding for genome-wide applications. Here, we present a strategy that focuses on the local regions of maximal structural change between mutant and wild-type. These local regions are approximated in a “screening mode” that is intended for genome-wide applications. Furthermore, localized regions are identified as those with maximal discrepancy. The mutation effects are quantified in terms of empirical P values. To this end, the RNAsnp software uses extensive precomputed tables of the distribution of SNP effects as function of length and GC content. RNAsnp thus achieves both a noise reduction and speed-up of several orders of magnitude over shuffling-based approaches. On a data set comprising 501 SNPs associated with human-inherited diseases, we predict 54 to have significant local structural effect in the untranslated region of mRNAs. RNAsnp is available at http://rth.dk/resources/rnasnp.

Single nucleotide variations: Biological impact and theoretical interpretation

“…For this reason, methods for predicting the impact of SNVs have historically focused on the high-yield category of non-synonymous coding SNVs. The existence of disease-associated synonymous mutations32,33 and nocoding variations with effects on lincRNA,34 miRNA,35,36 and promoters37,38 has produced interest in other types of mutations as well, but different tools will be needed to analyze these types of variations and such tools are comparatively still new and untested 39–41…”

The pre-mir-27a variant rs895819 may contribute to type 2 diabetes mellitus susceptibility in an Iranian cohort

“…To analyze whether rs895819 might change the secondary structure or the free energy of pre-mir-27a, we used RNAsnp [18] and RNAfold [19] to demonstrate that neither the conformation (P dmax > 0.2) nor the free energy are affected. However, it has been reported that a variation in the precursors of a certain miRNA can affect the maturation process and modulate the expression of mature miRNAs.…”

“…To estimate the effects of rs895819 on the transcript structure, we modelled the secondary structure of precursor pre-mir-27a (similar results were also obtained starting with the primitive pre-mir-27a sequence-data not shown) using the RNAsnp software [18] and RNAfold WebServer [19]. RNAsnp compares the vector of base pair probabilities derived for the RNA sequence with and without the SNP.…”

Developing maps of fitness consequences for plant genomes

“…Other methods aim to assess fitness effects of mutations in other annotation classes including microRNAs (mrSNP [39]), non-coding RNAs (RNAsnp [40]), and splice sites (SNPlice [41]). Most of these predictive methods can be implemented in plants and it should become possible to pre-compute exhaustively the effects of all possible mutations in each of these effect classes in well-annotated plant genomes [42].…”