riboSNitch detection software tools | RNA structure data analysis
Ribonucleic acid (RNA) secondary structure prediction continues to be a significant challenge, in particular when attempting to model sequences with less rigidly defined structures, such as messenger and non-coding RNAs. Crucial to interpreting RNA structures as they pertain to individual phenotypes is the ability to detect RNAs with large structural disparities caused by a single nucleotide variant (SNV) or riboSNitches.
A program that is structure based and relies on RNA secondary-structure prediction has been developed for assisting in RNA mutational analysis. RNAmute has been extended from single-point mutations to treat multiple-point mutations efficiently by initially calculating all suboptimal solutions, after which only the mutations that stabilize the suboptimal solutions and destabilize the optimal one are considered as candidates for being deleterious.
Provides several efficient tools to compute the ensemble of low-energy secondary structures for all k-mutants of a given RNA sequence, where k is bounded by a user-specified upper bound. In contrast to exhaustive enumeration, which is only possible for tiny sequences, RNAmutants uses dynamic programming to provide a complete analysis of the mutational landscape for a given RNA sequence.
Single-nucleotide polymorphisms (SNPs) are often linked to critical phenotypes such as diseases or responses to vaccines, medications and environmental factors. However, the specific molecular mechanisms by which a causal SNP acts is usually not obvious. Changes in RNA secondary structure emerge as a possible explanation necessitating the development of methods to measure the impact of single-nucleotide variation on RNA structure. To answer this need, remuRNA commutes the relative entropy between the Boltzmann ensembles of the native and a mutant structure.
Recognizes RNA structure change in large amounts of Selective 20 Hydroxyl Acylation by Primer Extension (SHAPE) data. classSNitch provides methods for normalization, noise reduction, and calculating features. It is a good approximation of human expert classification of SHAPE trace differences. This tool can be applied to high-throughput mutational datasets and can simulate human consensus classification of these data.
Helps identify RiboSNitches by leveraging GWAS data and an analysis of the mRNA structural ensemble. When running SNPfold, the sequence and mutations are required at bare minimum. The user can either paste the sequence directly into the command line command to run the program, or list the name of a two-line fasta file containing the sequence of interest.
An online tool for evaluating structural deleteriousness of single nucleotide mutation in RNA genes. Several structure comparison methods have been integrated; sub-optimal structures predicted can be optionally involved to mitigate the uncertainty of secondary structure prediction. With a user-friendly interface, the web application is easy to use. Intuitive illustrations are provided along with the original computational results to facilitate quick analysis.