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RNA molecules can achieve a broad range of regulatory functions through specific structures that are in turn determined by their sequence. The prediction of mutations changing the structural properties of RNA sequences (a.k.a. deleterious mutations) is therefore useful for conducting mutagenesis experiments and synthetic biology applications. While brute force approaches can be used to analyze single-point mutations, this strategy does not scale well to multiple mutations. In this article, we present corRna a web server for predicting the multiple-point deleterious mutations in structural RNAs. corRna uses our RNAmutants framework to efficiently explore the RNA mutational landscape. It also enables users to apply search heuristics to improve the quality of the predictions. We show that corRna predictions correlate with mutagenesis experiments on the hepatitis C virus cis-acting replication element as well as match the accuracy of previous approaches on a large test-set in a much lower execution time. We illustrate these new perspectives offered by corRna by predicting five-point deleterious mutations--an insight that could not be achieved by previous methods. corRna is available at: http://corrna.cs.mcgill.ca.

corRna: a web server for predicting multiple-point deleterious mutations in structural RNAs