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Mutational analysis in RNAs: Comparing programs for RNA deleteriousmutation prediction

    Research output: Contribution to journalArticlepeer-review

    22 Scopus citations

    Abstract

    Programs for RNA mutational analysis that are structure-based and rely on secondary structure prediction have been developed and expanded in the past several years. They can be used for a variety of purposes, such as in suggesting point mutations that will alter RNA virus replication or translation initiation, investigating the effect of deleterious and compensatory mutations in allosteric ribozymes and riboswitches, computing an optimal path of mutations to get from one ribozyme fold to another, or analyzing regulatory RNA sequences by their mutational profile. This review describes three different freeware programs (RNAMute, RDMAS and RNAmutants) that have been developed for such purposes. RNAMute and RDMAS in principle perform energy minimization prediction by available software such as RNAfold from the Vienna RNA package or Zuker's Mfold, while RNAmutants provides an efficient method using essential ingredients from energy minimization prediction. Both RNAMute in its extended version that uses RNAsubopt from the Vienna RNA package and the RNAmutants software are able to predict multiple-pointmutations using developedmethodologies, while RDMAS is currently restricted to single-pointmutations. The strength of RNAMute in its extended version is the ability to predict a small number of point mutations in an accurate manner. RNAmutants is well fit for large scale simulations involving the calculation of all k-mutants, where k can be a large integer number, of a given RNA sequence.

    Original languageEnglish
    Article numberbbq059
    Pages (from-to)104-114
    Number of pages11
    JournalBriefings in Bioinformatics
    Volume12
    Issue number2
    DOIs
    StatePublished - 1 Mar 2011

    Keywords

    • RNA mutational analysis
    • RNA secondary structure prediction

    ASJC Scopus subject areas

    • Information Systems
    • Molecular Biology

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