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SimulFold: simultaneously inferring RNA structures including pseudoknots, alignments, and trees using a Bayesian MCMC framework

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Item Type:Article
Title:SimulFold: simultaneously inferring RNA structures including pseudoknots, alignments, and trees using a Bayesian MCMC framework
Creators Name:Meyer, I.M. and Miklos, I.
Abstract:Computational methods for predicting evolutionarily conserved rather than thermodynamic RNA structures have recently attracted increased interest. These methods are indispensable not only for elucidating the regulatory roles of known RNA transcripts, but also for predicting RNA genes. It has been notoriously difficult to devise them to make the best use of the available data and to predict high-quality RNA structures that may also contain pseudoknots. We introduce a novel theoretical framework for co-estimating an RNA secondary structure including pseudoknots, a multiple sequence alignment, and an evolutionary tree, given several RNA input sequences. We also present an implementation of the framework in a new computer program, called SimulFold, which employs a Bayesian Markov chain Monte Carlo method to sample from the joint posterior distribution of RNA structures, alignments, and trees. We use the new framework to predict RNA structures, and comprehensively evaluate the quality of our predictions by comparing our results to those of several other programs. We also present preliminary data that show SimulFold's potential as an alignment and phylogeny prediction method. SimulFold overcomes many conceptual limitations that current RNA structure prediction methods face, introduces several new theoretical techniques, and generates high-quality predictions of conserved RNA structures that may include pseudoknots. It is thus likely to have a strong impact, both on the field of RNA structure prediction and on a wide range of data analyses.
Keywords:Algorithms, Artificial Intelligence, Automated Pattern Recognition, Base Sequence, Bayes Theorem, Chemical Models, Computer Simulation, Conserved Sequence, Genetic Models , Molecular Evolution, Molecular Models, Molecular Sequence Data, Monte Carlo Method, Sequence Alignment, RNA, RNA Sequence Analysis, Software, Structure-Activity Relationship
Source:PLoS Computational Biology
Publisher:Public Library of Science (U.S.A.)
Page Range:e149
Date:10 August 2007
Official Publication:https://doi.org/10.1371/journal.pcbi.0030149
PubMed:View item in PubMed

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