Simulfold: Simultaneously inferring RNA structures including pseudoknots, alignments, and trees using a Bayesian MCMC framework

Irmtraud M. Meyer, I. Miklós

Research output: Contribution to journalArticle

48 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1441-1454
Number of pages14
JournalPLoS Computational Biology
Volume3
Issue number8
DOIs
Publication statusPublished - Aug 2007

Fingerprint

Markov Chain Monte Carlo
RNA
Alignment
Structure Prediction
Prediction
prediction
Evolutionary Tree
Predict
RNA Secondary Structure
Multiple Sequence Alignment
Phylogeny
Markov Chain Monte Carlo Methods
Posterior distribution
Joint Distribution
Computational Methods
Thermodynamics
Likely
Psychological Techniques
Framework
alignment

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience
  • Ecology
  • Molecular Biology
  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Modelling and Simulation
  • Computational Theory and Mathematics

Cite this

Simulfold : Simultaneously inferring RNA structures including pseudoknots, alignments, and trees using a Bayesian MCMC framework. / Meyer, Irmtraud M.; Miklós, I.

In: PLoS Computational Biology, Vol. 3, No. 8, 08.2007, p. 1441-1454.

Research output: Contribution to journalArticle

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