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Energy-Based RNA Consensus Secondary Structure Prediction in Multiple Sequence Alignments

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RNA Sequence, Structure, and Function: Computational and Bioinformatic Methods

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1097))

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Abstract

Many biologically important RNA structures are conserved in evolution leading to characteristic mutational patterns. RNAalifold is a widely used program to predict consensus secondary structures in multiple alignments by combining evolutionary information with traditional energy-based RNA folding algorithms. Here we describe the theory and applications of the RNAalifold algorithm. Consensus secondary structure prediction not only leads to significantly more accurate structure models, but it also allows to study structural conservation of functional RNAs.

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Acknowledgements

Stefan Washietl was supported by an Erwin Schrödinger Fellowship of the Austrian Fonds zur Förderung der Wissenschaftlichen Forschung. Stephan H. Bernhart was funded by the Austrian GEN-AU project “Noncoding RNA.” We thank Ivo Hofacker and Benjamin Holmes for comments on the manuscript.

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Authors and Affiliations

  1. Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

    Stefan Washietl & Manolis Kellis

  2. Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany

    Stephan H. Bernhart

Authors
  1. Stefan Washietl
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  2. Stephan H. Bernhart
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  3. Manolis Kellis
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Editor information

Editors and Affiliations

  1. Center for non-coding RNA in Technology and Health, IKVH University of Copenhagen, Frederiksberg, Denmark

    Jan Gorodkin

  2. University of Washington Dept. Computer Science & Engineering, Seattle, Washington, USA

    Walter L. Ruzzo

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Washietl, S., Bernhart, S.H., Kellis, M. (2014). Energy-Based RNA Consensus Secondary Structure Prediction in Multiple Sequence Alignments. In: Gorodkin, J., Ruzzo, W. (eds) RNA Sequence, Structure, and Function: Computational and Bioinformatic Methods. Methods in Molecular Biology, vol 1097. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-709-9_7

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  • DOI: https://doi.org/10.1007/978-1-62703-709-9_7

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-708-2

  • Online ISBN: 978-1-62703-709-9

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