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This article is part of the supplement: Selected articles from the Eleventh Asia Pacific Bioinformatics Conference (APBC 2013): Bioinformatics

Open Access Proceedings

Characterising RNA secondary structure space using information entropy

Zsuzsanna Sükösd123*, Bjarne Knudsen4, James WJ Anderson5, Ádám Novák56, Jørgen Kjems23 and Christian NS Pedersen17

Author Affiliations

1 Bioinformatics Research Center, Aarhus University, Aarhus, Denmark

2 Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark

3 Interdisciplinary Nanoscience Center, Aarhus University, Aarhus, Denmark

4 CLC bio, Finlandsgade 10-12, Aarhus N, DK-8000, Denmark

5 Department of Statistics, University of Oxford, OX1 3TG, UK

6 Oxford Centre for Integrative Systems Biology, University of Oxford, OX1 3QU, UK

7 Department of Computer Science, Aarhus University, Aarhus, Denmark

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BMC Bioinformatics 2013, 14(Suppl 2):S22  doi:10.1186/1471-2105-14-S2-S22

Published: 21 January 2013


Comparative methods for RNA secondary structure prediction use evolutionary information from RNA alignments to increase prediction accuracy. The model is often described in terms of stochastic context-free grammars (SCFGs), which generate a probability distribution over secondary structures. It is, however, unclear how this probability distribution changes as a function of the input alignment. As prediction programs typically only return a single secondary structure, better characterisation of the underlying probability space of RNA secondary structures is of great interest. In this work, we show how to efficiently compute the information entropy of the probability distribution over RNA secondary structures produced for RNA alignments by a phylo-SCFG, and implement it for the PPfold model. We also discuss interpretations and applications of this quantity, including how it can clarify reasons for low prediction reliability scores. PPfold and its source code are available from webcite.