Characterising RNA secondary structure space using information entropy

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  • Zsuzsanna Sükösd
  • ,
  • Bjarne Knudsen, CLC bio, Aarhus, Denmark
  • James WJ Anderson, Department of Statistics, University of Oxford, United Kingdom
  • Adám Novák, Department of Statistics and Oxford Centre for Integrative Systems Biology, University of Oxford, United Kingdom
  • Jørgen Kjems
  • Christian Nørgaard Storm Pedersen
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
Original languageEnglish
Article numberS22
JournalB M C Bioinformatics
IssueSuppl 2
Pages (from-to)1-9
Number of pages9
Publication statusPublished - 21 Jan 2013
EventThe Eleventh Asia Pacific Bioinformatic Conference - Vancouver, Canada
Duration: 21 Jan 201324 Jan 2013
Conference number: 11th


ConferenceThe Eleventh Asia Pacific Bioinformatic Conference

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