Characterising RNA secondary structure space using information entropy
| Autor: | James W. J. Anderson, Bjarne Knudsen, Zsuzsanna Sükösd, Christian N. S. Pedersen, Jørgen Kjems, Adam M. Novak |
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| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Theoretical computer science
Source code Computer science media_common.quotation_subject Entropy Biochemistry Nucleic acid secondary structure Entropy (classical thermodynamics) Software Rule-based machine translation Structural Biology Entropy (information theory) Entropy (energy dispersal) Molecular Biology Protein secondary structure Entropy (arrow of time) media_common Probability Base Sequence Entropy (statistical thermodynamics) business.industry Applied Mathematics RNA Computational Biology Models Theoretical Computer Science Applications Proceedings Probability distribution Nucleic Acid Conformation DNA microarray business Algorithm Algorithms Entropy (order and disorder) |
| Zdroj: | Sükösd, Z, Knudsen, B, Anderson, J WJ, Novák, A, Kjems, J & Pedersen, C N S 2013, ' Characterising RNA secondary structure space using information entropy ', B M C Bioinformatics, vol. 14, no. Suppl 2, S22, pp. 1-9 . https://doi.org/10.1186/1471-2105-14-S2-S22 BMC Bioinformatics Scopus-Elsevier |
| Popis: | 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 http://birc.au.dk/software/ppfold/. |
| Databáze: | OpenAIRE |
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