Characterising RNA secondary structure space using information entropy.
BMC Bioinformatics
; 14 Suppl 2: S22, 2013.
Article
en En
| MEDLINE
| ID: mdl-23368905
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/.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
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ARN
/
Modelos Teóricos
/
Conformación de Ácido Nucleico
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2013
Tipo del documento:
Article
País de afiliación:
Dinamarca
Pais de publicación:
Reino Unido