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Target prediction and a statistical sampling algorithm for RNA–RNA interaction

Authors: Fenix W. D. Huang; Jing Qin 0006; Christian M. Reidys; Peter F. Stadler;

Target prediction and a statistical sampling algorithm for RNA–RNA interaction

Abstract

Abstract Motivation: It has been proven that the accessibility of the target sites has a critical influence on RNA–RNA binding, in general and the specificity and efficiency of miRNAs and siRNAs, in particular. Recently, O(N6) time and O(N4) space dynamic programming (DP) algorithms have become available that compute the partition function of RNA–RNA interaction complexes, thereby providing detailed insights into their thermodynamic properties. Results: Modifications to the grammars underlying earlier approaches enables the calculation of interaction probabilities for any given interval on the target RNA. The computation of the ‘hybrid probabilities’ is complemented by a stochastic sampling algorithm that produces a Boltzmann weighted ensemble of RNA–RNA interaction structures. The sampling of k structures requires only negligible additional memory resources and runs in O(k·N3). Availability: The algorithms described here are implemented in C as part of the rip package. The source code of rip2 can be downloaded from http://www.combinatorics.cn/cbpc/rip.html and http://www.bioinf.uni-leipzig.de/Software/rip.html. Contact: duck@santafe.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Country
Austria
Keywords

Models, Molecular, Mathematics - Statistics Theory, Statistics Theory (math.ST), Quantitative Biology - Quantitative Methods, 104001 Allgemeine Chemie, Databases, Genetic, FOS: Mathematics, Mathematics - Combinatorics, RNA, Small Interfering, Quantitative Methods (q-bio.QM), Binding Sites, Models, Statistical, Computational Biology, Biomolecules (q-bio.BM), Original Papers, MicroRNAs, Quantitative Biology - Biomolecules, 05A16, FOS: Biological sciences, Nucleic Acid Conformation, RNA, Combinatorics (math.CO), Algorithms, Software, 104001 General chemistry

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
39
Top 10%
Top 10%
Top 10%
Green
gold