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Dinucleotide controlled null models for comparative RNA gene prediction

BACKGROUND: Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those program...

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Detalles Bibliográficos
Autores principales: Gesell, Tanja, Washietl, Stefan
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2453142/
https://www.ncbi.nlm.nih.gov/pubmed/18505553
http://dx.doi.org/10.1186/1471-2105-9-248
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author Gesell, Tanja
Washietl, Stefan
author_facet Gesell, Tanja
Washietl, Stefan
author_sort Gesell, Tanja
collection PubMed
description BACKGROUND: Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. RESULTS: We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. CONCLUSION: SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. AVAILABILITY: SISSIz is available as open source C code that can be compiled for every major platform and downloaded here: .
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spelling pubmed-24531422008-07-11 Dinucleotide controlled null models for comparative RNA gene prediction Gesell, Tanja Washietl, Stefan BMC Bioinformatics Methodology Article BACKGROUND: Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. RESULTS: We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. CONCLUSION: SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. AVAILABILITY: SISSIz is available as open source C code that can be compiled for every major platform and downloaded here: . BioMed Central 2008-05-27 /pmc/articles/PMC2453142/ /pubmed/18505553 http://dx.doi.org/10.1186/1471-2105-9-248 Text en Copyright © 2008 Gesell and Washietl; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Gesell, Tanja
Washietl, Stefan
Dinucleotide controlled null models for comparative RNA gene prediction
title Dinucleotide controlled null models for comparative RNA gene prediction
title_full Dinucleotide controlled null models for comparative RNA gene prediction
title_fullStr Dinucleotide controlled null models for comparative RNA gene prediction
title_full_unstemmed Dinucleotide controlled null models for comparative RNA gene prediction
title_short Dinucleotide controlled null models for comparative RNA gene prediction
title_sort dinucleotide controlled null models for comparative rna gene prediction
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2453142/
https://www.ncbi.nlm.nih.gov/pubmed/18505553
http://dx.doi.org/10.1186/1471-2105-9-248
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