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Predicting Consensus Structures for RNA Alignments Via Pseudo-Energy Minimization

Thermodynamic processes with free energy parameters are often used in algorithms that solve the free energy minimization problem to predict secondary structures of single RNA sequences. While results from these algorithms are promising, an observation is that single sequence-based methods have moder...

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Detalles Bibliográficos
Autores principales: Spirollari, Junilda, Wang, Jason T.L., Zhang, Kaizhong, Bellofatto, Vivian, Park, Yongkyu, Shapiro, Bruce A.
Formato: Texto
Lenguaje:English
Publicado: Libertas Academica 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2808183/
https://www.ncbi.nlm.nih.gov/pubmed/20140072
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author Spirollari, Junilda
Wang, Jason T.L.
Zhang, Kaizhong
Bellofatto, Vivian
Park, Yongkyu
Shapiro, Bruce A.
author_facet Spirollari, Junilda
Wang, Jason T.L.
Zhang, Kaizhong
Bellofatto, Vivian
Park, Yongkyu
Shapiro, Bruce A.
author_sort Spirollari, Junilda
collection PubMed
description Thermodynamic processes with free energy parameters are often used in algorithms that solve the free energy minimization problem to predict secondary structures of single RNA sequences. While results from these algorithms are promising, an observation is that single sequence-based methods have moderate accuracy and more information is needed to improve on RNA secondary structure prediction, such as covariance scores obtained from multiple sequence alignments. We present in this paper a new approach to predicting the consensus secondary structure of a set of aligned RNA sequences via pseudo-energy minimization. Our tool, called RSpredict, takes into account sequence covariation and employs effective heuristics for accuracy improvement. RSpredict accepts, as input data, a multiple sequence alignment in FASTA or ClustalW format and outputs the consensus secondary structure of the input sequences in both the Vienna style Dot Bracket format and the Connectivity Table format. Our method was compared with some widely used tools including KNetFold, Pfold and RNAalifold. A comprehensive test on different datasets including Rfam sequence alignments and a multiple sequence alignment obtained from our study on the Drosophila X chromosome reveals that RSpredict is competitive with the existing tools on the tested datasets. RSpredict is freely available online as a web server and also as a jar file for download at http://datalab.njit.edu/biology/RSpredict.
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spelling pubmed-28081832010-02-04 Predicting Consensus Structures for RNA Alignments Via Pseudo-Energy Minimization Spirollari, Junilda Wang, Jason T.L. Zhang, Kaizhong Bellofatto, Vivian Park, Yongkyu Shapiro, Bruce A. Bioinform Biol Insights Original Research Thermodynamic processes with free energy parameters are often used in algorithms that solve the free energy minimization problem to predict secondary structures of single RNA sequences. While results from these algorithms are promising, an observation is that single sequence-based methods have moderate accuracy and more information is needed to improve on RNA secondary structure prediction, such as covariance scores obtained from multiple sequence alignments. We present in this paper a new approach to predicting the consensus secondary structure of a set of aligned RNA sequences via pseudo-energy minimization. Our tool, called RSpredict, takes into account sequence covariation and employs effective heuristics for accuracy improvement. RSpredict accepts, as input data, a multiple sequence alignment in FASTA or ClustalW format and outputs the consensus secondary structure of the input sequences in both the Vienna style Dot Bracket format and the Connectivity Table format. Our method was compared with some widely used tools including KNetFold, Pfold and RNAalifold. A comprehensive test on different datasets including Rfam sequence alignments and a multiple sequence alignment obtained from our study on the Drosophila X chromosome reveals that RSpredict is competitive with the existing tools on the tested datasets. RSpredict is freely available online as a web server and also as a jar file for download at http://datalab.njit.edu/biology/RSpredict. Libertas Academica 2009-06-03 /pmc/articles/PMC2808183/ /pubmed/20140072 Text en Copyright © 2009 The authors. https://creativecommons.org/licenses/by/2.0/This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/2.0/ (https://creativecommons.org/licenses/by/2.0/) ).
spellingShingle Original Research
Spirollari, Junilda
Wang, Jason T.L.
Zhang, Kaizhong
Bellofatto, Vivian
Park, Yongkyu
Shapiro, Bruce A.
Predicting Consensus Structures for RNA Alignments Via Pseudo-Energy Minimization
title Predicting Consensus Structures for RNA Alignments Via Pseudo-Energy Minimization
title_full Predicting Consensus Structures for RNA Alignments Via Pseudo-Energy Minimization
title_fullStr Predicting Consensus Structures for RNA Alignments Via Pseudo-Energy Minimization
title_full_unstemmed Predicting Consensus Structures for RNA Alignments Via Pseudo-Energy Minimization
title_short Predicting Consensus Structures for RNA Alignments Via Pseudo-Energy Minimization
title_sort predicting consensus structures for rna alignments via pseudo-energy minimization
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2808183/
https://www.ncbi.nlm.nih.gov/pubmed/20140072
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