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Sequence similarity is more relevant than species specificity in probabilistic backtranslation

BACKGROUND: Backtranslation is the process of decoding a sequence of amino acids into the corresponding codons. All synthetic gene design systems include a backtranslation module. The degeneracy of the genetic code makes backtranslation potentially ambiguous since most amino acids are encoded by mul...

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Autores principales: Ferro, Alfredo, Giugno, Rosalba, Pigola, Giuseppe, Pulvirenti, Alfredo, Di Pietro, Cinzia, Purrello, Michele, Ragusa, Marco
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1810562/
https://www.ncbi.nlm.nih.gov/pubmed/17313665
http://dx.doi.org/10.1186/1471-2105-8-58
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author Ferro, Alfredo
Giugno, Rosalba
Pigola, Giuseppe
Pulvirenti, Alfredo
Di Pietro, Cinzia
Purrello, Michele
Ragusa, Marco
author_facet Ferro, Alfredo
Giugno, Rosalba
Pigola, Giuseppe
Pulvirenti, Alfredo
Di Pietro, Cinzia
Purrello, Michele
Ragusa, Marco
author_sort Ferro, Alfredo
collection PubMed
description BACKGROUND: Backtranslation is the process of decoding a sequence of amino acids into the corresponding codons. All synthetic gene design systems include a backtranslation module. The degeneracy of the genetic code makes backtranslation potentially ambiguous since most amino acids are encoded by multiple codons. The common approach to overcome this difficulty is based on imitation of codon usage within the target species. RESULTS: This paper describes EasyBack, a new parameter-free, fully-automated software for backtranslation using Hidden Markov Models. EasyBack is not based on imitation of codon usage within the target species, but instead uses a sequence-similarity criterion. The model is trained with a set of proteins with known cDNA coding sequences, constructed from the input protein by querying the NCBI databases with BLAST. Unlike existing software, the proposed method allows the quality of prediction to be estimated. When tested on a group of proteins that show different degrees of sequence conservation, EasyBack outperforms other published methods in terms of precision. CONCLUSION: The prediction quality of a protein backtranslation methis markedly increased by replacing the criterion of most used codon in the same species with a Hidden Markov Model trained with a set of most similar sequences from all species. Moreover, the proposed method allows the quality of prediction to be estimated probabilistically.
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spelling pubmed-18105622007-03-13 Sequence similarity is more relevant than species specificity in probabilistic backtranslation Ferro, Alfredo Giugno, Rosalba Pigola, Giuseppe Pulvirenti, Alfredo Di Pietro, Cinzia Purrello, Michele Ragusa, Marco BMC Bioinformatics Software BACKGROUND: Backtranslation is the process of decoding a sequence of amino acids into the corresponding codons. All synthetic gene design systems include a backtranslation module. The degeneracy of the genetic code makes backtranslation potentially ambiguous since most amino acids are encoded by multiple codons. The common approach to overcome this difficulty is based on imitation of codon usage within the target species. RESULTS: This paper describes EasyBack, a new parameter-free, fully-automated software for backtranslation using Hidden Markov Models. EasyBack is not based on imitation of codon usage within the target species, but instead uses a sequence-similarity criterion. The model is trained with a set of proteins with known cDNA coding sequences, constructed from the input protein by querying the NCBI databases with BLAST. Unlike existing software, the proposed method allows the quality of prediction to be estimated. When tested on a group of proteins that show different degrees of sequence conservation, EasyBack outperforms other published methods in terms of precision. CONCLUSION: The prediction quality of a protein backtranslation methis markedly increased by replacing the criterion of most used codon in the same species with a Hidden Markov Model trained with a set of most similar sequences from all species. Moreover, the proposed method allows the quality of prediction to be estimated probabilistically. BioMed Central 2007-02-21 /pmc/articles/PMC1810562/ /pubmed/17313665 http://dx.doi.org/10.1186/1471-2105-8-58 Text en Copyright © 2007 Ferro et al; 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 Software
Ferro, Alfredo
Giugno, Rosalba
Pigola, Giuseppe
Pulvirenti, Alfredo
Di Pietro, Cinzia
Purrello, Michele
Ragusa, Marco
Sequence similarity is more relevant than species specificity in probabilistic backtranslation
title Sequence similarity is more relevant than species specificity in probabilistic backtranslation
title_full Sequence similarity is more relevant than species specificity in probabilistic backtranslation
title_fullStr Sequence similarity is more relevant than species specificity in probabilistic backtranslation
title_full_unstemmed Sequence similarity is more relevant than species specificity in probabilistic backtranslation
title_short Sequence similarity is more relevant than species specificity in probabilistic backtranslation
title_sort sequence similarity is more relevant than species specificity in probabilistic backtranslation
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1810562/
https://www.ncbi.nlm.nih.gov/pubmed/17313665
http://dx.doi.org/10.1186/1471-2105-8-58
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