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Open Access Software

Sequence similarity is more relevant than species specificity in probabilistic backtranslation

Alfredo Ferro12*, Rosalba Giugno1, Giuseppe Pigola1, Alfredo Pulvirenti1, Cinzia Di Pietro2, Michele Purrello2 and Marco Ragusa2

Author Affiliations

1 Dipartimento di Matematica e Informatica, Università di Catania, Viale A. Doria 6, I-95125 Catania, Italy

2 Dipartimento di Scienze Biomediche, Università di Catania, Via S. Sofia 87, I-95125 Catania, Italy

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BMC Bioinformatics 2007, 8:58  doi:10.1186/1471-2105-8-58

Published: 21 February 2007

Abstract

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.