An Efficient Parallel Algorithm for Multiple Sequence Similarities Calculation Using a Low Complexity Method

With the advance of genomic researches, the number of sequences involved in comparative methods has grown immensely. Among them, there are methods for similarities calculation, which are used by many bioinformatics applications. Due the huge amount of data, the union of low complexity methods with t...

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Autores principales: Marucci, Evandro A., Zafalon, Geraldo F. D., Momente, Julio C., Neves, Leandro A., Valêncio, Carlo R., Pinto, Alex R., Cansian, Adriano M., de Souza, Rogeria C. G., Shiyou, Yang, Machado, José M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4130029/
https://www.ncbi.nlm.nih.gov/pubmed/25140318
http://dx.doi.org/10.1155/2014/563016
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author Marucci, Evandro A.
Zafalon, Geraldo F. D.
Momente, Julio C.
Neves, Leandro A.
Valêncio, Carlo R.
Pinto, Alex R.
Cansian, Adriano M.
de Souza, Rogeria C. G.
Shiyou, Yang
Machado, José M.
author_facet Marucci, Evandro A.
Zafalon, Geraldo F. D.
Momente, Julio C.
Neves, Leandro A.
Valêncio, Carlo R.
Pinto, Alex R.
Cansian, Adriano M.
de Souza, Rogeria C. G.
Shiyou, Yang
Machado, José M.
author_sort Marucci, Evandro A.
collection PubMed
description With the advance of genomic researches, the number of sequences involved in comparative methods has grown immensely. Among them, there are methods for similarities calculation, which are used by many bioinformatics applications. Due the huge amount of data, the union of low complexity methods with the use of parallel computing is becoming desirable. The k-mers counting is a very efficient method with good biological results. In this work, the development of a parallel algorithm for multiple sequence similarities calculation using the k-mers counting method is proposed. Tests show that the algorithm presents a very good scalability and a nearly linear speedup. For 14 nodes was obtained 12x speedup. This algorithm can be used in the parallelization of some multiple sequence alignment tools, such as MAFFT and MUSCLE.
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spelling pubmed-41300292014-08-19 An Efficient Parallel Algorithm for Multiple Sequence Similarities Calculation Using a Low Complexity Method Marucci, Evandro A. Zafalon, Geraldo F. D. Momente, Julio C. Neves, Leandro A. Valêncio, Carlo R. Pinto, Alex R. Cansian, Adriano M. de Souza, Rogeria C. G. Shiyou, Yang Machado, José M. Biomed Res Int Research Article With the advance of genomic researches, the number of sequences involved in comparative methods has grown immensely. Among them, there are methods for similarities calculation, which are used by many bioinformatics applications. Due the huge amount of data, the union of low complexity methods with the use of parallel computing is becoming desirable. The k-mers counting is a very efficient method with good biological results. In this work, the development of a parallel algorithm for multiple sequence similarities calculation using the k-mers counting method is proposed. Tests show that the algorithm presents a very good scalability and a nearly linear speedup. For 14 nodes was obtained 12x speedup. This algorithm can be used in the parallelization of some multiple sequence alignment tools, such as MAFFT and MUSCLE. Hindawi Publishing Corporation 2014 2014-07-22 /pmc/articles/PMC4130029/ /pubmed/25140318 http://dx.doi.org/10.1155/2014/563016 Text en Copyright © 2014 Evandro A. Marucci et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Marucci, Evandro A.
Zafalon, Geraldo F. D.
Momente, Julio C.
Neves, Leandro A.
Valêncio, Carlo R.
Pinto, Alex R.
Cansian, Adriano M.
de Souza, Rogeria C. G.
Shiyou, Yang
Machado, José M.
An Efficient Parallel Algorithm for Multiple Sequence Similarities Calculation Using a Low Complexity Method
title An Efficient Parallel Algorithm for Multiple Sequence Similarities Calculation Using a Low Complexity Method
title_full An Efficient Parallel Algorithm for Multiple Sequence Similarities Calculation Using a Low Complexity Method
title_fullStr An Efficient Parallel Algorithm for Multiple Sequence Similarities Calculation Using a Low Complexity Method
title_full_unstemmed An Efficient Parallel Algorithm for Multiple Sequence Similarities Calculation Using a Low Complexity Method
title_short An Efficient Parallel Algorithm for Multiple Sequence Similarities Calculation Using a Low Complexity Method
title_sort efficient parallel algorithm for multiple sequence similarities calculation using a low complexity method
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4130029/
https://www.ncbi.nlm.nih.gov/pubmed/25140318
http://dx.doi.org/10.1155/2014/563016
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