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BioMed Research International
Volume 2014, Article ID 563016, 6 pages
http://dx.doi.org/10.1155/2014/563016
Research Article

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

1Department of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil
2Department of Control Engineering and Automation, Federal University of Santa Catarina, Rua Pomerode 710, 89065-300 Blumenau, SC, Brazil
3College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Received 12 February 2014; Accepted 2 July 2014; Published 22 July 2014

Academic Editor: Tzong-Yi Lee

Copyright © 2014 Evandro A. Marucci et al. 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.

Abstract

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.