title: An efficient parallel algorithm for multiple sequence similarities calculation using a low complexity method.
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.
authors.affiliation: Department of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.
authors.forename: Evandro A
authors.initials: EA
authors.lastname: Marucci
authors.affiliation: Department of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.
authors.forename: Geraldo F D
authors.identifier: 0000-0003-2384-011X
authors.initials: GF
authors.lastname: Zafalon
authors.affiliation: Department of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.
authors.forename: Julio C
authors.identifier: 0000-0003-3355-0343
authors.initials: JC
authors.lastname: Momente
authors.affiliation: Department of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.
authors.forename: Leandro A
authors.initials: LA
authors.lastname: Neves
authors.affiliation: Department of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.
authors.forename: Carlo R
authors.initials: CR
authors.lastname: Valêncio
authors.affiliation: Department of Control Engineering and Automation, Federal University of Santa Catarina, Rua Pomerode 710, 89065-300 Blumenau, SC, Brazil.
authors.forename: Alex R
authors.initials: AR
authors.lastname: Pinto
authors.affiliation: Department of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.
authors.forename: Adriano M
authors.initials: AM
authors.lastname: Cansian
authors.affiliation: Department of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.
authors.forename: Rogeria C G
authors.initials: RC
authors.lastname: de Souza
authors.affiliation: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Shiyou
authors.affiliation: Department of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.
authors.forename: José M
authors.initials: JM
authors.lastname: Machado
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D016678:Genome; D016415:Sequence Alignment; D017385:Sequence Homology; D012984:Software