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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Detalles Bibliográficos
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
Descripción
Sumario: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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