Optimization of SNP Search Based on Masks Using Graphics Processing Unit

• Autor(a) principal: da Cruz, Álvaro Magri Nogueira [UNESP]
• Data de Publicação: 2023
• Outros Autores: Gomes, Vitoria Zanon [UNESP], Andrade, Matheus Carreira [UNESP], Rici Amorim, Anderson [UNESP], Valêncio, Carlos Roberto [UNESP], Vaughan, Gilberto, Donegá Zafalon, Geraldo Francisco [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.5220/0011841500003467; http://hdl.handle.net/11449/250025
• topic: Bioinformatics; Graphics Processing Unit; Next Generation Sequencing; Parallel Processing; Single Nucleotide Polymorphism

In the context of bioinformatics one of the most important problems to be solved is the search for simple nucleotide polymorphism (SNP). When we perform the analysis of the files from the next generation sequencing (NGS) the search task for SNPs becomes more prohibitive due to the millions of sequences present on them. CPU multithreaded approaches are not enough when millions of sequences as considered. Then, the use of graphics processing units (GPUs) is a better alternative, because it can operate with hundreds of arithmetic logic units while CPU with no more than tens. Thus, in this work we developed a method to detect SNPs using a mask approach under GPU architecture. In the tests, a speedup of up to 5175.86 was obtained when compared to the multithreaded CPU approach, evaluating from 100,000 to 800,000 sequences using five masks to detect the occurrence of SNPs.