Alinhamento Global de Várias Sequências Biológicas utilizando Cluster de GPUs

• Ano de defesa: 2021
• Autor(a) principal: RODRIGO ALBUQUERQUE DE OLIVEIRA SIQUEIRA
• Orientador(a): Marco Aurelio Stefanes
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Fundação Universidade Federal de Mato Grosso do Sul
• País: Brasil
• Palavras-chave em Português: Alinhamento de Várias Sequências , Algoritmos Paralelos Híbridos, Algoritmos Multi-GPU, Alinhamento de Sequências Grandes.
• Link de acesso: https://repositorio.ufms.br/handle/123456789/3807

A multiple sequence alignment is an important tool for studying and representing similarities between a set of biological sequences – such as DNAs, RNAs and proteins. This study allows relevant information to be obtained from these sequences, i.e. their functional and evolutionary relations as well as their internal structures. Due to its importance, several methods have been proposed as a solution to this problem. Nonetheless, the problem’s inherent complexity, which is described as computationally NP-Hard, leads to prohibitive execution times in scenarios with large numbers of lengthy sequences. In this work, we present a complete implementation of the Progressive Alignment heuristic method, using hybrid parallelism for environments with multiple GPU devices. This approach allows the construction of global alignments between datasets of numerous lengthy sequences in reasonable time. Our implementation achieves expressive results, showing speedups of up to 380 when compared to the parallel ClustalW-MPI aligner for datasets obtained from NCBI’s sequence databases.