SparkBLAST : utilização da ferramenta Apache Spark para a execução do BLAST em ambiente distribuído e escalável

• Ano de defesa: 2017
• Autor(a) principal: Castro, Marcelo Rodrigo de
• Orientador(a): Senger, Hermes
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• Palavras-chave em Português: BLAST; Apache Spark; Nuvens computacionais; Sequenciamento genético
• Palavras-chave em Inglês: Cloud computing; Genetic sequencing; Hadoop
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/ufscar/9114

With the evolution of next generation sequencing devices, the cost for obtaining genomic data has significantly reduced. With reduced costs for sequencing, the amount of genomic data to be processed has increased exponentially. Such data growth supersedes the rate at which computing power can be increased year after year by the hardware and software evolution. Thus, the higher rate of data growth in bioinformatics raises the need for exploiting more efficient and scalable techniques based on parallel and distributed processing, including platforms like Clusters, and Cloud Computing. BLAST is a widely used tool for genomic sequences alignment, which has native support for multicore-based parallel processing. However, its scalability is limited to a single machine. On the other hand, Cloud computing has emerged as an important technology for supporting rapid and elastic provisioning of large amounts of resources. Current frameworks like Apache Hadoop and Apache Spark provide support for the execution of distributed applications. Such environments provide mechanisms for embedding external applications in order to compose large distributed jobs which can be executed on clusters and cloud platforms. In this work, we used Spark to support the high scalable and efficient parallelization of BLAST (Basic Local Alingment Search Tool) to execute on dozens to hundreds of processing cores on a cloud platform. As result, our prototype has demonstrated better performance and scalability then CloudBLAST, a Hadoop based parallelization of BLAST.