The development of next-generation sequencing platforms increased substantially the capacity of data generation. In addition, in the past years, the costs for whole genome sequencing have been reduced that made it easier to access this technology. As a result, the storage and analysis of the data generated became a challenge, ushering in the development of bioinformatic tools, such as programs and programming languages, able to store, process, and analyze this huge amount of information. In this article, we present MELC genomics, a framework for genome assembly in a simple and fast workflow.

No abstract

No abstract

Em bioinformática, existem vários programas disponíveis para análise de sequências de DNA. Esta é geralmente uma tarefa muito demorada, uma vez que essas sequências de DNA podem ser muito longas e complexas. O montador Velvet foi projetado para montar dados de sequenciamento de leitura curta e longa em sequências genômicas mais longas. A última versão do Velvet foi desenvolvida para funcionar com várias threads usando programação paralela com OpenMP. Aqui apresentamos uma nova versão do Velvet que explora multiprocessamento e unidades de processamento gráfico (GPU) por meio de diretivas OpenACC. Nossos testes demonstram que essa extensão do Velvet permite um desempenho mais rápido e uso de memória mais eficiente.

In bioinformatics, DNA sequence assembly refers to the reconstruction of an original DNA sequence by the alignment and merging of fragments that can be obtained from several sequencing methods. The main sequencing methods process thousands or even millions of these fragments, which can be short (hundreds of base pairs) or long (thousands of base pairs) read sequences. This is a highly computational task, which usually requires the use of parallel programs and algorithms, so that it can be performed with desirable accuracy and within suitable time limits. In this paper, we evaluate the performance of DALIGNER long read sequences aligner in a system using the Intel Xeon Phi 7210 processor. We are looking for scalable architectures that could provide a higher throughput that can be applied to future sequencing technologies.