Large-scale parallel genome assembler over cloud computing environment.
J Bioinform Comput Biol
; 15(3): 1740003, 2017 Jun.
Article
em En
| MEDLINE
| ID: mdl-28610458
ABSTRACT
The size of high throughput DNA sequencing data has already reached the terabyte scale. To manage this huge volume of data, many downstream sequencing applications started using locality-based computing over different cloud infrastructures to take advantage of elastic (pay as you go) resources at a lower cost. However, the locality-based programming model (e.g. MapReduce) is relatively new. Consequently, developing scalable data-intensive bioinformatics applications using this model and understanding the hardware environment that these applications require for good performance, both require further research. In this paper, we present a de Bruijn graph oriented Parallel Giraph-based Genome Assembler (GiGA), as well as the hardware platform required for its optimal performance. GiGA uses the power of Hadoop (MapReduce) and Giraph (large-scale graph analysis) to achieve high scalability over hundreds of compute nodes by collocating the computation and data. GiGA achieves significantly higher scalability with competitive assembly quality compared to contemporary parallel assemblers (e.g. ABySS and Contrail) over traditional HPC cluster. Moreover, we show that the performance of GiGA is significantly improved by using an SSD-based private cloud infrastructure over traditional HPC cluster. We observe that the performance of GiGA on 256 cores of this SSD-based cloud infrastructure closely matches that of 512 cores of traditional HPC cluster.
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Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
3_ND
Base de dados:
MEDLINE
Assunto principal:
Software
/
Genoma
/
Computação em Nuvem
Limite:
Humans
/
Male
Idioma:
En
Revista:
J Bioinform Comput Biol
Ano de publicação:
2017
Tipo de documento:
Article