Large-scale parallel genome assembler over cloud computing environment
| Autor: | Seung-Jong Park, Richard Platania, Sayan Goswami, Praveen Kumar Koppa, A. Das |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Power graph analysis Male Staphylococcus aureus Computer science Distributed computing 0206 medical engineering Cloud computing 02 engineering and technology Parallel computing Terabyte Biochemistry De Bruijn graph Giga 03 medical and health sciences symbols.namesake Databases Genetic Escherichia coli Humans Molecular Biology Genome business.industry Genome Human Volume (computing) High-Throughput Nucleotide Sequencing Cloud Computing Computer Science Applications 030104 developmental biology Scalability symbols Programming paradigm business 020602 bioinformatics Software |
| Zdroj: | Journal of bioinformatics and computational biology. 15(3) |
| ISSN: | 1757-6334 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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