SparkBLAST: scalable BLAST processing using in-memory operations
Abstract Background The demand for processing ever increasing amounts of genomic data has raised new challenges for the implementation of highly scalable and efficient computational systems. In this paper we propose SparkBLAST, a parallelization of a sequence alignment application (BLAST) that emplo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2017-06-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-017-1723-8 |
| _version_ | 1818992335664447488 |
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| author | Marcelo Rodrigo de Castro Catherine dos Santos Tostes Alberto M. R. Dávila Hermes Senger Fabricio A. B. da Silva |
| author_facet | Marcelo Rodrigo de Castro Catherine dos Santos Tostes Alberto M. R. Dávila Hermes Senger Fabricio A. B. da Silva |
| author_sort | Marcelo Rodrigo de Castro |
| collection | DOAJ |
| description | Abstract Background The demand for processing ever increasing amounts of genomic data has raised new challenges for the implementation of highly scalable and efficient computational systems. In this paper we propose SparkBLAST, a parallelization of a sequence alignment application (BLAST) that employs cloud computing for the provisioning of computational resources and Apache Spark as the coordination framework. As a proof of concept, some radionuclide-resistant bacterial genomes were selected for similarity analysis. Results Experiments in Google and Microsoft Azure clouds demonstrated that SparkBLAST outperforms an equivalent system implemented on Hadoop in terms of speedup and execution times. Conclusions The superior performance of SparkBLAST is mainly due to the in-memory operations available through the Spark framework, consequently reducing the number of local I/O operations required for distributed BLAST processing. |
| first_indexed | 2024-12-20T20:24:32Z |
| format | Article |
| id | doaj.art-f1269771a22f492f805eee4ced80bdd3 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-20T20:24:32Z |
| publishDate | 2017-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-f1269771a22f492f805eee4ced80bdd32022-12-21T19:27:29ZengBMCBMC Bioinformatics1471-21052017-06-0118111310.1186/s12859-017-1723-8SparkBLAST: scalable BLAST processing using in-memory operationsMarcelo Rodrigo de Castro0Catherine dos Santos Tostes1Alberto M. R. Dávila2Hermes Senger3Fabricio A. B. da Silva4Computer Science Department, Federal University of São CarlosLBCS-IOC, Oswaldo Cruz FoundationLBCS-IOC, Oswaldo Cruz FoundationComputer Science Department, Federal University of São CarlosPROCC, Oswaldo Cruz FoundationAbstract Background The demand for processing ever increasing amounts of genomic data has raised new challenges for the implementation of highly scalable and efficient computational systems. In this paper we propose SparkBLAST, a parallelization of a sequence alignment application (BLAST) that employs cloud computing for the provisioning of computational resources and Apache Spark as the coordination framework. As a proof of concept, some radionuclide-resistant bacterial genomes were selected for similarity analysis. Results Experiments in Google and Microsoft Azure clouds demonstrated that SparkBLAST outperforms an equivalent system implemented on Hadoop in terms of speedup and execution times. Conclusions The superior performance of SparkBLAST is mainly due to the in-memory operations available through the Spark framework, consequently reducing the number of local I/O operations required for distributed BLAST processing.http://link.springer.com/article/10.1186/s12859-017-1723-8Cloud computingComparative genomicsScalabilitySpark |
| spellingShingle | Marcelo Rodrigo de Castro Catherine dos Santos Tostes Alberto M. R. Dávila Hermes Senger Fabricio A. B. da Silva SparkBLAST: scalable BLAST processing using in-memory operations BMC Bioinformatics Cloud computing Comparative genomics Scalability Spark |
| title | SparkBLAST: scalable BLAST processing using in-memory operations |
| title_full | SparkBLAST: scalable BLAST processing using in-memory operations |
| title_fullStr | SparkBLAST: scalable BLAST processing using in-memory operations |
| title_full_unstemmed | SparkBLAST: scalable BLAST processing using in-memory operations |
| title_short | SparkBLAST: scalable BLAST processing using in-memory operations |
| title_sort | sparkblast scalable blast processing using in memory operations |
| topic | Cloud computing Comparative genomics Scalability Spark |
| url | http://link.springer.com/article/10.1186/s12859-017-1723-8 |
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