SparkBLAST: scalable BLAST processing using in-memory operations
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 f...
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Published in | BMC bioinformatics Vol. 18; no. 1; p. 318 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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BioMed Central Ltd
27.06.2017
BioMed Central BMC |
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Abstract | 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.
Experiments in Google and Microsoft Azure clouds demonstrated that SparkBLAST outperforms an equivalent system implemented on Hadoop in terms of speedup and execution times.
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. |
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AbstractList | 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. Keywords: Cloud computing, Comparative genomics, Scalability, Spark 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. BACKGROUNDThe 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.RESULTSExperiments in Google and Microsoft Azure clouds demonstrated that SparkBLAST outperforms an equivalent system implemented on Hadoop in terms of speedup and execution times.CONCLUSIONSThe 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. 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. Experiments in Google and Microsoft Azure clouds demonstrated that SparkBLAST outperforms an equivalent system implemented on Hadoop in terms of speedup and execution times. 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. 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. Experiments in Google and Microsoft Azure clouds demonstrated that SparkBLAST outperforms an equivalent system implemented on Hadoop in terms of speedup and execution times. 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. 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. |
ArticleNumber | 318 |
Audience | Academic |
Author | Senger, Hermes de Castro, Marcelo Rodrigo Dávila, Alberto M R Tostes, Catherine Dos Santos da Silva, Fabricio A B |
Author_xml | – sequence: 1 givenname: Marcelo Rodrigo surname: de Castro fullname: de Castro, Marcelo Rodrigo organization: Computer Science Department, Federal University of São Carlos, Rod. Washington Luís, Km 235, São Carlos, 21040-900, Brazil – sequence: 2 givenname: Catherine Dos Santos surname: Tostes fullname: Tostes, Catherine Dos Santos organization: LBCS-IOC, Oswaldo Cruz Foundation, Av Brasil 4365, Rio de Janeiro, 21040-900, Brazil – sequence: 3 givenname: Alberto M R surname: Dávila fullname: Dávila, Alberto M R organization: LBCS-IOC, Oswaldo Cruz Foundation, Av Brasil 4365, Rio de Janeiro, 21040-900, Brazil – sequence: 4 givenname: Hermes surname: Senger fullname: Senger, Hermes organization: Computer Science Department, Federal University of São Carlos, Rod. Washington Luís, Km 235, São Carlos, 21040-900, Brazil – sequence: 5 givenname: Fabricio A B surname: da Silva fullname: da Silva, Fabricio A B email: fabricio.silva@fiocruz.br organization: PROCC, Oswaldo Cruz Foundation, Av. Brasil 4365, Rio de Janeiro, 21040-900, Brazil. fabricio.silva@fiocruz.br |
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Snippet | The demand for processing ever increasing amounts of genomic data has raised new challenges for the implementation of highly scalable and efficient... Background The demand for processing ever increasing amounts of genomic data has raised new challenges for the implementation of highly scalable and efficient... BACKGROUNDThe demand for processing ever increasing amounts of genomic data has raised new challenges for the implementation of highly scalable and efficient... Abstract Background The demand for processing ever increasing amounts of genomic data has raised new challenges for the implementation of highly scalable and... |
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SubjectTerms | Algorithms Alignment Bacteria Bioinformatics Cloud Computing Comparative Genomic Hybridization Comparative genomics Computer applications Data processing Databases, Factual Fault tolerance Genetic algorithms Genomes Genomics Nucleotide sequence Parallel processing Provisioning Radioisotopes Scalability Scheduling Sequence Alignment Similarity Software Spark |
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Title | SparkBLAST: scalable BLAST processing using in-memory operations |
URI | https://www.ncbi.nlm.nih.gov/pubmed/28655296 https://www.proquest.com/docview/1915060472/abstract/ https://search.proquest.com/docview/1914578508 https://pubmed.ncbi.nlm.nih.gov/PMC5488373 https://doaj.org/article/f1269771a22f492f805eee4ced80bdd3 |
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