ArticleBMC bioinformatics2017
SparkBLAST: scalable BLAST processing using in-memory operations.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- ElasticBLAST: accelerating sequence search via cloud computing.BMC bioinformatics · 2023Article
- ElasticBLAST: Accelerating Sequence Search via Cloud Computing.bioRxiv : the preprint server for biology · 2023Article
- Scalable in-memory processing of omics workflows.Computational and structural biotechnology journal · 2022Article
- BigFiRSt: A Software Program Using Big Data Technique for Mining Simple Sequence Repeats From Large-Scale Sequencing Data.Frontiers in big data · 2021Article
- Article
- Hypothetical Proteins as Predecessors of Long Non-coding RNAs.Current genomics · 2020Review
- pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP.BMC bioinformatics · 2020Article
- A Genocentric Approach to Discovery of Mendelian Disorders.American journal of human genetics · 2019Article
- Bioinformatics applications on Apache Spark.GigaScience · 2018Review
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Authors and funding
5 authors.
Funding
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Abstract
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.
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Registered trials
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