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SparkBLAST: scalable BLAST processing using in-memory operations.

Marcelo Rodrigo de Castro1, Catherine Dos Santos Tostes2, Alberto M R Dávila2

  • 1Computer Science Department, Federal University of São Carlos, Rod. Washington Luís, Km 235, São Carlos, 21040-900, Brazil.

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|June 29, 2017
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SparkBLAST offers faster genomic data processing by leveraging Apache Spark for efficient sequence alignment. This parallelized approach significantly reduces computational times compared to traditional Hadoop systems.

Keywords:
Cloud computingComparative genomicsScalabilitySpark

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomic Data Analysis

Background:

  • Increasing genomic data necessitates scalable and efficient computational systems.
  • Traditional sequence alignment methods face challenges with large datasets.
  • Cloud computing and distributed frameworks offer potential solutions.

Purpose of the Study:

  • To develop and evaluate SparkBLAST, a parallelized sequence alignment tool.
  • To utilize cloud computing and Apache Spark for enhanced performance.
  • To analyze radionuclide-resistant bacterial genomes for similarity.

Main Methods:

  • Parallelization of the Basic Local Alignment Search Tool (BLAST) using Apache Spark.
  • Deployment on cloud computing platforms (Google Cloud, Microsoft Azure).
  • Comparative analysis against a Hadoop-based system.

Main Results:

  • SparkBLAST demonstrated superior speedup and reduced execution times compared to Hadoop.
  • Experiments were conducted using selected bacterial genomes.
  • Performance gains were observed in cloud environments.

Conclusions:

  • SparkBLAST's efficiency stems from Apache Spark's in-memory processing capabilities.
  • Reduced local I/O operations contribute to faster distributed BLAST processing.
  • The proposed framework effectively addresses the challenges of large-scale genomic data analysis.