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BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments.

Maria Luiza Mondelli1, Thiago Magalhães1, Guilherme Loss1

  • 1National Laboratory for Scientific Computing, Petrópolis, Rio de Janeiro, Brazil.

Peerj
|September 7, 2018
PubMed
Summary
This summary is machine-generated.

BioWorkbench streamlines bioinformatics experiments by automatically collecting and analyzing performance and scientific data. This framework significantly reduces execution times, enhancing computational efficiency for complex biological data analysis.

Keywords:
BioinformaticsData analyticsProfilingProvenanceScientific workflows

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Sequencing advances generate vast biological data, necessitating large-scale bioinformatics experiments.
  • These experiments are computation- and data-intensive, requiring high-performance computing and specialized systems.
  • Managing and analyzing complex bioinformatics workflows presents significant challenges.

Purpose of the Study:

  • To introduce BioWorkbench, a framework for managing and analyzing bioinformatics experiments.
  • To automatically collect provenance data, encompassing workflow performance and scientific domain information.
  • To simplify access to provenance data through a web application for enhanced analysis.

Main Methods:

  • Developed BioWorkbench, a framework integrating Scientific Workflow Management Systems and databases.
  • Implemented automatic collection of provenance data (performance and scientific).
  • Evaluated BioWorkbench using three case studies: SwiftPhylo, SwiftGECKO, and RASflow.

Main Results:

  • BioWorkbench demonstrates scalability and high-performance, reducing execution time by up to 98% in case studies.
  • Provenance data analysis provided insights from both computational and scientific perspectives.
  • Machine learning techniques were explored to enrich the analysis process.

Conclusions:

  • BioWorkbench effectively manages and analyzes large-scale bioinformatics experiments.
  • The framework enhances computational efficiency and simplifies data analysis through provenance tracking.
  • Automated data collection and analysis capabilities of BioWorkbench support advanced biological research.