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

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.