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BigDataScript: a scripting language for data pipelines.

Pablo Cingolani1, Rob Sladek2, Mathieu Blanchette2

  • 1McGill University School of Computer Science, 3480 University Street, Montreal, Québec H3A 0E9 and McGill University and Génome Québec Innovation Centre, 740 Dr. Penfield Avenue, Montréal, Québec H3A 0G1, Canada McGill University School of Computer Science, 3480 University Street, Montreal, Québec H3A 0E9 and McGill University and Génome Québec Innovation Centre, 740 Dr. Penfield Avenue, Montréal, Québec H3A 0G1, Canada.

Bioinformatics (Oxford, England)
|September 6, 2014
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Summary
This summary is machine-generated.

BigDataScript (BDS) is a new programming language designed to simplify the development and management of complex data processing pipelines. It enhances robustness and portability across diverse computational infrastructures, reducing development time.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Data Science

Background:

  • Analyzing large biological datasets necessitates complex, time-consuming processing pipelines.
  • Existing pipelines often struggle with software/hardware failures and portability issues.

Purpose of the Study:

  • Introduce BigDataScript (BDS), a novel programming language for data processing pipelines.
  • Enhance pipeline robustness, portability, and simplify management.

Main Methods:

  • Developed a script-like programming language with a minimalist syntax.
  • Implemented features for hardware abstraction, absolute serialization, and lazy processing.
  • Designed for seamless execution across various computational environments.

Main Results:

  • BDS pipelines run unmodified on diverse architectures, from laptops to cloud environments.
  • Achieved robustness through absolute serialization and lazy processing, enabling error recovery.
  • Simplified pipeline implementation, execution, and management, reducing development and debugging cycles.

Conclusions:

  • BDS offers a powerful, user-friendly solution for complex bioinformatics pipeline development.
  • Improves efficiency and reliability in large-scale biological data analysis.
  • Promotes cleaner code and faster iteration in research workflows.