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DeBasher: a flow-based programming bash extension for the implementation of complex and interactive workflows with

Daniel Ortiz-Martínez1

  • 1Department of Mathematics and Computer Science, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007, Barcelona, Spain. daniel.ortiz@ub.edu.

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|April 16, 2025
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Summary
This summary is machine-generated.

DeBasher, a flow-based programming (FBP) Bash extension, enables complex cyclic bioinformatics workflows with user interactivity and triggers. This tool efficiently processes large genomic datasets, enhancing workflow management capabilities.

Keywords:
BashData flow programmingFlow-based programmingPipelineWorkflowWorkflow interactivityWorkflow managerWorkflow triggers

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Software Engineering

Background:

  • Bioinformatics data analysis relies heavily on workflow managers (WfMs).
  • Data flow programming is common in WfMs, enabling reactive parallel processing.
  • Existing WfMs often lack support for cyclic workflows and interactivity.

Purpose of the Study:

  • To introduce DeBasher, a novel tool for managing complex bioinformatics workflows.
  • To enable cyclic workflows and enhance user interactivity in workflow execution.
  • To provide a scalable and flexible solution for bioinformatics data analysis.

Main Methods:

  • DeBasher utilizes the flow-based programming (FBP) paradigm.
  • It allows workflow components to control their lifecycle and store state.
  • Features include user-defined triggers, runtime piping, and dynamic scheduling.

Main Results:

  • DeBasher successfully executed complex cyclic workflows.
  • User interactivity allowed real-time alteration of running workflows.
  • The tool processed 131.7 TB of genomic data using a variant calling pipeline.

Conclusions:

  • DeBasher is a versatile FBP Bash extension for complex, interactive, and scalable workflows.
  • It offers significant advantages for bioinformatics analyses requiring advanced workflow management.
  • The tool demonstrates applicability in dynamic scheduling and large-scale data processing.