NFTest: automated testing of Nextflow pipelines

Area of Science:

• Bioinformatics
• Computational Biology
• Software Engineering

Background:

• Increasing biomedical data volume necessitates complex computational workflows.
• Orchestration languages like Nextflow are crucial for integrating research algorithms.
• Ensuring software development practices in workflows is vital to prevent errors.

Purpose of the Study:

• To introduce NFTest, a framework for automated testing and validation of Nextflow pipelines.
• To facilitate test-driven development in biomedical research workflows.
• To reduce the complexity and burden of testing computational pipelines.

Main Methods:

• Developed NFTest as an open-source Python framework.
• Implemented customizability options for Nextflow features.
• Designed a flexible interface for defining and running workflow tests with configurable assertions.

Main Results:

• NFTest enables automated testing and validation of Nextflow pipelines.
• The framework is customizable for complex workflow testing.
• It simplifies the testing process for developers, reducing the barrier to rigorous validation.

Conclusions:

• NFTest lowers the barrier to rigorous testing of biomedical workflows.
• Automated testing with NFTest helps reduce computational errors in biomedicine.
• The framework supports the development of more reliable and reproducible research pipelines.