NFTest: automated testing of Nextflow pipelines
Feb 1, 2024·
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0 min read
Yash Patel
Chenghao Zhu
Takafumi N. Yamaguchi
Yuan Zhe Bugh
Mao Tian
Aaron Holmes
Sorel Fitz-Gibbon
Paul C. Boutros
Abstract
Motivation The ongoing expansion in the volume of biomedical data has contributed to a growing complexity in the tools and technologies used in research with an increased reliance on complex workflows written in orchestration languages such as Nextflow to integrate algorithms into processing pipelines. The growing use of workflows involving various tools and algorithms has led to increased scrutiny of software development practices to avoid errors in individual tools and in the connections between them.
Results To facilitate test-driven development of Nextflow pipelines, we created NFTest, a framework for automated pipeline testing and validation with customizability options for Nextflow features. It is open-source, easy to initialize and use, and customizable to allow for testing of complex workflows with test success configurable through a broad range of assertions. NFTest simplifies the testing burden on developers by automating tests once defined and providing a flexible interface for running tests to validate workflows. This reduces the barrier to rigorous biomedical workflow testing and paves the way toward reducing computational errors in biomedicine.
Availability and implementation NFTest is an open-source Python framework under the GPLv2 license and is freely available at https://github.com/uclahs-cds/tool-NFTest. The call-sSNV Nextflow pipeline is available at: https://github.com/uclahs-cds/pipeline-call-sSNV.
Type
Publication
Bioinformatics

Authors
Research Assistant Professor
Chenghao Zhu is a Research Assistant Professor in the NCI-designated Cancer
Center at Sanford Burnham Prebys. His research focuses on developing
computational methods and software for proteogenomics and applying
proteogenomics to cancer diagnosis, prognosis, and clinico-epidemiologic
questions.