sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Structural variants (SVs) are key genetic variations linked to diseases, including cancer.
• Accurate SV detection from short-read sequencing data remains challenging due to computational and practical limitations.

Purpose of the Study:

• To present sv-callers, a portable workflow for parallel execution of multiple SV detection tools.
• To simplify the deployment, configuration, and addition of new analysis tools for SV detection.
• To facilitate SV analysis through provided Jupyter Notebook examples.

Main Methods:

• Developed a highly portable workflow enabling parallel execution of various SV detection tools.
• Integrated easy deployment of software dependencies and configuration management.
• Included example analyses within a Jupyter Notebook for user guidance.

Main Results:

• The workflow supports parallel execution of multiple SV callers.
• It simplifies the addition of new analysis tools and software dependencies.
• Demonstrated utility through somatic and germline SV analyses on high-performance computing systems.

Conclusions:

• Sv-callers offers a flexible and efficient solution for structural variant detection.
• The workflow streamlines complex SV analyses, aiding research into genetic diseases.
• Its portability ensures broad applicability across different computational environments.