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Published on: October 28, 2025
ScriptManager: a platform for scalable and reproducible high-resolution analysis of genomics datasets
Olivia W Lang1,2, Benjamin Beer2, Dean Zhang2
1Cornell EpiGenomics Facility, Cornell Institute of Biotechnology, Cornell University, Ithaca, New York, United States of America.
Biorxiv : the Preprint Server for Biology
|June 29, 2026
Summary
ScriptManager v1.0 offers a unified framework for reproducible genomics and epigenomics data analysis and visualization. This Java-based tool enhances workflow portability and transparency, enabling researchers to generate publication-quality figures across diverse computational infrastructures.
Area of Science:
- Genomics and Epigenomics
- Computational Biology
- Bioinformatics
Background:
- Diverse genomic and epigenomic assays generate complex data.
- Existing analysis and figure-generation tools are often assay-specific, limiting interoperability and reproducibility.
Purpose of the Study:
- To present ScriptManager v1.0, a Java-based framework for modular and reproducible genomics and epigenomics data analysis and visualization.
- To provide a unified and extensible solution for cross-assay visualization and workflow reproducibility.
Main Methods:
- Developed a Java-based framework (ScriptManager v1.0) with novel analytical modules.
- Integrated GUI session logging, automated testing, tutorials, and expanded documentation.
- Incorporated support for Singularity containers, Anaconda packaging, and Galaxy XML wrappers for enhanced reproducibility.
Main Results:
- ScriptManager v1.0 offers a unified framework for cross-assay visualization and workflow reproducibility.
- Demonstrated scalability of TagPileup analysis from single-core to a 10,305-job distributed analysis on the Open Science Grid (OSG) in under 2 hours.
- Enhanced workflow portability, transparency, and reproducibility for high-resolution genomic assays.
Conclusions:
- ScriptManager v1.0 bridges exploratory data analysis and publication-ready figure generation.
- Facilitates building, sharing, and reproducing genomic analyses across diverse computational infrastructures with minimal configuration.
- Improves the overall reproducibility and transparency of genomic data analysis workflows.
