Paper 1 & 2: Reproducibility - bcb420-2025/Keren_Zhang GitHub Wiki

Table of Contents Paper 1: "The five pillars of computational reproducibility: bioinformatics and beyond" Introduction The Five Pillars Key Issues Addressed Impact and Recommendations Conclusion Paper 2: "Reproducibility in Bioinformatics" Introduction Challenges in Reproducibility Data Provenance Portability and Scalability Features of Workflow Managers Conclusion Future Directions

Authors: Mark Ziemann, Pierre Poulain, Anusuiya Bora

Published in: Briefings in Bioinformatics, 2023

The paper addresses the urgent need for computational reproducibility in bioinformatics and related scientific disciplines. It proposes a framework comprising five essential pillars designed to ensure the reliability of computational research for future replication.

1. Literate Programming: Integrates code with narrative to enhance understanding and reproducibility.
2. Code Version Control and Sharing: Utilizes platforms like GitHub for code management and dissemination.
3. Compute Environment Control: Employs containers such as Docker to maintain consistent computing environments.
4. Persistent Data Sharing: Ensures the availability and reusability of data through recognized repositories.
5. Documentation: Provides thorough documentation to explain and contextualize research methods and data.

• The paper points out the widespread issues with reproducibility in scientific research, with a focus on bioinformatics where reproducibility rates are notably low.
• It discusses historical failures in bioinformatics that have led to significant consequences, making a case for improved practices to avert similar problems.

• The authors advocate for the widespread adoption of these pillars within the scientific community to enhance the reliability and credibility of computational research.
• They argue that these practices could lead to quicker translation of research into practical applications, increasing the effectiveness of scientific outputs.

The paper concludes that while the necessary technology and frameworks to enhance reproducibility exist, a cultural change within the scientific community is essential for these practices to be widely implemented.

• High-throughput technologies and massive data generation in biomedical research have necessitated the use of workflow managers.
• Workflow managers help in creating reproducible, scalable, and shareable analysis pipelines.

• Variability in software versions, operating systems, and computational resources affects the reproducibility of bioinformatics analyses.
• Workflow managers address these issues by standardizing analysis pipelines and maintaining consistent environments across different systems.

• Data provenance is crucial for reproducibility, detailing the methods, versions, and parameters used in computational analyses.
• Workflow managers automate tracking of these elements, enhancing transparency and reproducibility.

• Workflow managers ensure that pipelines can be executed with identical parameters across different systems.
• They support containerization and package management, making software installation and pipeline execution consistent and portable.

• They offer tools for managing dependencies, automating tasks, and handling large-scale data effectively.
• Examples include Nextflow, Snakemake, and Galaxy, each with unique features suited for different aspects of bioinformatics workflows.

• As biomedical data volumes grow, the role of workflow managers becomes increasingly critical.
• They not only facilitate the reproducibility of computational analyses but also support scalable and efficient data processing.

• Continued development and standardization of workflow managers are expected to further enhance reproducibility and efficiency in bioinformatics.
• Integration with cloud computing resources and expansion of community-developed pipelines are key areas of focus.