YAMP: a containerized workflow enabling reproducibility in metagenomics research.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Whole shotgun metagenomic data analysis requires robust and reproducible computational workflows.
• Existing pipelines may lack user-friendliness, flexibility, or cross-platform compatibility.

Purpose of the Study:

• To develop a user-friendly, reproducible, and flexible workflow for whole shotgun metagenomic data analysis.
• To facilitate collaborative research in metagenomics through containerization.

Main Methods:

• Development of Yet Another Metagenomics Pipeline (YAMP) as a containerized workflow.
• Implementation of support for UNIX-like systems, multiple job schedulers, and Amazon AWS cloud.
• Focus on modularization and customization for user flexibility.

Main Results:

• YAMP provides a ready-to-use solution for nonexpert users.
• The workflow ensures computational reproducibility via containerization.
• Bioinformaticians benefit from YAMP's modular design and customization options.

Conclusions:

• YAMP enhances the analysis of whole shotgun metagenomic data.
• The pipeline promotes reproducibility and collaboration in metagenomic research.
• YAMP offers a flexible and accessible tool for diverse users.