CAMP: a modular metagenomics analysis system for integrated multistep data exploration.

Area of Science:

• Bioinformatics
• Computational Biology
• Microbial Ecology

Background:

• Metagenomics sequencing generates vast datasets, offering insights into microbial communities.
• Managing diverse bioinformatics tools and file formats for metagenomics analysis presents scalability and compatibility challenges.
• Existing one-click pipelines often lack flexibility and can hinder reproducibility.

Purpose of the Study:

• To develop a robust, extensible, and distributable workflow system for metagenomics data analysis.
• To address the challenges of designing scalable workflows and exploring complex output data.
• To facilitate seamless integration and communication between different analytical steps in metagenomics.

Main Methods:

• Developed the Core Analysis Modular Pipeline (CAMP), a module-based system using Snakemake.
• Implemented a standardized module and directory architecture for CAMP.
• Enabled independent or sequential execution of modules for various analyses (e.g., preprocessing, assembly).
• Integrated summary statistics reports and Jupyter notebook visualizations.

Main Results:

• CAMP was applied to 10 metagenomics samples, demonstrating its modularity and ease of use.
• The system successfully produced target data formats and visualizations.
• Built-in data visualization facilitated clear communication of results from different analytical purposes.

Conclusions:

• CAMP provides an easily extensible yet robustly distributable solution for metagenomics workflows.
• The modular design and integrated visualizations enhance the analysis and interpretation of complex microbial community data.
• The CAMP ecosystem offers a standardized framework for reproducible and scalable metagenomics research.