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A Method to Define the Effects of Environmental Enrichment on Colon Microbiome Biodiversity in a Mouse Colon Tumor Model

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CAMP: A modular metagenomics analysis system for integrated multi-step data exploration.

Lauren Mak^1,2, Braden Tierney^2,3, Cynthia Ronkowski²

¹Tri-Institutional Computational Biology & Medicine Program, Weill Cornell Medicine of Cornell University, NY, USA.

Biorxiv : the Preprint Server for Biology

|April 17, 2023

Summary

This summary is machine-generated.

We developed the Core Analysis Metagenomics Pipeline (CAMP), a modular system for scalable metagenomics data analysis. CAMP simplifies complex workflows and enhances data exploration through integrated visualizations, improving microbial community insights.

Keywords:

De Novo Assembly Gene Cataloguing Metagenome-Assembled Genomes Metagenomics Metaviromics Taxonomic Classification Workflow Management Systems

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Area of Science:

Bioinformatics
Computational Biology
Metagenomics

Background:

Metagenomics sequencing generates vast datasets for microbial community analysis.
Existing bioinformatics pipelines face challenges in scalability, compatibility, and maintainability.
Designing end-to-end workflows for metagenomics data is complex due to numerous tools and file formats.

Purpose of the Study:

To develop a flexible and scalable metagenomics analysis system.
To address limitations of current one-click bioinformatics pipelines.
To facilitate easier exploration of large metagenomics datasets.

Main Methods:

Developed the Core Analysis Metagenomics Pipeline (CAMP) using Snakemake.
Implemented a module-based architecture for independent or combined workflow execution.
Established a standardized module and working directory structure.
Integrated aggregated summary statistics and Jupyter notebook visualizations.

Main Results:

CAMP was applied to ten metagenomics samples.
Demonstrated seamless inter-communication between different analytical outputs.
Showcased how modularity and intermediate visualizations aid data interpretation.
CAMP provides a robust framework for complex metagenomics analyses.

Conclusions:

CAMP offers an extensible and distributable solution for metagenomics workflows.
The modular design enhances flexibility and maintainability.
Integrated visualizations improve the understanding of complex microbial community data.