MeTAline: enabling reproducible and scalable metagenomic analyses

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Microbial communities play crucial roles in health and disease.
• Shotgun metagenomic sequencing provides high taxonomic resolution and functional insights into microbial communities.
• Analyzing metagenomic data requires specialized computational tools and resources.

Purpose of the Study:

• To present MeTAline, an efficient and reproducible bioinformatics pipeline for shotgun metagenomic data analysis.
• To streamline the process of taxonomic and functional characterization of microbial communities.
• To make advanced metagenomic analysis accessible for researchers.

Main Methods:

• MeTAline is implemented using Snakemake for workflow management.
• The pipeline includes read trimming, host DNA removal, taxonomic classification (k-mer and gene marker-based), and functional annotation.
• Containerization with Docker and Singularity ensures ease of installation and portability.
• The architecture supports high parallelization for local and high-performance computing.

Main Results:

• MeTAline provides a comprehensive workflow for shotgun metagenomic data analysis.
• The pipeline ensures reproducibility and efficiency through its implementation and containerization.
• It facilitates detailed taxonomic and functional characterization of microbial communities.

Conclusions:

• MeTAline offers an accessible and robust solution for shotgun metagenomic data analysis.
• The pipeline enhances the ability to study microbial communities and their associations with health and disease.
• MeTAline is freely available, promoting wider adoption in the research community.