BugBuster: a novel automatic and reproducible workflow for metagenomic data analysis.

Area of Science:

• Bioinformatics
• Computational Biology
• Microbial Ecology

Background:

• Metagenomic sequencing yields vast datasets for microbial community analysis.
• Current analysis software is complex, posing a barrier for non-specialist researchers.
• Need for accessible, reproducible tools to interpret metagenomic data.

Purpose of the Study:

• To present BugBuster, a novel modular and reproducible workflow for metagenomic data analysis.
• To streamline the process of transforming raw metagenomic data into meaningful biological insights.
• To enhance accessibility of metagenomic analysis for a broader research community.

Main Methods:

• Developed BugBuster as a fully containerized workflow using Nextflow.
• Implemented modular components for analysis at read, contig, and genome-assembled genome levels.
• Integrated modules for taxonomic profiling and resistome characterization.

Main Results:

• BugBuster provides a streamlined pipeline for metagenomic data analysis.
• The containerized nature allows deployment on diverse computational platforms (workstations, HPC, cloud).
• Facilitates robust, scalable, and reproducible analysis of microbial community datasets.

Conclusions:

• BugBuster democratizes metagenomic data analysis by simplifying complex processes.
• The workflow supports comprehensive microbial community characterization, including taxonomic and functional insights.
• Enhances the ability of researchers to leverage metagenomic data without extensive bioinformatics training.