Sunbeam: an extensible pipeline for analyzing metagenomic sequencing experiments.

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Metagenomic sequencing is crucial for understanding microbial communities.
• Analysis involves multiple complex steps: quality control, adapter trimming, host decontamination, classification, assembly, and alignment.
• Standardizing these steps is essential for reproducible results.

Purpose of the Study:

• To present Sunbeam, a modular and extensible pipeline for metagenomic data analysis.
• To introduce Komplexity, a tool for removing low-complexity sequences.
• To provide a standardized and reproducible workflow for metagenomic analysis.

Main Methods:

• Developed a modular pipeline (Sunbeam) using Python and Snakemake.
• Integrated a software tool (Komplexity) for filtering low-complexity sequences.
• Implemented an extension framework for custom analysis steps.

Main Results:

• Sunbeam offers a consistent and reproducible method for metagenomic data preprocessing and analysis.
• The pipeline is easily installable, requires no administrative access, and is compatible with cluster computing.
• Komplexity effectively removes problematic low-complexity nucleotide sequences.

Conclusions:

• Sunbeam standardizes analytical steps, facilitating in-depth analyses and comparisons in metagenomic studies.
• The pipeline's user-extensible nature allows for custom workflow integration.
• Sunbeam is freely available, well-documented, and actively maintained.