SHOGUN: a modular, accurate and scalable framework for microbiome quantification.

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Shotgun metagenomics generates vast amounts of sequencing data.
• Accurate profiling of taxonomic and gene abundances is crucial for understanding microbial communities.
• Existing tools may lack flexibility or scalability for diverse research needs.

Purpose of the Study:

• To introduce SHOGUN, a novel software pipeline for analyzing short-read shotgun metagenomics data.
• To provide a scalable, modular, and flexible solution for taxonomic and gene abundance profiling.
• To enhance the convenience and reproducibility of metagenomics research.

Main Methods:

• SHOGUN profiles taxonomic and gene abundances using short-read shotgun metagenomics data.
• It offers a choice of three DNA alignment tools, from k-mer-based to gapped alignment.
• The pipeline incorporates an empirical Bayesian method for taxonomy assignment disambiguation.

Main Results:

• The SHOGUN pipeline is scalable, modular, and flexible, supporting individual or automated workflows.
• Users can create custom reference databases and select alignment tools based on their needs.
• It produces both taxonomy and gene abundance profiles efficiently.

Conclusions:

• SHOGUN facilitates convenient and reproducible metagenomics research.
• Its flexible design and multiple alignment options cater to various computational resources and analysis requirements.
• The software is readily available via conda and integrates with QIIME2 and QIITA.