MGS-Fast: Metagenomic shotgun data fast annotation using microbial gene catalogs

Area of Science:

• Bioinformatics
• Metagenomics
• Microbial Ecology

Background:

• Current metagenomics shotgun sequencing (MGS) annotation relies on computationally intensive, low-stringency read mapping.
• Existing methods often use generic protein databases or reference genomes, limiting accuracy.

Purpose of the Study:

• To develop a faster and more accurate method for annotating metagenomic shotgun sequencing data.
• To provide a high-stringency alternative to current MGS annotation approaches.

Main Methods:

• Developed MGS-Fast, a novel analysis approach for shotgun whole-genome metagenomic data.
• Utilized Bowtie2 DNA-DNA alignment for high-stringency read mapping (>90% DNA sequence identity).
• Integrated with a curated gene catalog from human microbiome data.

Main Results:

• MGS-Fast demonstrated rapid and accurate functional annotation of metagenomic reads.
• Successfully detected differentially abundant Kyoto Encyclopedia of Genes and Genomes gene functions in liver disease and Human Microbiome Project data.
• Achieved high-stringency matches between metagenomic reads and annotated genes.

Conclusions:

• MGS-Fast provides a confident and efficient way to transfer functional annotations from gene databases to metagenomic reads.
• The method offers significant improvements in speed and accuracy for MGS data analysis.
• MGS-Fast is available as a free Galaxy workflow within a Docker image.