Strain/species identification in metagenomes using genome-specific markers

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Shotgun metagenome sequencing is a powerful tool for microbial community analysis.
• Accurate identification of microorganisms at the strain or species level in metagenomic data remains a significant challenge.

Purpose of the Study:

• To develop and validate a novel k-mer-based approach, GSMer, for strain/species-level identification in metagenomes.
• To identify genome-specific markers (GSMs) for microbial identification.

Main Methods:

• Developed GSMer, a k-mer-based method to identify genome-specific markers (GSMs).
• Identified millions of 50-mer GSMs from 5390 microbial genomes for strain and species identification.
• Evaluated GSM specificity using mock communities, sequenced genomes, and real metagenomes.

Main Results:

• Generated over 8.7 million strain-specific and 11.7 million species-specific GSMs.
• Demonstrated that 50 GSMs per strain are sufficient for identification with ≥0.25× coverage.
• Identified microbial strains/species associated with type 2 diabetes and obesity in gut metagenomes.

Conclusions:

• GSMer provides a robust method for accurate strain/species-level identification in metagenomic data.
• The approach offers valuable strain-level insights into microbial community composition.
• GSMer simplifies metagenomic analysis by eliminating complex pre-processing steps.