Abstract
Fermented foods, which contain a diversity of microbes and microbial metabolites, have been used for millennia to increase food security, flavor, and nutritional content; more recently, they have been recognized as potential mediators of human health. Metagenomics is a powerful approach to characterize microbes in fermented foods, providing high taxonomic resolution and functional insights. Here, we introduce the Microbial Food DataBase, a metagenomics-based approach designed for the identification of fermentation-associated microbes. Using this primary database of metagenome-assembled genomes and relevant deposited genomes of prokaryotes, eukaryotes, and common food-relevant substrates, we investigated 89 fermented food samples. We present a streamlined high-confidence characterization of microbial diversity in fermented food, identifying previously undiscovered genomes and facilitating strain-level tracking across food environments. The easy and robust functionality of the workflow has significant implications for advancing food safety, promoting desired microbial communities, and increasing sustainability in food production.IMPORTANCEFermented foods have microbial communities that influence food safety, flavor, and human health. Microbial Food DataBase (MiFoDB), an alignment-based sequencing workflow and database, addresses the limitations of existing tools by enabling strain-level resolution, identifying novel genomes, and providing functional insights into microbial communities. Applying MiFoDB to fermented food samples, we demonstrate its ability to uncover novel species, track microbial strains across substrates, and integrate functional annotations. Additionally, the outlined workflow is highly customizable and can be used to generate alignment-based databases for other microbial ecosystems. This work highlights the importance of fermentation-specific workflows for studying microbial food ecosystems, advancing food safety, sustainability, and innovation in fermented food research.