Insights into the analysis of microbial communities in fermented foods from the perspective of DNA-based techniques.
TL;DR
The exponential growth of sequencing data will propel the era of precision fermentation, and standardized data preprocessing and specific databases are critical for improving characterization.
Abstract
The quality, flavor, and stability of fermented foods depend on the microbial community. However, microbial dynamics are difficult to observe directly, leading to limited control over fermentation. High-throughput sequencing is a revolutionary tool for microbial characterization, among which DNA-based amplicon and metagenomic sequencing are core techniques. Nevertheless, the related data processing workflows in the context of fermented foods have not yet been systematically summarized, hindering the translation of research findings into fermentation practices. This review clarifies the applications of amplicon and metagenomic sequencing in fermented foods. For amplicon sequencing, the impacts of target regions, data preprocessing, and reference databases are addressed. For metagenomic sequencing, sequencing strategies, read-based and binning-based analytical methods, functional annotation, and species-specific databases are discussed. In addition, major strategies for downstream analysis of community data are summarized, including microbial diversity, co-occurrence networks, niche and community assembly, key environmental drivers, and machine learning-based prediction. Amplicon sequencing efficiently reveals microbial succession during fermentation but has limitations in functional annotation. Metagenomic sequencing is notable for functional annotation, enabling the linkage between microbial communities and metabolic potential alongside community characterization. Standardized data preprocessing and specific databases are critical for improving characterization. For community data, integrated analysis allows uncovering the driving factors of microbial succession, thereby helping to regulate fermentation. Notably, the compositional nature of the data must be considered and validated to avoid spurious associations. In summary, the exponential growth of sequencing data will propel the era of precision fermentation.