FoodMicrobionet

FoodMicrobionet provides a consolidated bioinformatics resource for culture-independent analysis of food-associated bacterial communities using 16S rRNA gene high-throughput sequencing data.

Key Features:

• Data Aggregation: Consolidates 16S rRNA gene targeted high-throughput sequencing data from seventeen studies on dairy, meat, sourdough, and fermented vegetable products.
• Standardized Classification: Classifies each sample using the FoodEx2 system to enable consistent comparison across studies.
• OTU-level Data: Represents microbial composition at the Operational Taxonomic Unit (OTU) level for community analysis.
• Network Analysis (Gephi): Constructs and analyzes networks of OTUs and samples using Gephi to examine network properties and node relationships.
• Network Visualizations: Generates network visualizations to examine relationships between OTUs and samples and to identify core and sample-specific bacterial communities.
• External References: Integrates links to external resources such as NCBI taxonomy and original research articles for taxonomic and literature reference.
• Microbial Interaction Analysis (CoNet): Applies CoNet for microbial interaction network analysis and reports that food bacterial community networks often show lower complexity than human or soil microbiomes, a pattern possibly influenced by dataset bias toward fermented foods and starter cultures.

Scientific Applications:

• Core community identification: Identification of core bacterial communities across food matrices at the OTU level.
• Quality control and monitoring: Analysis of microbial composition to support quality control and microbiological stability assessments.
• Fermentation research: Informing development and evaluation of fermentation processes by revealing microbial community structure and interactions.
• Comparative studies: Comparative analysis of bacterial communities across different food types and studies to investigate ecological and compositional differences.

Methodology:

Integrates 16S rRNA gene targeted high-throughput sequencing data from multiple studies using standardized pipelines, classifies samples with FoodEx2, represents community composition as OTUs, and performs network construction and analysis with Gephi and CoNet.