Metagenomic Approaches to Foodborne Pathogen Detection

By Vijay Kumar MalesuReviewed by Lauren Hardaker

Introduction 
Automation and Sample-to-Answer Workflows 
Multi-Omics Integration 
Cloud-Based Bioinformatics and Real-Time Surveillance 
High-Throughput Food Safety Across the Supply Chain 
Future Outlook 
Conclusion 
References
Further Reading 

Metagenomics and next-generation sequencing approaches integrate automation, multi-omics, and advanced bioinformatics to deliver real-time insights into microbial activity, contamination pathways, and food quality. These advances enable earlier intervention, improved traceability, and more effective risk management across the entire food supply chain. 

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Introduction 

Traditional food safety testing relies on culture-based methods and targeted molecular tools such as polymerase chain reaction (PCR). While culture techniques are reliable, they are slow and depend on the growth of viable organisms, limiting timely detection and may fail to detect viable but non-culturable or fastidious microorganisms. PCR methods provide quicker detection for known targets but are not useful for detecting unknown or diverse microbial populations.⁴ 

Furthermore, traditional testing is episodic, providing only a single snapshot of a contamination event rather than continuous monitoring. Metagenomics overcomes these limitations by enabling culture-independent sequencing of all genetic material within a sample, enabling comprehensive, less-biased monitoring of entire microbial communities without the need for prior cultivation.^1,2,4

This article explores how metagenomics is transforming food safety from slow, targeted testing to more comprehensive, high-resolution, and potentially near-real-time surveillance, enabling the detection of microbial communities without culturing.

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Automation and Sample-to-Answer Workflows 

Automation in food safety testing is increasingly driven by next-generation sequencing (NGS)-based sample-to-answer workflows that integrate multiple steps, including sample collection, nucleic acid extraction, library preparation, sequencing, and bioinformatics analysis into unified pipelines. Unlike traditional culture-dependent methods that require isolation and growth of microorganisms, automated sequencing workflows bypass these steps, significantly reducing turnaround time and enabling faster detection of foodborne pathogens.^2,4

Advancements in sequencing platforms, including high-throughput and portable systems, have made workflows faster and more flexible. Techniques such as short- and long-read sequencing enable fast, detailed analysis of microbial communities, supporting high-throughput testing across many samples and enabling strain-level resolution and detection of antimicrobial resistance genes. This increased efficiency can lower long-term operational costs by reducing labor intensity and enabling batch processing of samples.^2-4

There are still many challenges to overcome, including variability in food matrices and low abundance of pathogens relative to background microbiota, which can hinder detection sensitivity. Additionally, if the sample is contaminated during preparation or sequencing, this may introduce bias; therefore, strict quality control will be required. Standardization of protocols across laboratories is needed, as differences in sampling, processing, and analysis techniques affect results. Addressing these challenges is necessary for the widespread adoption of automated metagenomics methods in routine food safety monitoring.^1,2,4

Multi-Omics Integration 

Metagenomics has improved food safety by identifying microbial communities, but it primarily focuses on listing organisms, making it difficult to assess their activity. The limited ability to determine microbial activity is due to deoxyribonucleic acid (DNA)-based methods for identifying the microorganisms that exist within food. DNA methods can be related to an organism’s ability to potentially cause food safety issues, but they are not able to identify whether or not an organism is actively causing food safety issues at the time of sampling. Therefore, combining metagenomics with other methodologies, such as metatranscriptomics, proteomics, and metabolomics, would provide more information on the functions and activities of microorganisms and their interactions within complex microbial ecosystems.^2,3,4

Metatranscriptomics analyzes RNA transcripts, providing insights into which genes are actively expressed under specific conditions. This provides information on how microbes respond to environmental changes, including changes in microbial activity. Additionally, proteomics identifies proteins produced by organisms during their lifetime, allowing researchers to study functional processes and detect enzymes or toxins that may persist even after microbial cells are no longer viable. Finally, metabolomics examines metabolites and shows how microorganisms affect food quality and safety. Therefore, these three methods of analysis demonstrate a shift from an emphasis on identifying the presence of microorganisms to understanding their functional behavior.^1-4

In food systems, multi-omics integration has practical applications in predicting shelf life, detecting spoilage, and identifying metabolic pathways associated with contamination or degradation. For example, monitoring changes in gene expression and metabolites can reveal early spoilage before it is visible. Similarly, understanding how microbes interact helps identify indicator species and microbial interactions associated with pathogen presence or inhibition. By integrating multiple layers of biological data, researchers can move beyond static microbial profiling toward dynamic, systems-level understanding, ultimately enhancing the precision and effectiveness of food safety monitoring.^1,2,4

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Cloud-Based Bioinformatics and Real-Time Surveillance 

As metagenomic technologies become more integrated into food safety research, the use of localized data analysis methods is being replaced by integrated bioinformatics systems that can scale up to support significantly larger volumes of data from high-throughput sequencing. High-throughput sequencing generates large amounts of data, which require specialized computational pipelines and high-performance or cloud-based infrastructure to process, store, and analyze. Recent advances in bioinformatics pipelines enable efficient processing and management of these datasets, enabling large-scale analysis and more rapid decision-making during food safety surveillance activities.^1,2,4

NGS technologies have strengthened surveillance capabilities by enabling precise identification and tracking of foodborne pathogens and facilitating phylogenetic analysis for outbreak source attribution. These methods help detect outbreaks, identify contamination sources, and monitor food safety throughout the supply chain. The integration of sequencing data from different locations enhances multi-site monitoring and provides a more comprehensive understanding of contamination patterns.^1,2,4

However, a number of challenges still exist despite these benefits. One of the major issues is inconsistency in the methods used to analyze and store data. These inconsistencies may make it difficult to standardize study results and compare studies. Similar to other large-scale databases, large-scale genomic databases also raise issues regarding data ownership, security, and controlled access. Addressing these issues is essential to fully realize the potential of scalable bioinformatics and real-time surveillance systems in improving food safety outcomes.^2,4

High-Throughput Food Safety Across the Supply Chain 

Advances in NGS and metagenomics are driving a shift in food safety from traditional batch testing toward continuous, high-throughput monitoring across the supply chain. Conventional methods typically assess samples at discrete time points, whereas sequencing-based approaches enable broader and more frequent analysis of microbial communities in food and processing environments. This transition improves the ability to detect contamination events earlier and with greater resolution, including detection of low-abundance pathogens that may be missed by conventional methods.¹

High-throughput sequencing technologies are now applied at multiple stages, including production, storage, and retail. In production settings, they help identify microbial sources from raw materials and processing environments. They track spoilage and microbial changes during storage to help determine shelf life. At the retail level, these tools support quality control and product safety verification. Such applications allow continuous tracking of microbial dynamics rather than relying on isolated testing events.^1,2,4

These capabilities support early contamination detection and provide data that can inform predictive risk models. By identifying changes in microbial populations before visible spoilage or outbreaks occur, producers can act fast to improve safety. As a result, metagenomics-based monitoring can help reduce product recalls, improve regulatory compliance, and enhance traceability across the food supply chain, thereby strengthening overall food safety systems.^1,2,4

Future Outlook 

The future of food safety is becoming more integrated, predictive, and preventive, driven by advances in metagenomics and NGS technologies. In addition to metagenomics, which identifies the microbial composition of food, automated workflows and multi-omics are producing greater insights into the microbial communities of food and their functional dynamics, while metatranscriptomics and metabolomics provide information on gene expression and metabolic activity. Collectively, these technologies will provide an even more comprehensive understanding of food systems, including microbial interactions, resistance mechanisms, and environmental influences.^1,2,3

This integration shifts from reactive contamination detection to proactive prediction and prevention. High-throughput sequencing and data-rich analytical pipelines help in early detection, preventing spoilage and outbreaks before they happen, and supporting data-driven risk modeling and decision-making. These developments are paving the way for digital food systems where large datasets can be used to model risks and improve decision-making across the supply chain.^2,3

Variations in methodology and analytics across studies have been a barrier due to the lack of standardization and comparability. The evolution of regulatory frameworks will have important implications for the new generation of sequencing-based diagnostics, which are still evolving. The cost of advanced technology affects how soon new technologies can be adopted on a wide scale.

Addressing these barriers will be essential to fully realize the transition toward predictive and preventative food safety systems driven by metagenomics and integrated omics approaches.^1,2,3

Conclusion 

Metagenomics is emerging as a central tool in modern food safety, transforming how microbial risks are detected and managed. By enabling culture-independent analysis of entire microbial communities, it supports continuous, system-wide monitoring across the food supply chain rather than isolated testing events.

This method improves early detection, traceability, and outbreak prevention while enabling the identification of novel or previously unrecognized hazards. However, for widespread use, standardized methods, stronger regulations, and robust infrastructure are needed. Cost considerations and technical expertise also influence implementation. Addressing these factors will be critical for integrating metagenomics into routine food safety systems globally.⁴

References

1. Forbes, J. D., Knox, N. C., Ronholm, J., Pagotto, F., & Reimer, A. (2017). Metagenomics: the next culture-independent game changer. Frontiers in microbiology. 8. DOI:10.3389/fmicb.2017.01069, https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2017.01069/full
2. Tigrero-Vaca, J., Díaz, B., Gu, G., & Cevallos-Cevallos, J. M. (2025). Next-generation sequencing applications in food science: fundamentals and recent advances. Frontiers in Bioengineering and Biotechnology. 13. DOI:10.3389/fbioe.2025.1638957, https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1638957/full
3. Aguiar-Pulido, V., Huang, W., Suarez-Ulloa, V., Cickovski, T., Mathee, K., & Narasimhan, G. (2016). Metagenomics, metatranscriptomics, and metabolomics approaches for microbiome analysis: supplementary issue: bioinformatics methods and applications for big metagenomics data. Evolutionary bioinformatics. 12. DOI:10.4137/EBO.S36436, https://journals.sagepub.com/doi/10.4137/EBO.S36436
4. Billington, C., Kingsbury, J. M., & Rivas, L. (2022). Metagenomics approaches for improving food safety: a review. Journal of Food Protection. 85(3). 448-464. DOI:10.4315/JFP-21-301, https://www.sciencedirect.com/science/article/pii/S0362028X22067916

Further Reading

• All Food and Beverage Analysis Content
• How Do Food Sensitivity Assays Work?
• Microbes in Raw Meat
• Eliminating American Foulbrood using Endolysins
• Examining Coacervate Formation at Protein-Polysaccharide Interfaces

Last Updated: Apr 20, 2026