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Narrative review compares mNGS enrichment techniques against shotgun sequencing for pathogen detection workflowsNew mNGS enrichment methods may improve pathogen detection compared to standard shotgun sequencing

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Key Takeaway
Note that enrichment techniques may compromise the hypothesis-free nature of shotgun mNGS.

This narrative review examines various mNGS enrichment techniques, including PCR-based enrichment, CRISPR-Cas9 enrichment, molecular inversion probes, nanopore adaptive sequencing, and hybridisation capture-based methods. These approaches are compared against standard shotgun mNGS across secondary outcomes such as methodology, cost, sensitivity, specificity, and ease of integration into clinical workflows. The scope includes applications for pathogen detection and antimicrobial resistance profiling as well as whole-genome sequencing capabilities.

The authors highlight that while these enrichment strategies offer targeted advantages, they may compromise the hypothesis-free nature and breadth of shotgun mNGS. This limitation is a key consideration for laboratories deciding between broad-spectrum and targeted sequencing approaches for clinical diagnostics.

The review concludes that these findings should guide the optimisation of mNGS workflows in clinical diagnostics. Clinicians and laboratory professionals must weigh the trade-offs between targeted sensitivity and the loss of broad pathogen discovery inherent in shotgun sequencing.

Standard shotgun mNGS is a powerful tool that looks at all DNA in a sample without bias. However, a recent narrative review suggests that adding specific enrichment steps could help find certain pathogens more easily. These methods include PCR-based enrichment, CRISPR-Cas9 enrichment, and others like molecular inversion probes or nanopore adaptive sequencing.

The review looked at how these new techniques compare to the standard shotgun approach. They examined factors like cost, sensitivity, and how easy it is to use them in a hospital lab. The goal is to guide doctors and lab staff on how to optimize their diagnostic workflows for better patient care.

But there is a trade-off to consider. Using these enrichment steps changes the nature of the test. It compromises the hypothesis-free nature and breadth of the standard shotgun method. This means the test no longer looks at everything equally, which is a significant limitation for a method that prides itself on being unbiased.

What this means for you:
Enrichment methods may improve detection but limit the unbiased view of shotgun mNGS.

Study Details

Study typeSystematic review
EvidenceLevel 1
PublishedMay 2026
View Original Abstract ↓
Metagenomic next-generation sequencing (mNGS) offers a powerful, hypothesis-free approach for pathogen detection in clinical samples, allowing the identification of both known and novel microorganisms. However, the predominance of host nucleic acid in most samples poses a significant challenge, often overshadowing low-abundance pathogen sequences and increasing the cost of mNGS due to the high sequencing depth required. Enrichment techniques which selectively amplify pathogen-specific sequences can help to overcome this challenge, improving the sensitivity, specificity, and overall efficiency of mNGS – albeit while compromising the hypothesis-free nature and breadth of shotgun mNGS. As such, they can augment the use of mNGS in clinical scenarios where a more targeted approach is needed. This review provides a comprehensive analysis of the main enrichment techniques currently employed in the field, including PCR-based enrichment, CRISPR-Cas9 enrichment, molecular inversion probes (MIP), nanopore adaptive sequencing (AS), and hybridisation capture-based methods. We evaluate each method on a range of metrics including methodology, cost, sensitivity, specificity, and ease of integration into clinical workflows, as well as describing their application to date for purposes including pathogen detection, antimicrobial resistance profiling, and whole-genome sequencing across diverse clinical sample types. Current limitations and future directions for refinement and implementation of these techniques are also discussed. By summarising the current landscape and latest advancements in mNGS enrichment strategies, this review aims to guide the optimisation of mNGS workflows in clinical diagnostics and highlight key areas for future research.
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