A Framework for Analytical Validation of Genomic DNA-Based Shotgun Metagenomic Next-Generation Sequencing Workflows
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Background
Metagenomic next-generation sequencing (mNGS) is increasingly adopted as a rapid and unbiased alternative to culture-based pathogen detection. However, standardized approaches for analytical validation of mNGS workflows are lacking. Validation strategies based solely on comparison with culture are inherently limited due to low diagnostic yield of culture, and the inability to derive analytical parameters such as limit of detection (LoD) from clinical samples with unknown microbial loads. Systematic analytical approaches are therefore required to define mNGS workflow performance independently of clinical comparators. In addition to diagnostics, mNGS is widely applied to microbiome profiling, where preservation of microbial diversity at low DNA inputs is a key performance criterion.
Materials and Methods
The analytical performance of the PaRTI-Seq mNGS workflow (Micronbrane, Taiwan), comprising host depletion, DNA extraction, library preparation, and bioinformatics analysis, was evaluated. Library-level ana0lytical sensitivity was assessed using quantified genomic DNA (gDNA) from Enterobacter hormaechei , Candida albicans , and Mycobacterium smegmatis . Workflow-level sensitivity was evaluated using enumerated suspensions of E. hormaechei , Staphylococcus aureus , M. smegmatis , Candida albicans and Aspergillus brasiliensis . Microbiome performance at low DNA input was evaluated using stool, saliva and gut mock community samples at 50ng, 5ng, and 0.5ng gDNA input and compared with an alternative low-input library preparation workflow.
Results
The library preparation module demonstrated analytical sensitivity down to 0.1 pg of E. hormaechei and C. albicans gDNA, and 1pg for M. smegmatis gDNA. Workflow-level LoDs were determined to be 1 CFU for E. hormaechei , 272 CFUs for S. aureus, 39 CFUs for A. brasiliensis , 36 CFUs for C. albicans and 3,702 CFUs for M. smegmatis . Differences in extraction efficiencies and extracted gDNA qualities appeared to be important determinants of workflow-level LoDs. For microbiome applications, Micronbrane’s Unison Ultra-low DNA input library preparation kit maintained more consistent bacterial diversity indices down to 5ng DNA input for stool and saliva samples, and the gut mock community than the comparator workflow.
Conclusion
This study outlines practical and generalizable analytical validation approaches for mNGS library preparation and complete workflows using defined spike-in models, providing a foundation for informed clinical interpretation and implementation of mNGS in diagnostic and microbiome applications.
