# Evaluating Seqstant LiveGene Analysis in real-time assessment of metagenomic next-generation sequencing (mNGS) data from respiratory samples

## TL;DR

This study compares real-time metagenomic sequencing with traditional methods for detecting pathogens in respiratory samples, finding that the new method is non-inferior and offers faster results.

## Contribution

The study demonstrates the non-inferiority and potential advantages of real-time metagenomic next-generation sequencing (rt-mNGS) over conventional culture methods in respiratory pathogen detection.

## Key findings

- rt-mNGS showed non-inferiority to culture methods with statistical significance.
- rt-mNGS detected pathogens in 16.12% of samples where culture failed.
- Real-time analysis achieved a 93.75% positive predictive value at cycle 46.

## Abstract

The detection of pathogens causing infections by conventional diagnostic methods can be challenging and next-generation sequencing (NGS) technology offers a promising alternative method. In this study, we evaluated the performance of real-time metagenomic next-generation sequencing (rt-mNGS) for the detection of pathogens in respiratory samples.

We used rt-mNGS, using the Seqstant LiveGene Analysis platform, on 335 respiratory samples in comparison to conventional culture results.

We observed an overall good concordance in 71.64% (240/335) of the methods. The rt-mNGS outperformed the gold standard culture in 16.12% (54/335) of the samples, while the culture was superior in detecting the clinically relevant pathogen in 12.24% (41/335) of the samples. The non-inferiority of rt-mNGS was statistically significant (δ = 10, α = 0.05, 1 − β = 0.8). We also observed that the real-time analysis of NGS data is beneficial in obtaining reliable, timely results, as the initial report at cycle 46 exhibits a Positive Predictive Value (PPV) of 93.75% at the species-level with a sensitivity of 32.09%.

Overall, our study showed the non-inferiority of rt-mNGS compared to the standard-of-care microbiology for respiratory samples with statistical significance. Moreover, the rt-mNGS method exhibited superior sensitivity and superior overall performance. It also uniquely detected certain organisms that are typically hard to culture. However, rt-mNGS reported a higher number of false positives and faced limitations in detecting Aspergillus spp. In conclusion, the study highlights the potential of rt-mNGS as a powerful tool in clinical diagnostics of respiratory infections and beyond.

The online version contains supplementary material available at 10.1007/s15010-025-02665-y.