Sequencing Showdown: Two DNA Tests Match Up in the Race to Diagnose Tuberculosis

Tuberculosis remains one of the deadliest infectious diseases on the planet, and yet confirming that a patient’s cough and lung lesions are actually caused by Mycobacterium tuberculosis can still take days or weeks with conventional tools. A new real-world study from China now offers clinicians a clearer picture of how two of the most advanced molecular diagnostics, targeted next-generation sequencing and metagenomic next-generation sequencing, stack up against each other when the stakes are highest. The research, published in BMC Infectious Diseases by a team at Yan’an University Affiliated Hospital in Shaanxi province, suggests that the two technologies are far closer in diagnostic power than their price tags might imply, and that they may work best as complements rather than competitors.

The two sequencing approaches represent different philosophies for reading genetic material from a patient sample. Targeted next-generation sequencing, often abbreviated tNGS, uses panels of primers or probes to enrich and amplify DNA from a predefined list of pathogens, which concentrates sequencing capacity on organisms that clinicians already suspect. Metagenomic next-generation sequencing, or mNGS, takes the opposite approach: it sequences all nucleic acid in a sample without any preselection, allowing the laboratory to detect virtually any microbe present, including unexpected or fastidious organisms that no one thought to test for. That breadth comes at a cost, both financially and computationally, because vast amounts of background human and environmental DNA must be filtered out before a pathogen signal emerges. The question facing hospitals is whether mNGS’s unbounded scope translates into measurably better care for patients with suspected pulmonary tuberculosis.

To answer that question fairly, the researchers confronted a familiar obstacle in observational medicine: patients are not assigned to tests at random. In routine practice, physicians choose mNGS or tNGS based on how sick a patient appears, what previous tests have shown, and what the patient can afford, which means simple comparisons between the two groups can be badly skewed. The team therefore turned to propensity score matching, a statistical technique that pairs patients from each group who share similar baseline characteristics, effectively simulating the balance of a randomized trial. After this matching procedure, the analysis rested on 403 patients with confirmed pulmonary infections, of whom 304 had undergone tNGS and 99 had received mNGS.

The headline diagnostic finding was one of statistical equivalence. In the matched cohorts, the final rate of tuberculosis diagnosis was 17.4 percent in the tNGS group versus 23.2 percent in the mNGS group, a difference that did not reach statistical significance, with a p value of 0.257. Similarly, the proportion of samples in which the Mycobacterium tuberculosis complex was directly detected by sequencing was 20.4 percent for tNGS and 28.3 percent for mNGS, again without a significant difference at p equals 0.134. When the researchers plotted receiver operating characteristic curves to summarize overall diagnostic accuracy, the area under the curve came to 0.936 for tNGS, with a 95 percent confidence interval of 0.898 to 0.975, and 0.967 for mNGS, with a confidence interval of 0.939 to 0.995. The comparison between these two areas yielded a p value of 1.000, about as close to a tie as statistics allows.

Beneath that tie, however, lay one of the study’s most striking results. Within the matched mNGS cohort of 99 patients, metagenomic sequencing achieved 100 percent sensitivity and a 100 percent negative predictive value for tuberculosis, meaning that in this dataset a negative mNGS result reliably ruled the disease out. That figure deserves careful interpretation: in the larger unmatched full cohort of 104 patients who received mNGS, sensitivity slipped to 95.8 percent and the negative predictive value to 98.7 percent, a reminder that perfect performance in a small matched subset can be fragile. Even so, the authors argue that such a high negative predictive value, if confirmed in larger studies, could make mNGS a powerful tool for excluding tuberculosis in diagnostically challenging cases, where every additional day of uncertainty carries clinical and emotional weight.

The sequencing tests were also benchmarked against the established tuberculosis diagnostic arsenal. In subset-specific area-under-the-curve analyses performed after propensity score matching, sequencing targeting the Mycobacterium tuberculosis complex, referred to as MTBC-NGS, outperformed the interferon-gamma release assay, the tuberculosis DNA test, and acid-fast staining, three workhorses of mycobacterial diagnosis that respectively measure immune response, detect bacterial DNA by conventional amplification, and visualize acid-fast bacilli under the microscope. Notably, MTBC-NGS also showed an advantage over the Xpert MTB/RIF assay, the World Health Organization-endorsed rapid molecular test that has transformed tuberculosis detection worldwide, although that advantage was smaller and only became statistically significant after the researchers applied a Bonferroni correction to account for multiple comparisons. The pattern suggests that while newer sequencing platforms do not render existing tools obsolete, they can extract additional diagnostic signal in the right clinical subsets.

Diagnostic performance, however, is only half the story, because healthcare systems must also weigh what each test costs and how it shapes the patient’s journey through the hospital. Here the study delivered a clear verdict: the mNGS group incurred substantially higher healthcare costs than the tNGS group. Metagenomic sequencing’s untargeted design requires deeper sequencing, more intensive bioinformatics, and greater laboratory infrastructure, expenses that are inevitably passed on to patients and payers. Targeted sequencing, by focusing resources on a curated panel of pathogens, achieves comparable diagnostic accuracy for tuberculosis at a lower price point, a distinction that matters enormously in the high-burden settings where tuberculosis testing decisions are made most often.

One widely held assumption did not survive the study’s statistical scrutiny. Earlier comparisons had suggested that patients who received mNGS experienced longer hospital stays, an observation that seemed intuitive given the turnaround time and complexity of untargeted sequencing. But when the researchers adjusted their analysis for the calendar period in which patients were tested, that difference in length of stay lost its statistical significance. The authors conclude that the apparent prolongation of hospitalization may have been attributable in part to temporal changes in clinical practice over the course of the study rather than to the test modality itself. It is a subtle but important methodological lesson: in fast-evolving fields like clinical genomics, when a test was ordered can matter as much as which test was ordered.

The study, led by Xinle Liang and colleagues in the Department of Respiratory and Critical Care Medicine at Yan’an University Affiliated Hospital, was approved by the hospital’s ethics committee and conducted in accordance with the Declaration of Helsinki. The team acknowledged the Xi’an Regional Medical Laboratory Center, which provided both tNGS and mNGS testing services, and the hospital’s clinical laboratory for supporting the work. As a retrospective, single-center analysis, the investigation carries inherent limitations that the authors themselves flag, most notably the modest size of the mNGS group even after matching, and the possibility that unmeasured differences between patients influenced both test selection and outcomes. The 100 percent sensitivity and negative predictive value observed in the matched mNGS cohort, in particular, is explicitly presented as a finding that requires validation in larger studies before it can guide practice.

For clinicians wrestling with suspected pulmonary tuberculosis, the practical message is one of complementarity rather than replacement. Metagenomic sequencing offers unmatched breadth and, in this cohort, an exceptional ability to rule tuberculosis out when results are negative, making it attractive for critically ill or diagnostically perplexing patients in whom the causative pathogen could be almost anything. Targeted sequencing delivers essentially equivalent accuracy for tuberculosis at lower cost, positioning it as the rational first-line genomic option when the clinical question is narrower. As sequencing costs continue to fall and bioinformatics pipelines mature, studies like this one, grounded in real-world propensity-matched cohorts rather than idealized trial conditions, will be essential for deciding which patients truly benefit from the most expensive diagnostic technology and which can be served just as well by its leaner cousin.

Subject of Research: Comparative diagnostic performance of targeted and metagenomic next-generation sequencing for pulmonary tuberculosis

Article Title: Clinical value of targeted next-generation sequencing and metagenomic next-generation sequencing in the diagnosis of pulmonary tuberculosis: a real-world propensity score matching study

Article References: Liang, X., Sun, Y., Sun, J., Zhao, J., He, F., & Bai, Z. (2026). Clinical value of targeted next-generation sequencing and metagenomic next-generation sequencing in the diagnosis of pulmonary tuberculosis: a real-world propensity score matching study. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14537-3

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14537-3

Keywords: pulmonary tuberculosis, targeted next-generation sequencing, metagenomic next-generation sequencing, propensity score matching, Mycobacterium tuberculosis complex, Xpert MTB/RIF, diagnostic accuracy, negative predictive value, healthcare costs, BMC Infectious Diseases, molecular diagnostics, retrospective study