High‐Throughput Metagenomics for Identification of Pathogens in the Clinical Settings

The application of sequencing technology is shifting from research to clinical laboratories owing to rapid technological developments and substantially reduced costs. However, although thousands of microorganisms are known to infect humans, identification of the etiological agents for many diseases remains challenging as only a small proportion of pathogens are identifiable by the current diagnostic methods. These challenges are compounded by the emergence of new pathogens. Hence, metagenomic next‐generation sequencing (mNGS), an agnostic, unbiased, and comprehensive method for detection, and taxonomic characterization of microorganisms, has become an attractive strategy. Although many studies, and cases reports, have confirmed the success of mNGS in improving the diagnosis, treatment, and tracking of infectious diseases, several hurdles must still be overcome. It is, therefore, imperative that practitioners and clinicians understand both the benefits and limitations of mNGS when applying it to clinical practice. Interestingly, the emerging third‐generation sequencing technologies may partially offset the disadvantages of mNGS. In this review, mainly: a) the history of sequencing technology; b) various NGS technologies, common platforms, and workflows for clinical applications; c) the application of NGS in pathogen identification; d) the global expert consensus on NGS‐related methods in clinical applications; and e) challenges associated with diagnostic metagenomics are described.
Sequencing technology is becoming increasingly available in clinic. This review sheds lights on the most commonly used metagenomic next‐generation sequencing (mNGS). History and different platforms, current workflows, and applications of mNGS in pathogens identification, as well as challenges in the diagnostic metagenomics, are discussed. mNGS cannot substitute for traditional methods in the short term, but plays an irreplaceable role in microbiological detection.