Next-generation sequencing: Driving a paradigm shift from pathogen detection to systems-level analysis

Abstract

The advent of advanced molecular technologies, particularly the widespread use of next-generation sequencing (NGS), has revolutionized the life sciences research landscape. NGS, characterized by high throughput, specificity, and sensitivity, offers a superior alternative to traditional detection methods, particularly in the context of viral pandemics and emerging infectious diseases. It has proven indispensable not only in identifying novel therapeutic targets but also in monitoring viral resistance. This review provides a comprehensive analysis of NGS applications in infectious diseases and pathogenic microorganisms, with an emphasis on its role in rapid pathogen detection, accurate identification, interspecies transmission tracking, and resistance profiling. Through the integration of these insights, the review offers a forward-looking perspective on emerging trends and challenges, highlighting the transformative impact of these technologies on the understanding and management of infectious diseases.

1. Introduction

The persistent and evolving threat of infectious diseases to global public health necessitates continuous advancements in scientific capabilities for pathogen detection, analysis, and management. For decades, traditional methods such as cell culture and Sanger sequencing (SS) have formed the cornerstone of virology and microbiology research, despite their inherent simplicity. However, these techniques are limited in terms of sensitivity, throughput, and scope of application. The 21st century has ushered in a new era in molecular biology, primarily driven by next-generation sequencing (NGS) technology. This groundbreaking advancement has significantly reduced sequencing costs while exponentially increasing data output, making genomics more accessible and establishing it as an indispensable tool in life sciences research.¹

NGS generates vast amounts of genetic data from diverse samples, fundamentally transforming pathogen detection, monitoring, and analysis. In contrast to traditional culture-based methods, NGS offers a much higher detection rate, particularly when identifying slow-growing or non-culturable microorganisms.² Additionally, NGS enables the examination of specific pathogens’ genomic information, facilitating the tracking of epidemic transmission routes.^3,4 Moreover, genomic analysis of pathogens can uncover new drug targets, providing critical insights that inform timely adjustments to treatment strategies.^5,6

This review investigates the wide-ranging applications of advanced molecular technologies in understanding pathogen evolution and transmission dynamics. It highlights the transformative role of NGS in pathogen detection and surveillance.

2. NGS redefining pathogen detection and identification

The ongoing integration of NGS into clinical diagnostics is driving a paradigm shift, radically altering the methods used to diagnose and monitor infectious diseases. NGS offers advantages unmatched by conventional testing approaches, including enhanced sensitivity, higher resolution, and the ability to identify pathogens without prior assumptions, enabling comprehensive analysis of complex microbial communities (Fig. 1).

Fig. 1.

The transformative power of NGS in pathogen detection and surveillance. Created in BioRender. peng, Y. (2026). https://BioRender.com/9cfazn1.

Abbreviation: NGS, next-generation sequencing.

2.1. Enhancing the speed and accuracy of pathogen identification

Historically, microbial culture was considered the gold standard for pathogen identification. However, this method is time-consuming, and its sensitivity is limited when pathogens are of low abundance or have specialized growth requirements. The advent of PCR technology and SS represented significant advancements in diagnostic speed and sensitivity. However, these targeted approaches require prior knowledge of the pathogen’s genetic sequence, limiting their effectiveness in cases involving unknown pathogens.

NGS addresses the limitations of traditional methods by providing a comprehensive, unbiased representation of all genomic material present in a sample. It enables the simultaneous detection and analysis of all microorganisms—viruses, bacteria, fungi, and parasites—without the need for prior assumptions about the pathogen. This capability is particularly critical in clinical settings, where rapid and accurate diagnoses are essential for saving lives. For example, in cases of severe and unexplained illnesses, metagenomic NGS (mNGS) can sequence all genetic material from samples such as blood or cerebrospinal fluid. In a clinical study on patients with blood disorders, nanopore-targeted sequencing achieved a pathogen detection rate of 90%, whereas blood and anal swab cultures detected pathogens in only 32.6% and 14.6% of cases, respectively. Based on nanopore targeted sequencing results, clinicians could quickly adjust antibiotic treatments, significantly improve patient outcomes and reducing infection-related mortality to zero.⁷ A retrospective study evaluating the diagnostic performance of SS and NGS in epidermolysis bullosa demonstrated that NGS improved diagnostic accuracy from 89% (SS) to 95%, while significantly reducing turnaround time from 211 days to 39 days (p < 0.001). Furthermore, the rate of identifying variants of uncertain significance and other findings rose from 5% and 1% (SS) to 22% and 13% (NGS), respectively.⁸ In urinary tract infection diagnostics, targeted NGS (tNGS) reduced detection time to approximately 12.89 hours, compared to 61.48 hours for traditional culture, facilitating rapid diagnosis.⁹ Notably, tNGS identified polymicrobial infections in 55.4% of samples negative by traditional culture, outperforming mNGS (27.7%). For COVID-19 with concurrent lower respiratory tract infection, mNGS demonstrated superior sensitivity (95.35%) compared to traditional culture (81.08%).¹⁰ Additionally, NGS has proven invaluable in diagnosing complex infections, such as liver damage and intracranial infections caused by typhus, where traditional methods often fall short.¹¹ In veterinary medicine, high-throughput sequencing (HTS) plays a pivotal role in monitoring zoonotic diseases. The detailed data generated by HTS allow for the identification of known, novel, and emerging pathogens across various animal species, including cattle, dogs, sheep, pigs, horses, chickens, and ducks.¹² For example, NGS sequencing of tissue samples from camels with leukemia ruled out bovine leukemia virus as the cause, highlighting NGS’s ability to exclude infectious agents in diseases of unknown origin.¹³

NGS’s higher detection rate compared to traditional culture methods is well-documented in numerous studies, reinforcing its potential as a broad-spectrum pathogen monitoring tool.⁶ This enhanced detection capability aids in understanding the true prevalence and species variation of infectious pathogens across different populations and environments.

2.2. Revolutionizing surveillance and outbreak investigation

Effective public health monitoring is essential for successful infectious disease control, and NGS technology has now become integral to modern surveillance systems. By analyzing the genetic makeup of pathogens, NGS provides comprehensive genomic data that enables rapid and accurate responses to epidemics. It aids in tracking infection sources and transmission routes, offering critical scientific evidence for developing effective infection control strategies.¹⁴ NGS allows governments to monitor the spread of infectious diseases, identify transmission chains, and analyze the dynamics of outbreaks in real time.¹⁵ During the COVID-19 pandemic, NGS was instrumental in tracking the emergence and spread of new SARS-CoV-2 variants. By sequencing viral genomes from patient samples, researchers identified mutations and assessed their impact on transmission, infection severity, and vaccine efficacy. In Colombia, NGS was used to analyze SARS-CoV-2 mutations during the pandemic’s first year, uncovering new circulating lineages and key mutations like E484K and D614G.¹⁶ Moreover, NGS outperforms traditional methods in monitoring the diversity of viral variations in wastewater, with sequencing covering over 99% of the SARS-CoV-2 genome.¹⁷ Such high-resolution genomic monitoring is unattainable with conventional techniques.

To facilitate the rapid sharing and analysis of genomic data, several global platforms have been established. The collaborative management platform for detection and analyses of (re-) emerging and foodborne outbreaks project, for example, aims to create a global platform for pathogen data exchange based on sequencing. These platforms are vital during disease outbreaks, playing a pivotal role in the swift detection and containment of pathogens.¹⁸ National monitoring networks are also advancing the integration of NGS capabilities. A survey by the National Animal Health Laboratory Network in the USA revealed an increasing adoption of NGS, with 18 of 30 laboratories (60%) reporting sequencing capabilities.¹⁹ In one study, NGS technology was used to detect 28 RNA viruses, 13 DNA viruses, 16 bacteria, and 4 protozoa in ticks collected from the blood of various animals.²⁰ This research, conducted in collaboration with nine Asian countries, aimed to prevent the spread of tick-borne diseases. A study conducted from mid-2022 to mid-2023 assessed pathogen genomic surveillance in South and Southeast Asia, highlighting that national health service capacity is a critical factor in local preparedness for emerging infectious disease outbreaks.²¹ These findings confirm NGS’s utility as a powerful tool for large-scale pathogen monitoring across diverse geographic regions and ecosystems.

2.3. Practical applications of different NGS

The choice of NGS method is dictated by the specific clinical or research objective, the type of sample, and the depth of information required (Table 1). mNGS is particularly useful for detecting pathogens in various infectious body fluids, including pleural effusion, ascites, and synovial fluid. Even in cases where traditional culture or PCR results are negative, mNGS can still yield positive results in over half of the samples. When compared to a composite gold standard based on clinical judgment, culture, and PCR testing, the sensitivity and specificity for bacterial detection ranged from 81%–86% and 91%–95%, respectively, while for fungal detection, they were 63%–70% and 92%–96%, respectively. PCR and mNGS testing were consistent for 6 of the 8 culture-negative body fluid samples, but mNGS identified two additional occult pathogens.²² mNGS is also effective in detecting unculturable and hard-to-diagnose microorganisms. A seven-year clinical study²³ demonstrated that mNGS is the most sensitive method for pathogen detection in patients with severe, diagnostically challenging central nervous system infections, with an overall sensitivity of 63.1% and specificity of 99.6%. The test detected mycobacteria and fungi that can take weeks or months to grow in culture, including Histoplasma capsulatum, Mycobacterium tuberculosis, Aspergillus sp., Rhizopus sp., and Coccidioides sp.²⁴ Both mNGS and tNGS exhibit high diagnostic accuracy, with tNGS potentially offering better specificity than mNGS. A meta-analysis reported pooled sensitivity and specificity values for diagnosing periprosthetic joint infections of 0.89 (95% confidence interval [CI]: 0.84–0.93) and 0.92 (95% CI: 0.89–0.95) for mNGS, and 0.84 (95% CI: 0.74–0.91) and 0.97 (95% CI: 0.88–0.99) for tNGS.²⁵ Furthermore, tNGS enhances detection depth and the signal-to-noise ratio by enriching target regions, making it more sensitive to low-biomass pathogens, while also simplifying bioinformatics analysis and reducing costs. Multiplex PCR-based tNGS and hybrid capture-based tNGS, targeting 198 and 3060 pathogens (co-detection of DNA and RNA), have been established. Multiplex PCR-based tNGS and hybrid capture-based tNGS took 10.3 and 16 hours, respectively, with low sequencing data sizes of 0.1 M and 1 M reads, and test costs were reduced to a quarter and half of those of mNGS.²⁶

Table 1.

Different application strategies based on NGS.

Items	Core strategy	Key advantages	Primary applications
mNGS	Untargeted sequencing of all nucleic acids in a sample	Hypothesis-free Broad-spectrum	Diagnosis of unexplained infections, microbiome studies, environmental surveillance23,24
tNGS	Sequencing of specific regions enriched by probes or PCR	High Sensitivity and Specificity	Detection of known pathogens/genes26
fNGS	Preferential retention of microbial nucleic acids via host depletion	Improved Signal-to-Noise	Clinical infection diagnosis from sterile sites
WGS	Complete sequencing of a single organism’s genome	Highest Resolution	Outbreak, high-precision strain typing, detailed mechanistic studies28,29
RNA-seq	Sequencing of RNA transcripts to assess gene expression	Functional Insight	Transcriptomic studies, host-pathogen interactions, biomarker discovery30

Abbreviations: NGS, next-generation sequencing; mNGS, metagenomic NGS; tNGS, targeted NGS; fNGS, focused NGS; PCR, polymerase chain reaction; WGS, whole genome sequencing; RNA-seq, RNA sequencing.

Whole genome sequencing (WGS) aims to sequence the entire genomic DNA of an organism, providing the most complete genetic blueprint. WGS approaches can comprehensively explore all types of genomic alterations in cancer, offering insights into the landscape of driver mutations and mutational signatures in cancer genomes. Analysis of WGS data from malignant melanomas revealed somatic mutations in the telomerase reverse transcriptase gene promoter in 17 out of 19 (89%) cases examined.²⁷ Statistical analysis of non-coding somatic mutations using WGS datasets from the cancer genome atlas and international cancer genome consortium revealed frequent mutations in several non-coding regions.²⁸ Additionally, WGS can identify genomic integrations of pathogens into the host human genome. A massively parallel sequencing study showed that HBV integration was observed more frequently in tumors (86.4%) compared to adjacent liver tissues (30.7%).²⁹

RNA sequencing (RNA-seq) is instrumental in studying host-pathogen interactions and gene expression profiles, aiding in the identification of biomarkers and therapeutic targets. The rapid advancement of sequencing technologies has led to the development of specialized RNA-seq approaches tailored to address diverse molecular biology questions. Single-cell RNA-seq (scRNA-seq) reveals the heterogeneity within cell populations, while BacDrop, a highly scalable method for bacterial scRNA-seq, captures cellular states undetectable by bulk RNA-seq. Long-read RNA-seq has generated over 427 million long-read sequences from complementary DNA and direct RNA datasets.³⁰ The circRNA identifier using long-read sequencing data protocol, which combines rolling circular reverse transcription with nanopore sequencing, enables the capture of full-length circRNA sequences.³¹ Rapid NGS technologies, using host DNA depletion methods, enhance microbial signal detection, improving sensitivity in low-biomass sterile environments.³²

In summary, for unknown or complex infections, mNGS is the preferred approach, while for rapid, highly sensitive detection of known pathogens, tNGS is more cost-effective and operationally convenient. The integration of WGS and RNA-seq facilitates multi-omics analysis, enabling comprehensive studies of cancer genomics and gene fusions. scRNA-seq remains a fundamental tool for gene expression and regulation research.

The integration of NGS technology into pathogen detection and surveillance represents a transformative advancement. Its high throughput, sensitivity, and specificity have revolutionized clinical diagnostics, as well as epidemic investigations and pathogen monitoring. As the technology continues to evolve and gain broader adoption, NGS is poised to play an even more significant role in global clinical practice and public health monitoring, offering increased support for future advancements.

2.4. NGS unraveling pathogen dynamics

NGS offers a powerful tool for observing pathogen evolution, providing higher resolution and sensitivity than traditional methods. Unlike techniques such as SS, which are constrained by throughput, NGS enables the detection of subtle genetic variations and facilitates the study of pathogen adaptation to environmental changes. This technology is particularly valuable for investigating cross-species transmission, as it allows for the tracking of genetic alterations across hosts, offering insights into viral evolution and transmission dynamics.

2.5. The origins and spread of viral outbreaks

HTS of viral genomes enables the reconstruction of transmission chains (Fig. 2A). By analyzing viral genome data across different regions and time points, researchers have constructed phylogenetic trees that highlight the occurrence of superspreading events during the early stages of the pandemic. These trees also offer insights into the geographic transmission routes of the virus.^33,34 In Italy, genomic surveillance revealed that SARS-CoV-2 circulation likely stemmed from multiple introductions from China and Germany.³⁵ In regions such as the USA, France, and Thailand, viral genome analysis has been used to estimate viral transmission timelines, helping to infer early infection events.^36,37 Ongoing global sequencing of SARS-CoV-2 enables real-time monitoring of the virus’s evolution and mutation occurrences. For example, the clinical and genomic characteristics of the Omicron BA.5 variant were mapped, revealing specific mutations associated with enhanced environmental transmission and immune evasion.³⁸ Additionally, NGS demonstrated that the genome of SARS-CoV-2 in bats shares approximately 96% similarity with the RaTG13 coronavirus, supporting the hypothesis that bats may be the virus’s origin.³⁷ These findings highlight the importance of continuous global virus surveillance and open new avenues for investigating viral origins. Beyond SARS-CoV-2, NGS has been employed for time-series sequencing of hantavirus genomes across various regions.²⁴ During the Ebola virus disease outbreak in the Democratic Republic of the Congo (2018–2020), NGS played a critical role in tracking the virus’s transmission chain and elucidating its dynamic transmission patterns within communities.²⁵ Unlike SS, NGS provides a comprehensive view of viral populations within the host. By comparing viral alleles across different patients, researchers can pinpoint sources of infection and track transmission routes within healthcare settings, thereby informing targeted infection control strategies.²⁶ This information is vital for implementing focused interventions, such as rodent control in high-risk areas, to significantly reduce virus transmission.

Fig. 2.

Unraveling viral evolution and cross-species transmission through genomic analysis. Created in BioRender. peng, Y. (2026). https://BioRender.com/9cfazn1.

2.6. Genomic recombination and cross-species transmission

Cross-species transmission has been a fundamental driver of many disease pandemics throughout human history. When a host is concurrently infected by two different viral strains, viral genomic recombination—a key mechanism of viral evolution—occurs (Fig. 2B). For example, this is common with influenza and rotaviruses. During replication within the host, viral genomes are fragmented and recombined, resulting in the production of new viral offspring that possess a combination of genetic material from both viruses. This recombination can endow the new virus with altered characteristics. When viruses cross species barriers, they may overcome the immune system limitations of the new host through gene amplification or mutation. This adaptive evolution enables the virus to replicate more efficiently and spread more effectively in the new host. Using NGS technology, researchers recently identified a new, relatively rare G9P rotavirus strain, which emerged through genetic recombination among different circulating rotavirus genotype.¹ This finding highlights the importance of continuous genomic surveillance, as it allows for the early detection of emerging strains that may exhibit increased pathogenicity.

Compared to other research methods, the primary advantages of NGS lie in its high throughput and resolution. Traditional sequencing techniques are typically limited to analyzing small fragments of the viral genome, whereas NGS can simultaneously sequence millions of viral genomes, yielding far more comprehensive genomic data.³⁹ For instance, a study on the whole-genome sequencing of gout related to pebble pain identified a new strain, the goose star virus AStV-SDPY. The nucleotide homology between this virus and other classic avian strains ranged from 48.8% to 68.2%, placing it in a distinct branch of avian astroviruses. Subsequent outbreaks in a duck farm linked star viruses isolated from the infected ducks to the AStV-SDPY branch, with nucleotide homology between the two strains reaching 97.2% to 98.8%.⁴⁰ These findings suggest that the virus may be spreading across species among domestic waterfowl. Similarly, avian influenza virus, with the potential to spread to mammals, including humans, was studied in the Middle East using NGS to analyze the H9N2 strain’s genetic composition.⁴¹ Deep sequencing revealed significant heterogeneity in key amino acid positions in the hemagglutinin protein, affecting the virus’s ability to bind to receptors in both birds and mammals. This mammalian-like feature further emphasizes the zoonotic potential of these avian viruses. NGS-based research on bat viromes has identified viruses with zoonotic potential, showing that from the perspective of bat viruses, humans represent another host species (Fig. 2C). Cross-species transmission is largely driven by ecological and behavioral factors rather than biological constraints.⁴² NGS applications extend beyond animal viruses, significantly advancing the diagnosis and understanding of plant viruses. HTS of plant samples has revealed valuable insights into the diversity and evolution of plant viruses, playing a critical role in crop protection and food security.⁴³

NGS technologies can be categorized into short-read sequencing and long-read sequencing based on read length and underlying principles, with the two approaches offering complementary advantages. Short-read NGS platforms, represented by Illumina, are the most widely used in clinical applications. Their core advantages lie in high throughput, high accuracy, and relatively low cost, making them particularly suitable for large-scale, deep-coverage genome, exome, or transcriptome sequencing.⁴⁴ PacBio utilizes single-molecule real-time technology for sequencing. Compared to Illumina, the long reads (HiFi Reads) generated by PacBio, as well as Oxford Nanopore Technologies, can better resolve repetitive sequences and complex genomic regions, which is especially important for pathogens with highly repetitive or variable genomes.^45,46 Compared to other platforms, Nanopore devices are typically more compact and suitable for field applications, making them ideal for rapid monitoring of pathogen transmission in epidemiological studies.^47,48 The Ion Torrent sequencing platform, based on semiconductor technology, is well-suited for small-scale, rapid analyses and demonstrates superior performance in detecting localized mutations and analyzing differences between viral strains.^49,50 The BGISEQ platform, developed by BGI in China, offers high throughput and fast sequencing speed, making it applicable for efficient pathogen screening, particularly in large-scale epidemic surveillance and during disease outbreaks.^51,52

In summary, NGS facilitates the detection of viral evolution and provides in-depth genomic data. These detailed sequencing results are instrumental in accurately tracking outbreaks, analyzing genetic recombination events, and identifying cross-species transmission, thereby enabling proactive prediction, prevention, and response to emerging viral threats.

3. NGS guiding interventions

Moreover, NGS and other advanced molecular technologies are not only transforming pathogen identification but also playing pivotal roles in drug discovery and antibiotic resistance monitoring. These technologies offer detailed genomic and molecular insights that are accelerating the development of novel therapies.

3.1. NGS as a catalyst for novel drug and vaccine development

The discovery of new drugs relies on identifying pathogen targets that can be inhibited by drug molecules. Through NGS, the genetic variations, virulence factors, and resistance mechanisms of pathogens are revealed, providing a theoretical foundation for screening new drug and vaccine targets. By sequencing the entire genome of pathogens (Fig. 3A), drugs can be designed to target key microbial processes while minimizing off-target effects on the host.³ In the context of HIV treatment, tNGS technology enables the detection of resistance to integrase strand transfer inhibitors and protease inhibitors, thus informing individualized therapy. This precise monitoring of drug resistance not only optimizes the use of existing drugs but also aids in the development of next-generation antiviral therapies targeting resistance mutations.^53,54 Beyond small-molecule drugs, NGS plays a critical role in vaccine development (Fig. 3B). By sequencing the genomes of diverse pathogen strains, researchers can identify conserved antigens that are less likely to mutate, making them ideal candidates for broadly protective vaccines. NGS-based methods also enable the detection of virulence mutations in vaccine batches, such as the identification of the neurovirulent variant 472-C in the poliovirus genome from the oral poliovirus vaccine.⁵⁵ Additionally, a detailed understanding of the host immune response to infection, gained through transcriptomic studies of infected tissues, can guide the design of vaccines that elicit the most effective immunity. RNA-seq allows researchers to investigate whether differences in immune responses to pathogens or candidate vaccines are due to altered expression of coding genes or if “unseen” regions of the genome are involved in regulating these responses.⁵⁶

Fig. 3.

NGS guiding interventions. Created in BioRender. peng, Y. (2026).https://BioRender.com/9cfazn1.

Abbreviations: tNGS, targeted next-generation sequencing; mNGS, metagenomic next-generation sequencing.

Phage immunoprecipitation sequencing is a high-throughput serological detection method primarily used to assess antibody status in serum. In this approach, a specific phage library is prepared and incubated with the patient’s serum. During this incubation, antibodies in the serum bind to phages displaying corresponding pathogenic peptides. Sequencing is then performed on the phages bound to the antibodies, allowing for the detection of antibody characteristics against various infectious pathogens, which can be recorded for further analysis.⁵⁷ Phage immunoprecipitation sequencing has also been used for single-cell analysis of antibody responses to West Nile virus, providing insights at the level of individual B cells and advancing our understanding of humoral immune responses.⁵⁸

3.2. Monitoring and combating antimicrobial resistance

The emergence of antibiotic resistance represents a critical challenge currently facing global health. When pathogens, such as bacteria, develop resistance to commonly used first-line drugs, it can lead to poor treatment outcomes, prolonged patient recovery, and even increased mortality. By sequencing the genomes of clinical isolates, it is possible to accurately identify known resistance-conferring mutations and genes (Fig. 3C). For example, a multicenter study from a high-incidence area of tuberculosis in southeastern China utilized a self-developed targeted amplification sequencing method, the target amplicon sequencing by NGS technology method, combined with NGS technology to detect resistance mutations related to eight anti-tuberculosis drugs.⁵⁹ Similarly, WGS of Mycobacterium tuberculosis in the Philippines has revealed circulating strain types and their corresponding drug resistance mutations, indicating the emergence of mutations associated with newer drugs such as bedaquiline.⁶⁰ NGS technology also has the ability to detect low-frequency mutations and quasi-species that are often missed by conventional methods, which is essential for understanding the development of drug resistance.⁶¹ By analyzing resistance genes to predict antimicrobial resistance, clinicians can select the most effective narrow-spectrum antibiotics, thereby preventing the overuse of broad-spectrum antibiotics. This approach enables researchers to investigate, at the molecular level, how drugs function and the genetic basis of resistance, providing a scientific foundation for personalized treatment plans. A meta-analysis including data from 24 diagnostic accuracy studies covering 23 different tuberculosis drugs or drug groups, including first-line drugs, injectable drugs, and fluoroquinolones, demonstrated that tNGS achieved an overall sensitivity of 94.1% (95% credible interval [CrI]: 90.9–96.3) and an overall specificity of 98.1% (95% CrI: 97.0–98.9) for detecting drug resistance.⁶² These results indicate that tNGS can reliably identify genetic mutations associated with tuberculosis drug resistance, providing genotype resistance results across multiple drugs within days, thus facilitating the faster initiation or adjustment of effective treatment regimens.⁶³ In a case of severe pneumonia, the combined use of mNGS and extracorporeal membrane oxygenation successfully detected co-infection with influenza A virus and methicillin-resistant Staphylococcus aureus, enabling clinicians to promptly adjust antimicrobial therapy within 24 hours based on the mNGS results.⁶⁴

NGS plays a pivotal role not only in bacterial and parasitic infections but also in viral infections. In the case of HIV, for example, reliable statistical methods applied to RNA-seq data can help identify biomarkers linked to the virus and its status, offering a clearer understanding of the host-pathogen interaction.⁶⁵ This also enables the discovery of potential therapeutic targets. The relationship between the host microbiome and infectious pathogens is an emerging research area, with significant implications for both disease susceptibility and treatment. NGS-based microbiome studies have shown that the composition of the microbial community can influence infection outcomes, as seen with human papillomavirus infections. Insights into how the microbiome interacts with pathogens and modulates the host immune response can lead to novel therapeutic strategies, such as using probiotics or prebiotics to restore a healthy microbial balance.⁶⁶ Another notable advantage of NGS in monitoring drug resistance is its capacity to handle large sample volumes and deliver rapid analysis. For example, the tNGS platform for tuberculosis provides drug resistance analysis in just 6 to 9 hours, a stark contrast to the traditional drug sensitivity testing method, which takes 69.5 days.⁶⁷

By enabling rapid, comprehensive, and highly accurate pathogen detection, NGS allows clinicians to develop and implement precisely targeted antimicrobial treatment strategies. This capability facilitates timely infection control, reduces the incidence of drug-related adverse events, shortens hospital stays, and shows clear potential in decreasing mortality among critically ill patients.

4. NGS: frontiers and future directions

The integration of NGS with other advanced technologies has accelerated the convergence of scientific research and clinical practice. This synergy allows researchers to address complex biological challenges more efficiently.

4.1. Integration of NGS with advanced molecular technologies

NGS-generated genomic or transcriptomic data can be utilized to construct customized protein databases for mass spectrometry analysis. MALDI-TOF mass spectrometry, with its ability to simultaneously detect major SARS-CoV-2 variants such as Alpha, Beta, Delta, and Omicron, plays a pivotal role in pathogen identification and mutation detection.⁶⁸ Its rapid analysis and low sample consumption have made it an essential tool for microbial identification and mutation monitoring. By integrating NGS with mass spectrometry, researchers can leverage the complementary strengths of both technologies, enabling efficient and accurate detection of pathogen variants. In tuberculosis drug resistance testing, the combination of NGS and mass spectrometry facilitates the simultaneous detection of multiple gene mutations linked to resistance. These mutations can then be verified at the protein level, providing a foundation for designing personalized treatment plans.⁶⁹

The fusion of the clustered regularly interspaced short palindromic repeats/CRISPR-associated systems (CRISPR-Cas) system with NGS technology enables in-depth analysis of precisely targeted gene loci. One of the most prominent applications is whole-genome CRISPR screening, where researchers construct a genome-wide gRNA library and introduce it into a pool of cells via lentivirus. After applying selective pressure (such as drug treatment), NGS is used to detect gRNA enrichment or depletion, thereby identifying key genes associated with specific phenotypes.⁷⁰ This combination has also spurred the development of single-molecule detection technologies. Single-molecule sequencing can directly observe the dynamic behavior of DNA or RNA molecules, while NGS supports large-scale data generation and analysis. For example, multiplexed single-molecule characterization methods combine single-molecule fluorescence microscopy with NGS to analyze the dynamic properties of numerous DNA sequences and uncover the complex mechanisms of Cas9 protein binding to target DNA.⁷¹ Additionally, this integration can identify new genetic markers of virulence, which can then be utilized to develop CRISPR-based vector control tools, such as gene drive technology, to reduce the mosquito population spreading malaria or to prevent mosquitoes from transmitting parasites. While deploying these advanced technologies in low- and middle-income countries presents challenges, their potential to strengthen local diagnostic and monitoring systems is substantial.⁷²

Metagenomics, which sequences all microbial DNA in a sample, maps the species composition and functional potential of microbial communities. However, DNA-level detection cannot differentiate between active and dead microbes, nor can it fully capture the true transcriptional activity of pathogens during infection. Metatranscriptomics, by sequencing microbial RNA, directly captures the gene expression profiles of active pathogens, revealing dynamic changes in host-pathogen interactions. In patients with melanoma undergoing immunotherapy, 16S rRNA gene sequencing and shotgun metagenomic sequencing (n = 27) were used to characterize the gut microbiome in pre-treatment fecal samples. Microbial species associated with progression-free survival were first identified through metagenomics, and the transcriptional expression of metagenomic pathways was confirmed in 17 subsets using metatranscriptomics.⁷³ A study analyzing the fecal microbiome of 1067 patients with gastrointestinal symptoms demonstrated that combining metagenomics and metatranscriptomics improves the diagnosis of infectious intestinal diseases.⁷⁴ This integrated approach directly links microbial metabolic activity to clinical outcomes, providing more precise guidance for identifying therapeutic targets.

4.2. Integration of NGS with artificial intelligence and machine learning

Artificial intelligence (AI) and machine learning (ML) are significantly reshaping the analytical approach to NGS data. Traditional bioinformatics workflows often depend on predefined thresholds and rules, which limits the ability to fully explore complex patterns in large genomic datasets. AI/ML algorithms, however, have demonstrated remarkable potential in pathogen detection, mutation prediction, and modeling epidemic dynamics.⁷⁵ For example, the Classification Perception-Combination Inference Network has improved pathogen identification in metagenomic sequencing.⁷⁶ SetBERT, leveraging deep learning pretraining models, processes entire sequencing datasets to generate contextualized microbial embeddings, achieving genus-level classification accuracy of up to 95%.⁷⁷ In the future, with the accumulation of multimodal data—spanning genomics, transcriptomics, proteomics, and clinical phenotypes—AI/ML will become the central engine for integrative analysis, propelling infectious disease research toward predictive and precision-based approaches.

4.3. Transformative potential of real-time sequencing technologies

Third-generation sequencing technologies, particularly nanopore sequencing, are shifting diagnostics from centralized laboratories to on-site, real-time testing. Oxford nanopore technologies platforms, with their miniaturized, portable devices, enable real-time sequencing in resource-limited settings or at outbreak sites, drastically reducing the time from sample collection to result generation.⁷⁸ These technologies’ long-read capabilities facilitate the analysis of complex structural variations in genomes, such as recombination breakpoints and repetitive sequences, which are especially important in studying viral cross-species transmission and adaptive evolution.⁷⁹ By integrating real-time sequencing with cloud-based data analysis platforms, scientists worldwide can monitor pathogen mutation dynamics nearly simultaneously, enabling rapid adjustments to vaccine and treatment strategies.

In conclusion, the integration of NGS with other molecular biology techniques provides a powerful and effective tool for studying and diagnosing infectious diseases. However, overcoming technical, ethical, and logistical challenges remains crucial to fully realizing the potential of these innovations. As these technologies continue to advance, they will play a pivotal role in transforming clinical practice and improving global health outcomes.

5. Limitations of NGS technology

While NGS technology has revolutionized genomic research and clinical diagnostics, several limitations have emerged as its applications expand. Challenges persist in transitioning from laboratory research to clinical application, including the complexity of the technology, the lack of standardized data analysis protocols, balancing cost and benefit, and integrating NGS with clinical decision support systems.

5.1. Sequencing error rate and chimeric reads

Although NGS technology has seen significant advancements and its error rate has been notably reduced, some sequencing errors persist, particularly in regions with low coverage or high guanine-cytosine content. These errors can lead to false positives or negatives, potentially affecting clinical decisions. High error rates directly limit NGS’s ability to detect low-frequency variants. Traditional NGS struggles to identify rare variants with frequencies below 1%, as its error rate typically ranges from 0.1% to 1%. Chimeric reads, which occur due to DNA heterogeneity or recombination between different samples during sequencing, are another challenge. DNA in formalin-fixed paraffin-embedded samples is particularly prone to damage from cross-linking and prolonged storage, resulting in numerous artifact chimeric reads.⁸⁰ Additionally, inverted repeat sequences or palindromic sequences in the genome can lead to chimeric reads during library preparation, caused by mechanisms such as partial single-strand molecule pairing. To mitigate these limitations, several experimental and computational strategies have been developed. Molecular consensus sequencing, combined with unique molecular identifiers, has become the gold standard for improving sensitivity and specificity, reducing error rates to very low levels. This approach is particularly beneficial for circulating tumor DNA analysis, viral monitoring, and basic research.⁸¹

5.2. Host DNA background interference

Clinical samples, such as blood, cerebrospinal fluid, and bronchoalveolar lavage fluid, often contain abundant human host cells and DNA, while the target microorganisms or specific genomes are typically present in much lower quantities. This significant disparity can severely affect sequencing sensitivity and accuracy, making it challenging to detect low-abundance pathogens or specific sequences effectively. To overcome this issue, researchers and clinicians may rely on specialized preprocessing techniques to improve pathogen or tumor DNA detection sensitivity. A novel filtration device based on zwitterionic interface self-assembly coating has been developed, capable of removing over 99% of white blood cells from various blood volumes while allowing bacteria and viruses to pass through unimpeded. After filtration, the detection of pathogens is enhanced, with microbial counts averaging more than ten times higher than in unfiltered samples.⁸² Another human-cell-specific filtration membrane can achieve over 98% removal of host DNA, enriching pathogen reads by 6 to 8 times.⁸³ Additionally, methylation-binding proteins, which exploit the differences in cytosine-phosphate-guanine methylation between human and bacterial DNA, can be used to selectively remove methylated host DNA, though this may also affect certain methylated pathogen DNA.⁷¹ Furthermore, selecting the appropriate sequencing method based on sample type can further improve detection rates. For example, tNGS technology used for detecting Yersinia pestis MLVA loci has shown 100% accuracy in genotyping, even when conventional whole-genome sequencing fails.⁸⁴

5.3. Variability in bioinformatics pipelines and its impact on results

NGS data processing relies heavily on bioinformatics pipelines, and variability in results can arise from differences in laboratories, sequencing platforms, and software tools. In addition to positive results, such as mutations or pathogen detection, NGS data also yield negative results, which pose significant challenges for clinical interpretation. Misinterpretations or reporting errors can have catastrophic consequences for patients and their families. In cancer genetics, for example, incorrect interpretations may lead to unnecessary surgeries or inappropriate screening strategies.⁸⁵ The American College of Medical Genetics and Genomics and the Association for Molecular Pathology have issued consensus guidelines for the interpretation of clinical variants.³² However, discrepancies often arise in how different laboratories interpret and classify variants based on these guidelines, as well as in the availability of detailed clinical information. Regular re-evaluation of variants, the use of updated resources, collaborative efforts to accurately store information in public databases, and physician education on interpreting reported variants in conjunction with patient phenotypes are critical for ensuring accurate classification.

The large volumes of data generated by NGS require specialized bioinformatics expertise and robust computational infrastructure, which pose significant challenges for small and medium-sized clinical laboratories with limited resources. To lower the analytical barrier, several commercial bioinformatics platforms have emerged, such as Illumina’s BaseSpace and Thermo Fisher’s Ion Reporter, offering comprehensive analysis solutions from raw data to clinical reporting.⁵³ However, these commercial tools have limitations in terms of adaptability and scalability when handling complex samples or addressing specialized application scenarios. In response, open-source bioinformatics workflows, such as the nf-core series, have gained popularity, providing laboratories with flexible, reproducible, and community-supported solutions.^86,87 Additionally, the establishment of national data-sharing platforms, like the National Center for Biotechnology Information’s sequence read archive and the European Nucleotide Archive, along with pathogen-specific databases such as Global Initiative on Sharing All Influenza Data and Bacterial and Viral Bioinformatics Resource Center, has greatly facilitated global data standardization and comparative analysis.

5.4. Cost-effectiveness and clinical utility

The cost of sequencing remains another key factor. Developing more stable and cost-effective sequencing methods is essential. For example, a small RNA virus sequencing protocol combining random sequencing and barcode reuse has made these technologies more accessible, particularly in regions where NGS has not been widely adopted.⁵⁵ In countries with limited access to NGS technology, relying on older yet reliable methods like SS for screening SARS-CoV-2 variants can be a more practical and cost-effective approach.⁵⁶

5.5. Clinical decision integration and validation studies

Effectively integrating NGS results into existing clinical decision-making processes remains a core challenge in its translational application. While NGS provides detailed genomic insights into pathogens, its clinical relevance and direct impact on treatment outcomes still require more high-quality evidence. For instance, in the diagnosis of tuberculosis in children,⁵⁸ plasma cell-free DNA sequencing, as a non-invasive method, improves microbiological confirmation rates, but its consistency with sputum testing remains suboptimal, and its role in the diagnostic process requires further optimization. Similarly, in oncology, while NGS is widely used to guide personalized treatment, robust evidence from randomized controlled trials regarding its survival benefits and overall impact on healthcare systems remains insufficient.^54,59 To address these gaps, several prospective clinical studies, such as the USA National Cancer Institute—Molecular Analysis for Therapy Choice trial and the UK 100,000 Genomes Project, are underway to systematically assess the real-world utility of NGS-guided treatment.^86,88

5.6. Privacy issues and global ethical frameworks

Shared data often contains raw information related to patients, which poses a potential risk of individual re-identification. Additionally, pathogen genomic data may reveal disease burdens specific to certain populations or regions, raising concerns about equity and stigmatization. To address these challenges, the international community is working to establish an ethical framework that balances data openness with privacy protection. The United Nations Educational, Scientific and Cultural Organization and the World Health Organization have respectively released ethical guidelines for genomic data sharing, emphasizing core principles such as informed consent, data de-identification, fair benefit-sharing, and transparent governance.^89,90 Privacy-enhancing technologies, including differential privacy, federated learning, and secure multi-party computation, are being explored for cross-institutional genomic data analysis, enabling collaborative modeling without the need to share raw data.^49,91, 92, 93

6. Conclusions

The rapid advancement of NGS technology has made its application increasingly common in genomics, clinical diagnostics, and biomedical research. NGS generates high-resolution, genome-level data, which enhances diagnostic accuracy and broadens its scope, while also offering new insights into the mechanisms driving pathogen adaptation and evolution. This article explores the profound impact of NGS and its integration with other innovative platforms. Timely and accurate pathogen identification is essential for providing a theoretical foundation for public health interventions. NGS plays a pivotal role in analyzing the genetic mechanisms behind pathogen recombination and cross-species transmission, enhancing our understanding of emerging pathogens and enabling the assessment of potential zoonotic spillover risks.

The impact of these technologies extends across the entire drug and vaccine development process. The growing problem of antibiotic resistance highlights the importance of NGS in rapidly and accurately identifying resistance mutations, ensuring the continued efficacy of existing drugs, and guiding the development of new antibacterial agents. The integration of NGS with mass spectrometry and CRISPR-based systems promises to deliver faster, more sensitive, and accessible tools across various application scenarios. However, applying these powerful technologies in clinical and public health practice still faces numerous challenges. Key issues include cost, data analysis complexities, and the need for more robust verification research. Addressing these barriers is essential to ensure the equitable global adoption of these innovations.

CRediT authorship contribution statement

Yu Peng: Writing – review & editing, Writing – original draft, Visualization, Supervision, Project administration, Conceptualization. Yupu Wang: Writing – original draft, Project administration, Conceptualization. Liwen Liu: Writing – review & editing, Supervision. Yanyue Liu: Visualization. Yan Yu: Visualization. Ranran Sun: Writing – review & editing, Writing – original draft. Zujiang Yu: Writing – review & editing, Supervision.

Informed consent

Not applicable.

Organ donation

Not applicable.

Ethics statement

Not applicable.

Data availability statement

Data sharing is not applicable to this article as no data sets were generated or analyzed.

Animal treatment

Not applicable.

Generative AI

Not applicable.

Funding

None.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.