Metagenomic next-generation sequencing for the detection of viruses from respiratory samples: a systematized review on current methods and future directions.
5 affiliations
- 1Manipal Institute of Virology, Manipal Academy of Higher Education, Manipal, India.
- 2Manipal Institute of Virology, Manipal Academy of Higher Education, Manipal, India. [email protected].
- 3Nitte (Deemed to be University), Nitte University Centre for Science Education & Research, Department of Infectious Diseases and Microbial Genomics, Mangalore- 575 018, India, Nitte (Deemed to be University), KS Hegde Medical Academy (KSHEMA), Department of Microbiology, Mangalore- 575 018, India.
- 4UK Public Health Rapid Support Team, UK Health Security Agency, and 10 South Colonnade, London E14 4PU, UK.
- 5Manipal Institute of Virology, Manipal Academy of Higher Education, Manipal, India. [email protected].
Abstract
The comprehensive identification of viruses using metagenomic next-generation sequencing (mNGS) has revolutionized the infectious disease diagnosis. Accurate detection of viruses on time is crucial to effectively control the outbreak. This review has meticulously analyzed the use of mNGS to identify viruses from respiratory samples, emphasizing how sample enrichment and amplification methods affect viral detection efficiency and genome coverage. A total of 66 studies were considered to evaluate sequencing workflows and their alignment with routine molecular techniques. Among these studies, the most frequently used method to enrich viruses was centrifugation (50%), followed by enzymatic treatment (45%). Sequence-independent single-primer amplification (SISPA) and switching mechanism at the 5' end of RNA template (SMART) was most commonly used amplification methods, which produced better genome coverage for most of the viruses. While mNGS showed better detection capabilities, they showed variable agreement with routine diagnostic methods, with sensitivity varying from 62.5% to 95%. Rhinovirus was detected mainly in lower respiratory tract infections (68%) and respiratory syncytial viruses in upper respiratory tract infections (60%). Although mNGS has the potential, its routine use is hampered by various technical issues. Overcoming these limitations through optimized sequencing protocols and greater cost-effectiveness will be pivotal in integrating mNGS into clinical applications.
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BibTeX
@article{kumari2026metagenomic,
title = {{Metagenomic next-generation sequencing for the detection of viruses from respiratory samples: a systematized review on current methods and future directions.}},
author = {Pushpa Kumari and Sudheesh N and Krishna Kumar Ballamoole and Babak Afrough and Anitha Jagadesh},
journal = {Virology journal},
year = {2026},
month = aug,
doi = {10.1186/s12985-026-03269-0},
url = {https://europepmc.org/article/MED/42972591},
note = {CC BY},
}
RIS
TY - JOUR
TI - Metagenomic next-generation sequencing for the detection of viruses from respiratory samples: a systematized review on current methods and future directions.
AU - Pushpa Kumari
AU - Sudheesh N
AU - Krishna Kumar Ballamoole
AU - Babak Afrough
AU - Anitha Jagadesh
PY - 2026
DA - 2026/08/05
JO - Virology journal
DO - 10.1186/s12985-026-03269-0
UR - https://europepmc.org/article/MED/42972591
AB - The comprehensive identification of viruses using metagenomic next-generation sequencing (mNGS) has revolutionized the infectious disease diagnosis. Accurate detection of viruses on time is crucial to effectively control the outbreak. This review has meticulously analyzed the use of mNGS to identify viruses from respiratory samples, emphasizing how sample enrichment and amplification methods affect viral detection efficiency and genome coverage. A total of 66 studies were considered to evaluate sequencing workflows and their alignment with routine molecular techniques. Among these studies, the most frequently used method to enrich viruses was centrifugation (50%), followed by enzymatic treatment (45%). Sequence-independent single-primer amplification (SISPA) and switching mechanism at the 5' end of RNA template (SMART) was most commonly used amplification methods, which produced better genome coverage for most of the viruses. While mNGS showed better detection capabilities, they showed variable agreement with routine diagnostic methods, with sensitivity varying from 62.5% to 95%. Rhinovirus was detected mainly in lower respiratory tract infections (68%) and respiratory syncytial viruses in upper respiratory tract infections (60%). Although mNGS has the potential, its routine use is hampered by various technical issues. Overcoming these limitations through optimized sequencing protocols and greater cost-effectiveness will be pivotal in integrating mNGS into clinical applications.
SN - 1743-422X
ER -