The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.
mNGS in clinical microbiology laboratories: on the road to maturity
Abstract
Metagenomic next-generation sequencing (mNGS) is increasingly being applied in clinical laboratories for unbiased culture-independent diagnosis. Whether it can be a next routine pathogen identification tool has become a topic of concern. We review the current implementation of this new technology for infectious disease diagnostics and discuss the feasibility of transforming mNGS into a routine diagnostic test. Since 2008, numerous studies from over 20 countries have revealed the practicality of mNGS in the work-up of undiagnosed infectious diseases. mNGS performs well in identifying rare, novel, difficult-to-detect and coinfected pathogens directly from clinical samples and presents great potential in resistance prediction by sequencing the antibiotic resistance genes, providing new diagnostic evidence that can be used to guide treatment options and improve antibiotic stewardship. Many physicians recognized mNGS as a last resort method to address clinical infection problems. Although several hurdles, such as workflow validation, quality control, method standardisation, and data interpretation, remain before mNGS can be implemented routinely in clinical laboratories, they are temporary and can be overcome by rapidly evolving technologies. With more validated workflows, lower cost and turnaround time, and simplified interpretation criteria, mNGS will be widely accepted in clinical practice. Overall, mNGS is transforming the landscape of clinical microbiology laboratories, and to ensure that it is properly utilised in clinical diagnosis, both physicians and microbiologists should have a thorough understanding of the power and limitations of this method.
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Cites
Identification of acquired antimicrobial resistance genes
Zankari, E., Hasman, H., Cosentino, S., Vestergaard, M., Rasmussen, S., Lund, O., Aarestrup, F. M., & Larsen, M. V. (2012) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dks261 · Cited by 4,980
Environmental genome shotgun sequencing of the sargasso sea
Venter, J. C., Remington, K., Heidelberg, J. F., Halpern, A. L., Rusch, D., Eisen, J. A., Wu, D., Paulsen, I., Nelson, K. E., Nelson, W., et al. (2004) · Science · doi:10.1126/science.1093857 · Cited by 4,376
Reagent and laboratory contamination can critically impact sequence-based microbiome analyses
Salter, S. J., Cox, M. J., Turek, E. M., Calus, S. T., Cookson, W. O., Moffatt, M. F., Turner, P., Parkhill, J., Loman, N. J., & Walker, A. W. (2014) · BMC Biology · doi:10.1186/s12915-014-0087-z · Cited by 3,463
CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database
Jia, B., Raphenya, A. R., Alcock, B., Waglechner, N., Guo, P., Tsang, K. K., Lago, B. A., Dave, B. M., Pereira, S., Sharma, A. N., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1004 · Cited by 2,661
Best practices for analysing microbiomes
Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L.-I., McDonald, D., et al. (2018) · Nature Reviews Microbiology · doi:10.1038/s41579-018-0029-9 · Cited by 2,030
Comparative Metagenomics of Microbial Communities
Tringe, S. G., von Mering, C., Kobayashi, A., Salamov, A. A., Chen, K., Chang, H. W., Podar, M., Short, J. M., Mathur, E. J., Detter, J. C., et al. (2005) · Science · doi:10.1126/science.1107851 · Cited by 1,689
Chiu, C. Y., & Miller, S. A. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0113-7 · Cited by 1,620
Clinical Metagenomic Next-Generation Sequencing for Pathogen Detection
Gu, W., Miller, S., & Chiu, C. Y. (2019) · Annual Review of Pathology: Mechanisms of Disease · doi:10.1146/annurev-pathmechdis-012418-012751 · Cited by 1,458
ARDB--Antibiotic Resistance Genes Database
Liu, B., & Pop, M. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn656 · Cited by 1,257
Metagenomics: Genomic Analysis of Microbial Communities
Riesenfeld, C. S., Schloss, P. D., & Handelsman, J. (2004) · Annual Review of Genetics · doi:10.1146/annurev.genet.38.072902.091216 · Cited by 1,129
Cited by
Emerging contaminants: A One Health perspective
Wang, F., Xiang, L., Sze-Yin Leung, K., Elsner, M., Zhang, Y., Guo, Y., Pan, B., Sun, H., An, T., Ying, G., et al. (2024) · The Innovation · doi:10.1016/j.xinn.2024.100612 · Cited by 639
Metadata
- Authors
- Han, D., Li, Z., Li, R., Tan, P., Zhang, R., & Li, J.
- Venue
- Critical Reviews in Microbiology
- Publisher
- Informa UK Limited
- Published
- 2019-11-02
- Volume
- 45
- Issue
- 5-6
- Pages
- 668-685
- ISSN
- 1040-841X (ISSN-L)
- 1549-7828
- OpenAlex
- W2982825545
- Cited by
- 361
- References
- 108
- FWCI
- 14.57
- Type
- journal article
- Language
- en
- Open access
- closed
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- Han, D., Li, Z., Li, R., Tan, P., Zhang, R., & Li, J. (2019). mNGS in clinical microbiology laboratories: on the road to maturity. Critical Reviews in Microbiology. https://doi.org/10.1080/1040841x.2019.1681933
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