The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Laboratory validation of a clinical metagenomic sequencing assay for pathogen detection in cerebrospinal fluid
Open accessPublished version of a preprint
Abstract
Metagenomic next-generation sequencing (mNGS) for pan-pathogen detection has been successfully tested in proof-of-concept case studies in patients with acute illness of unknown etiology but to date has been largely confined to research settings. Here, we developed and validated a clinical mNGS assay for diagnosis of infectious causes of meningitis and encephalitis from cerebrospinal fluid (CSF) in a licensed microbiology laboratory. A customized bioinformatics pipeline, SURPI+, was developed to rapidly analyze mNGS data, generate an automated summary of detected pathogens, and provide a graphical user interface for evaluating and interpreting results. We established quality metrics, threshold values, and limits of detection of 0.2–313 genomic copies or colony forming units per milliliter for each representative organism type. Gross hemolysis and excess host nucleic acid reduced assay sensitivity; however, spiked phages used as internal controls were reliable indicators of sensitivity loss. Diagnostic test accuracy was evaluated by blinded mNGS testing of 95 patient samples, revealing 73% sensitivity and 99% specificity compared to original clinical test results, and 81% positive percent agreement and 99% negative percent agreement after discrepancy analysis. Subsequent mNGS challenge testing of 20 positive CSF samples prospectively collected from a cohort of pediatric patients hospitalized with meningitis, encephalitis, and/or myelitis showed 92% sensitivity and 96% specificity relative to conventional microbiological testing of CSF in identifying the causative pathogen. These results demonstrate the analytic performance of a laboratory-validated mNGS assay for pan-pathogen detection, to be used clinically for diagnosis of neurological infections from CSF.
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Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
Quality control and preprocessing of metagenomic datasets
Schmieder, R., & Edwards, R. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr026 · Cited by 5,072
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
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
Self-citationGranerod, J., Ambrose, H. E., Davies, N. W., Clewley, J. P., Walsh, A. L., Morgan, D., Cunningham, R., Zuckerman, M., Mutton, K. J., Solomon, T., et al. (2010) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(10)70222-x · Cited by 1,358
Clinical Metagenomic Sequencing for Diagnosis of Meningitis and Encephalitis
Wilson, M. R., Sample, H. A., Zorn, K. C., Arevalo, S., Yu, G., Neuhaus, J., Federman, S., Stryke, D., Briggs, B., Langelier, C., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1803396 · Cited by 1,067
Self-citationActionable Diagnosis of Neuroleptospirosis by Next-Generation Sequencing
Wilson, M. R., Naccache, S. N., Samayoa, E., Biagtan, M., Bashir, H., Yu, G., Salamat, S. M., Somasekar, S., Federman, S., Miller, S., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1401268 · Cited by 977
Self-citationSimner, P. J., Miller, S., & Carroll, K. C. (2018) · Clinical Infectious Diseases · doi:10.1093/cid/cix881 · Cited by 821
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Chiu, C. Y., & Miller, S. A. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0113-7 · Cited by 1,620
Self-citationClinical Metagenomic Sequencing for Diagnosis of Meningitis and Encephalitis
Wilson, M. R., Sample, H. A., Zorn, K. C., Arevalo, S., Yu, G., Neuhaus, J., Federman, S., Stryke, D., Briggs, B., Langelier, C., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1803396 · Cited by 1,067
Self-citationRapid pathogen detection by metagenomic next-generation sequencing of infected body fluids
Gu, W., Deng, X., Lee, M., Sucu, Y. D., Arevalo, S., Stryke, D., Federman, S., Gopez, A., Reyes, K., Zorn, K., et al. (2021) · Nature Medicine · doi:10.1038/s41591-020-1105-z · Cited by 689
Self-citationBenchmarking Metagenomics Tools for Taxonomic Classification
Ye, S. H., Siddle, K. J., Park, D. J., & Sabeti, P. C. (2019) · Cell · doi:10.1016/j.cell.2019.07.010 · Cited by 593
mNGS in clinical microbiology laboratories: on the road to maturity
Han, D., Li, Z., Li, R., Tan, P., Zhang, R., & Li, J. (2019) · Critical Reviews in Microbiology · doi:10.1080/1040841x.2019.1681933 · Cited by 361
Metadata
- Authors
- Miller, S., Naccache, S. N., Samayoa, E., Messacar, K., Arevalo, S., Federman, S., Stryke, D., Pham, E., Fung, B., Bolosky, W. J., Ingebrigtsen, D., Lorizio, W., Paff, S. M., Leake, J. A., Pesano, R., DeBiasi, R., Dominguez, S., & Chiu, C. Y.
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2019-04-16
- Volume
- 29
- Issue
- 5
- Pages
- 831-842
- ISSN
- 1088-9051 (ISSN-L)
- 1549-5469
- OpenAlex
- W2951983907
- Cited by
- 572
- References
- 44
- Type
- posted content
- Language
- en
- Open access
- bronze
- Funders
- National Institutes of Health (R01 HL105704, R21/R33 AI120977)
- UC Center for Accelerated Innovation
- NIH (U54 HL119893)
- NCATS UCSF-CTSI (UL1 TR000004)
- California Initiative to Advance Precision Medicine
- Abbott Laboratories, Inc.
- UCSF Medical Center
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- Miller, S., Naccache, S. N., Samayoa, E., Messacar, K., Arevalo, S., Federman, S., Stryke, D., Pham, E., Fung, B., Bolosky, W. J., Ingebrigtsen, D., Lorizio, W., Paff, S. M., Leake, J. A., Pesano, R., DeBiasi, R., Dominguez, S., & Chiu, C. Y. (2019). Laboratory validation of a clinical metagenomic sequencing assay for pathogen detection in cerebrospinal fluid. Genome Research. https://doi.org/10.1101/gr.238170.118
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