A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples
Open accessAbstract
Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Here we describe SURPI (“sequence-based ultrarapid pathogen identification”), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7–500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times.
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A single mNGS assay can in principle detect almost every pathogen type-viruses, bacteria, fungi, and parasites.
“In principle, nearly all pathogens, including viruses, bacteria, fungi and parasites, can be identified in a single assay56.”
From Clinical metagenomics (2019) · cites this paper as [56]
Customized bioinformatics pipelines for clinical mNGS data still require highly trained programming staff to develop, validate and maintain for clinical use.
“Thus, customized bioinformatics pipelines for analysis of clinical mNGS data56,109–111 still require highly trained programming staff to develop, validate and maintain the pipeline for clinical use.”
From Clinical metagenomics (2019) · cites this paper as [56]
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- Authors
- Naccache, S. N., Federman, S., Veeraraghavan, N., Zaharia, M., Lee, D., Samayoa, E., Bouquet, J., Greninger, A. L., Luk, K.-C., Enge, B., Wadford, D. A., Messenger, S. L., Genrich, G. L., Pellegrino, K., Grard, G., Leroy, E., Schneider, B. S., Fair, J. N., Martínez, M. A., Isa, P., Crump, J. A., DeRisi, J. L., Sittler, T., Hackett, J., Miller, S., & Chiu, C. Y.
- Affiliations
- University of California, San Francisco (US)
- University of California, Berkeley (US)
- Abbott (United States) (US)
- California Department of Public Health (US)
- Centre International de Recherches Médicales de Franceville (GA)
- Metabiota (United States) (US)
- Duke University (US)
- Corresponding author
- Chiu, Charles Y.
- ORCID
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2014-06-04
- Volume
- 24
- Issue
- 7
- Pages
- 1180-1192
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC4079973
- OpenAlex
- W2137662380
- Cited by
- 460
- References
- 59
- Citations by year
- 2026: 12 · 2025: 21 · 2024: 25 · 2023: 26 · 2022: 33 · 2021: 50 · 2020: 38 · 2019: 38 · 2018: 48 · 2017: 44 · 2016: 55 · 2015: 64 · 2014: 6
- Type
- posted content
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- cc-by-nc
- Locations
- Genome Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DukeSpace (Duke University) (submittedVersion, cc-by-nc-nd)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc)
- HighWire Press Open Archive (submittedVersion)
- Funders
- United States Agency for International Development
- National Institutes of Health
- Defense Threat Reduction Agency
- National Heart, Lung, and Blood Institute (R01HL105704)
- National Institute of General Medical Sciences (T32GM007618)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Bacterial Identification and Susceptibility Testing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- pathogen identification, metagenomics, clinical microbiology, cloud computing, bioinformatics pipeline, virus detection, bacterial detection, de novo assembly, protein homology search, infectious disease diagnosis, outbreak investigation
- MeSH terms
- Humans, ROC Curve, Software, Reproducibility of Results, Computational Biology, Databases, Nucleic Acid, Metagenomics, High-Throughput Nucleotide Sequencing
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