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A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples.

dc.contributor.author Naccache, SN
dc.contributor.author Federman, S
dc.contributor.author Veeraraghavan, N
dc.contributor.author Zaharia, M
dc.contributor.author Lee, D
dc.contributor.author Samayoa, E
dc.contributor.author Bouquet, J
dc.contributor.author Greninger, AL
dc.contributor.author Luk, K-C
dc.contributor.author Enge, B
dc.contributor.author Wadford, DA
dc.contributor.author Messenger, SL
dc.contributor.author Genrich, GL
dc.contributor.author Pellegrino, K
dc.contributor.author Grard, G
dc.contributor.author Leroy, E
dc.contributor.author Schneider, BS
dc.contributor.author Fair, JN
dc.contributor.author Martínez, MA
dc.contributor.author Isa, P
dc.contributor.author Crump, John Andrew
dc.contributor.author DeRisi, JL
dc.contributor.author Sittler, T
dc.contributor.author Hackett, J
dc.contributor.author Miller, S
dc.contributor.author Chiu, CY
dc.coverage.spatial United States
dc.date.accessioned 2017-03-02T19:08:55Z
dc.date.available 2017-03-02T19:08:55Z
dc.date.issued 2014-07
dc.identifier https://www.ncbi.nlm.nih.gov/pubmed/24899342
dc.identifier gr.171934.113
dc.identifier.uri https://hdl.handle.net/10161/13772
dc.description.abstract 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.
dc.language eng
dc.relation.ispartof Genome Res
dc.relation.isversionof 10.1101/gr.171934.113
dc.subject Computational Biology
dc.subject Databases, Nucleic Acid
dc.subject High-Throughput Nucleotide Sequencing
dc.subject Humans
dc.subject Metagenomics
dc.subject ROC Curve
dc.subject Reproducibility of Results
dc.subject Software
dc.title A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples.
dc.type Journal article
pubs.author-url https://www.ncbi.nlm.nih.gov/pubmed/24899342
pubs.begin-page 1180
pubs.end-page 1192
pubs.issue 7
pubs.organisational-group Clinical Science Departments
pubs.organisational-group Duke
pubs.organisational-group Medicine
pubs.organisational-group Medicine, Infectious Diseases
pubs.organisational-group Pathology
pubs.organisational-group School of Medicine
pubs.publication-status Published
pubs.volume 24
dc.identifier.eissn 1549-5469


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