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      Laboratory validation of a clinical metagenomic sequencing assay for pathogen detection in cerebrospinal fluid

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          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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          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            Basic local alignment search tool.

            A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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              Cutadapt removes adapter sequences from high-throughput sequencing reads

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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                May 2019
                May 2019
                : 29
                : 5
                : 831-842
                Affiliations
                [1 ]Department of Laboratory Medicine, University of California, San Francisco, San Francisco, California 94143, USA;
                [2 ]UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, California 94143, USA;
                [3 ]Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, California 90027, USA;
                [4 ]Department of Pediatrics, Children's Hospital Colorado and University of Colorado School of Medicine, Aurora, Colorado 80045, USA;
                [5 ]Microsoft Research, Redmond, Washington 98052, USA;
                [6 ]Quest Diagnostics Nichols Institute, San Juan Capistrano, California 92675, USA;
                [7 ]Department of Pediatrics, Division of Pediatric Infectious Diseases, Children's National Health System, Washington, DC 20010, USA;
                [8 ]Department of Pediatrics, Microbiology, Immunology, and Tropical Medicine, The George Washington University School of Medicine, Washington, DC 20037, USA;
                [9 ]Department of Medicine, Division of Infectious Diseases, University of California, San Francisco, San Francisco, California 94143, USA
                Author notes
                [10]

                These authors contributed equally to this work.

                Corresponding author: charles.chiu@ 123456ucsf.edu
                Author information
                http://orcid.org/0000-0003-2915-2094
                Article
                9509184
                10.1101/gr.238170.118
                6499319
                30992304
                61d9ac4a-3a84-4ad1-90b9-3cd77ddfc1ac
                © 2019 Miller et al.; Published by Cold Spring Harbor Laboratory Press

                This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 6 April 2018
                : 25 February 2019
                Page count
                Pages: 12
                Funding
                Funded by: National Institutes of Health (NIH) , open-funder-registry 10.13039/100000002;
                Award ID: R01 HL105704
                Award ID: R21/R33 AI120977
                Funded by: UC Center for Accelerated Innovation
                Funded by: NIH , open-funder-registry 10.13039/100000002;
                Award ID: U54 HL119893
                Funded by: NIH , open-funder-registry 10.13039/100000002;
                Funded by: NCATS UCSF-CTSI
                Award ID: UL1 TR000004
                Funded by: California Initiative to Advance Precision Medicine
                Funded by: Abbott Laboratories, Inc.
                Funded by: UCSF Medical Center
                Categories
                Method

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