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dc.contributor.authorWatts, George S
dc.contributor.authorThornton, James E
dc.contributor.authorYouens-Clark, Ken
dc.contributor.authorPonsero, Alise J
dc.contributor.authorSlepian, Marvin J
dc.contributor.authorMenashi, Emmanuel
dc.contributor.authorHu, Charles
dc.contributor.authorDeng, Wuquan
dc.contributor.authorArmstrong, David G
dc.contributor.authorReed, Spenser
dc.contributor.authorCranmer, Lee D
dc.contributor.authorHurwitz, Bonnie L
dc.date.accessioned2020-01-28T20:30:56Z
dc.date.available2020-01-28T20:30:56Z
dc.date.issued2019-11-22
dc.identifier.citationWatts GS, Thornton JE, Jr., Youens-Clark K, Ponsero AJ, Slepian MJ, Menashi E, et al. (2019) Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity. PLoS Comput Biol 15 (11): e1006863. https://doi.org/10.1371/journal. pcbi.1006863en_US
dc.identifier.issn1553-734X
dc.identifier.pmid31756192
dc.identifier.doi10.1371/journal.pcbi.1006863
dc.identifier.urihttp://hdl.handle.net/10150/636751
dc.description.abstractInfections are a serious health concern worldwide, particularly in vulnerable populations such as the immunocompromised, elderly, and young. Advances in metagenomic sequencing availability, speed, and decreased cost offer the opportunity to supplement or even replace culture-based identification of pathogens with DNA sequence-based diagnostics. Adopting metagenomic analysis for clinical use requires that all aspects of the workflow are optimized and tested, including data analysis and computational time and resources. We tested the accuracy, sensitivity, and resource requirements of three top metagenomic taxonomic classifiers that use fast k-mer based algorithms: Centrifuge, CLARK, and KrakenUniq. Binary mixtures of bacteria showed all three reliably identified organisms down to 1% relative abundance, while only the relative abundance estimates of Centrifuge and CLARK were accurate. All three classifiers identified the organisms present in their default databases from a mock bacterial community of 20 organisms, but only Centrifuge had no false positives. In addition, Centrifuge required far less computational resources and time for analysis. Centrifuge analysis of metagenomes obtained from samples of VAP, infected DFUs, and FN showed Centrifuge identified pathogenic bacteria and one virus that were corroborated by culture or a clinical PCR assay. Importantly, in both diabetic foot ulcer patients, metagenomic sequencing identified pathogens 4–6 weeks before culture. Finally, we show that Centrifuge results were minimally affected by elimination of time-consuming read quality control and host screening steps.en_US
dc.description.sponsorshipSouthwest Environmental Health Sciences Center, NIEHS grant [ES06694]; Arizona Cancer Center, NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [CA23074]; University of Arizona Bio5 Institute; Flinn Foundation [2097]; University of Arizona; Leukemia and Lymphoma SocietyLeukemia and Lymphoma Societyen_US
dc.language.isoenen_US
dc.publisherPUBLIC LIBRARY SCIENCEen_US
dc.rightsCopyright © 2019 Watts et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are crediteden_US
dc.titleIdentification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivityen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Ctr Cancen_US
dc.contributor.departmentUniv Arizona, Dept Pharmacolen_US
dc.contributor.departmentUniv Arizona, Dept Biosyst Engnen_US
dc.contributor.departmentUniv Arizona, Dept Meden_US
dc.contributor.departmentUniv Arizona, Dept Biomed Engnen_US
dc.contributor.departmentUniv Arizona, Arizona Ctr Accelerated Biomed Innovaten_US
dc.contributor.departmentUniv Arizona, Dept Family & Community Meden_US
dc.contributor.departmentUniv Arizona, BIO5 Insten_US
dc.identifier.journalPLOS COMPUTATIONAL BIOLOGYen_US
dc.description.noteOpen access journalen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitlePLoS computational biology
refterms.dateFOA2020-01-28T20:30:57Z


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