Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity
AuthorWatts, George S
Thornton, James E
Ponsero, Alise J
Slepian, Marvin J
Armstrong, David G
Cranmer, Lee D
Hurwitz, Bonnie L
AffiliationUniv Arizona, Ctr Canc
Univ Arizona, Dept Pharmacol
Univ Arizona, Dept Biosyst Engn
Univ Arizona, Dept Med
Univ Arizona, Dept Biomed Engn
Univ Arizona, Arizona Ctr Accelerated Biomed Innovat
Univ Arizona, Dept Family & Community Med
Univ Arizona, BIO5 Inst
MetadataShow full item record
PublisherPUBLIC LIBRARY SCIENCE
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.1006863
JournalPLOS COMPUTATIONAL BIOLOGY
RightsCopyright © 2019 Watts et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
Collection InformationThis 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 email@example.com.
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.
NoteOpen access journal
VersionFinal published version
SponsorsSouthwest 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 ; University of Arizona; Leukemia and Lymphoma SocietyLeukemia and Lymphoma Society
Except where otherwise noted, this item's license is described as Copyright © 2019 Watts et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
- A novel data structure to support ultra-fast taxonomic classification of metagenomic sequences with k-mer signatures.
- Authors: Liu X, Yu Y, Liu J, Elliott CF, Qian C, Liu J
- Issue date: 2018 Jan 1
- k-SLAM: accurate and ultra-fast taxonomic classification and gene identification for large metagenomic data sets.
- Authors: Ainsworth D, Sternberg MJE, Raczy C, Butcher SA
- Issue date: 2017 Feb 28
- Selection of marker genes for genetic barcoding of microorganisms and binning of metagenomic reads by Barcoder software tools.
- Authors: Rotimi AM, Pierneef R, Reva ON
- Issue date: 2018 Aug 30
- Exploiting topic modeling to boost metagenomic reads binning.
- Authors: Zhang R, Cheng Z, Guan J, Zhou S
- Issue date: 2015
- Quality control of microbiota metagenomics by k-mer analysis.
- Authors: Plaza Onate F, Batto JM, Juste C, Fadlallah J, Fougeroux C, Gouas D, Pons N, Kennedy S, Levenez F, Dore J, Ehrlich SD, Gorochov G, Larsen M
- Issue date: 2015 Mar 14