Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity.

Autor: Watts GS; University of Arizona Cancer Center and Department of Pharmacology, University of Arizona, Tucson, Arizona, United States of America., Thornton JE Jr; Department of Biosystems Engineering, University of Arizona, Tucson, Arizona, United States of America., Youens-Clark K; Department of Biosystems Engineering, University of Arizona, Tucson, Arizona, United States of America., Ponsero AJ; Department of Biosystems Engineering, University of Arizona, Tucson, Arizona, United States of America., Slepian MJ; Department of Medicine, University of Arizona, Tucson, Arizona, United States of America.; Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, United States of America.; Arizona Center for Accelerated Biomedical Innovation, University of Arizona, Tucson, Arizona, United States of America., Menashi E; Honor Health Hospital, Scottsdale, Arizona, United States of America., Hu C; Dignity Health Chandler Regional Medical Center, Chandler, Arizona, United States of America., Deng W; Department of Endocrinology, Multidisciplinary Diabetic Foot Medical Center, Affiliated Central Hospital of Chongqing University, Chongqing, China., Armstrong DG; Southwestern Academic Limb Salvage Alliance (SALSA), Department of Surgery, Keck School of Medicine of University of Southern California, Los Angeles, California, United States of America., Reed S; University of Arizona Department of Family and Community Medicine, Tucson, Arizona, United States of America., Cranmer LD; Department of Medicine, University of Washington and Fred Hutchinson Cancer Research Center, and Seattle Cancer Care Alliance, Seattle, Washington, United States of America., Hurwitz BL; Department of Biosystems Engineering, University of Arizona, Tucson, Arizona, United States of America.; BIO5 Institute, University of Arizona, Tucson, Arizona, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2019 Nov 22; Vol. 15 (11), pp. e1006863. Date of Electronic Publication: 2019 Nov 22 (Print Publication: 2019).
DOI: 10.1371/journal.pcbi.1006863
Abstrakt: Infections 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.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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