Comparing genome versus proteome-based identification of clinical bacterial isolates. (22nd December 2016)

• Record Type: Journal Article
• Authors: Galata, Valentina; Backes, Christina; Laczny, Cédric Christian; Hemmrich-Stanisak, Georg; Li, Howard; Smoot, Laura; Posch, Andreas Emanuel; Schmolke, Susanne; Bischoff, Markus; von Müller, Lutz; Plum, Achim; Franke, Andre; Keller, Andreas
• Is Part Of: Briefings in bioinformatics. Volume 19:Number 3(2018:May)
• Page Start: 495
• Page End: 505
• Publication Date: 2016-12-22
• Subjects: bacteria -- taxonomy -- MALDI-TOF MS -- whole-genome next-generation sequencing; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbw122
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Whole-genome sequencing (WGS) is gaining importance in the analysis of bacterial cultures derived from patients with infectious diseases. Existing computational tools for WGS-based identification have, however, been evaluated on previously defined data relying thereby unwarily on the available taxonomic information. Here, we newly sequenced 846 clinical gram-negative bacterial isolates representing multiple distinct genera and compared the performance of five tools (CLARK, Kaiju, Kraken, DIAMOND/MEGAN and TUIT). To establish a faithful 'gold standard', the expert-driven taxonomy was compared with identifications based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) analysis. Additionally, the tools were also evaluated using a data set of 200 Staphylococcus aureus isolates. CLARK and Kraken (with k =31) performed best with 626 (100%) and 193 (99.5%) correct species classifications for the gram-negative and S . aureus isolates, respectively. Moreover, CLARK and Kraken demonstrated highest mean F -measure values (85.5/87.9% and 94.4/94.7% for the two data sets, respectively) in comparison with DIAMOND/MEGAN (71 and 85.3%), Kaiju (41.8 and 18.9%) and TUIT (34.5 and 86.5%). Finally, CLARK, Kaiju and Kraken outperformed the other tools by a factor of 30 to 170 fold in terms of runtime. We conclude that the application of nucleotide-based tools using k-mers—e.g. CLARK or Kraken—allows for accurate and fast taxonomic characterization of bacterial isolates from WGS data. Hence, our results suggest WGS-based genotyping to be a promising alternative to the MS-based biotyping in clinical settings. Moreover, we suggest that complementary information should be used for the evaluation of taxonomic classification tools, as public databases may suffer from suboptimal annotations.