Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications. Issue 10 (21st October 2022)

• Record Type: Journal Article
• Authors: Govender, Kumeren N.; Eyre, David W.
• Is Part Of: Microbial genomics. Volume 8:Issue 10(2022)
• Publication Date: 2022-10-21
• Subjects: diagnosis -- infection -- metagenomics -- simulation -- taxonomic classification -- whole-genome sequencing; Microbial genomics -- Periodicals; 572.8629
• DOI: 10.1099/mgen.0.000886
• Languages: English
• ISSNs: 2057-5858

Abstract

Abstract : Culture-independent metagenomic detection of microbial species has the potential to provide rapid and precise real-time diagnostic results. However, it is potentially limited by sequencing and taxonomic classification errors. We use simulated and real-world data to benchmark rates of species misclassification using 100 reference genomes for each of the ten common bloodstream pathogens and six frequent blood-culture contaminants ( n =1568, only 68 genomes were available for Micrococcus luteus ). Simulating both with and without sequencing error for both the Illumina and Oxford Nanopore platforms, we evaluated commonly used classification tools including Kraken2, Bracken and Centrifuge, utilizing mini (8 GB) and standard (30–50 GB) databases. Bracken with the standard database performed best, the median percentage of reads across both sequencing platforms identified correctly to the species level was 97.8% (IQR 92.7:99.0) [range 5:100]. For Kraken2 with a mini database, a commonly used combination, median species-level identification was 86.4% (IQR 50.5:93.7) [range 4.3:100]. Classification performance varied by species, with Escherichia coli being more challenging to classify correctly (probability of reads being assigned to the correct species: 56.1–96.0%, varying by tool used). Human read misclassification was negligible. By filtering out shorter Nanopore reads we found performance similar or superior to Illumina sequencing, despite higher sequencing error rates. Misclassification was more common when the misclassified species had a higher average nucleotide identity to the true species. Our findings highlight taxonomic misclassification of sequencing data occurs and varies by sequencing and analysis workflow. To account for 'bioinformatic contamination' we present a contamination catalogue that can be used in metagenomic pipelines to ensure accurate results that can support clinical decision making.