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J Antimicrob Chemother. 2016 Sep;71(9):2484-8. doi: 10.1093/jac/dkw184. Epub 2016 Jun 30.

Benchmarking of methods for identification of antimicrobial resistance genes in bacterial whole genome data.

Author information

1
Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, 2800 Kgs Lyngby, Denmark.
2
Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, 2800 Kgs Lyngby, Denmark Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, 2800 Kgs Lyngby, Denmark.
3
Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, 2800 Kgs Lyngby, Denmark.
4
Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, 2800 Kgs Lyngby, Denmark lund@cbs.dtu.dk.

Abstract

OBJECTIVES:

Next generation sequencing (NGS) may be an alternative to phenotypic susceptibility testing for surveillance and clinical diagnosis. However, current bioinformatics methods may be associated with false positives and negatives. In this study, a novel mapping method was developed and benchmarked to two different methods in current use for identification of antibiotic resistance genes in bacterial WGS data.

METHODS:

A novel method, KmerResistance, which examines the co-occurrence of k-mers between the WGS data and a database of resistance genes, was developed. The performance of this method was compared with two previously described methods; ResFinder and SRST2, which use an assembly/BLAST method and BWA, respectively, using two datasets with a total of 339 isolates, covering five species, originating from the Oxford University Hospitals NHS Trust and Danish pig farms. The predicted resistance was compared with the observed phenotypes for all isolates. To challenge further the sensitivity of the in silico methods, the datasets were also down-sampled to 1% of the reads and reanalysed.

RESULTS:

The best results were obtained by identification of resistance genes by mapping directly against the raw reads. This indicates that information might be lost during assembly. KmerResistance performed significantly better than the other methods, when data were contaminated or only contained few sequence reads.

CONCLUSIONS:

Read mapping is superior to assembly-based methods and the new KmerResistance seemingly outperforms currently available methods particularly when including datasets with few reads.

PMID:
27365186
DOI:
10.1093/jac/dkw184
[Indexed for MEDLINE]

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