Abstract
Antimicrobial resistance (AMR) is one of the major threats to human and animal health worldwide, yet few high-throughput tools exist to analyse and predict the resistance of a bacterial isolate from sequencing data. Here we present a new tool, ARIBA, that identifies AMR-associated genes and single nucleotide polymorphisms directly from short reads, and generates detailed and customizable output. The accuracy and advantages of ARIBA over other tools are demonstrated on three datasets from Gram-positive and Gram-negative bacteria, with ARIBA outperforming existing methods.
Original language | English |
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Article number | 000131 |
Journal | Microbial Genomics |
Volume | 3 |
Issue number | 10 |
Early online date | 4 Sep 2017 |
DOIs | |
Publication status | Published - 1 Oct 2017 |
Externally published | Yes |
Profiles
-
Alison Mather
- Faculty of Medicine and Health Sciences - ISP Leader
- Norwich Medical School - Honorary Professor
- Metabolic Health - Member
Person: Honorary, Research Group Member, Academic, Teaching & Research