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ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads

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TLDR
A new tool is presented, ARIBA, that identifies AMR-associated genes and single nucleotide polymorphisms directly from short reads, and generates detailed and customizable output.
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.

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Epidemic of carbapenem-resistant Klebsiella pneumoniae in Europe is driven by nosocomial spread

TL;DR: Combined analysis of the genetic and geographic distances between isolates with different β-lactam resistance determinants suggests that the propensity of K. pneumoniae to spread in hospital environments correlates with the degree of resistance and that carbapenemase-positive isolates have the highest transmissibility.
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Sequencing-based methods and resources to study antimicrobial resistance.

TL;DR: Focusing on sequence-based discovery of antibiotic resistance genes, this Review discusses computational strategies and resources for resistance gene identification in genomic and metagenomic samples, including recent deep-learning approaches.
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Using Genomics to Track Global Antimicrobial Resistance.

TL;DR: A scientific literature review is conducted and a description of examples of available tools and databases for antimicrobial resistance (AMR) detection and future perspectives and recommendations are presented.
Journal ArticleDOI

Genome-Based Prediction of Bacterial Antibiotic Resistance.

TL;DR: It is argued that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences.
Journal ArticleDOI

Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella

TL;DR: A collection of 5,278 nontyphoidal Salmonella genomes was used to generate extreme gradient boosting (XGBoost)-based machine learning models for predicting MICs for 15 antibiotics, showing that highly accurate MIC prediction models can be generated with less than 500 genomes.
References
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Journal ArticleDOI

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The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

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