Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis

@inproceedings{Bradley2015RapidAP,
  title={Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis},
  author={Phelim Bradley and N Claire Gordon and Timothy M. Walker and Laura Dunn and Simon Heys and Bill Huang and Sarah G. Earle and L Pankhurst and Luke W. Anson and Mariateresa de Cesare and Paolo A. Piazza and Antonina A. Votintseva and Tanya Golubchik and Daniel J. Wilson and David H. Wyllie and Roland Diel and Stefan Niemann and Silke Feuerriegel and Thomas Andreas Kohl and Nazir Ahmed Ismail and Shaheed Vally Omar and E. Grace Smith and David H. Buck and Gilean McVean and A. Sarah Walker and Tim E. A. Peto and Derrick W. Crook and Zamin Iqbal},
  booktitle={Nature communications},
  year={2015}
}
The rise of antibiotic-resistant bacteria has led to an urgent need for rapid detection of drug resistance in clinical samples, and improvements in global surveillance. Here we show how de Bruijn graph representation of bacterial diversity can be used to identify species and resistance profiles of clinical isolates. We implement this method for Staphylococcus aureus and Mycobacterium tuberculosis in a software package ('Mykrobe predictor') that takes raw sequence data as input, and generates a… CONTINUE READING
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