TY - JOUR T1 - Rapid antibiotic resistance predictions from genome sequence data for <em>S. aureus</em> and <em>M. tuberculosis</em> JF - bioRxiv DO - 10.1101/018564 SP - 018564 AU - Phelim Bradley AU - N. Claire Gordon AU - Timothy M. Walker AU - Laura Dunn AU - Simon Heys AU - Bill Huang AU - Sarah Earle AU - Louise J. Pankhurst AU - Luke Anson AU - Mariateresa de Cesare AU - Paolo Piazza AU - Antonina A. Votintseva AU - Tanya Golubchik AU - Daniel J. Wilson AU - David H. Wyllie AU - Roland Diel AU - Stefan Niemann AU - Silke Feuerriegel AU - Thomas A. Kohl AU - Nazir Ismail AU - Shaheed V. Omar AU - E. Grace Smith AU - David Buck AU - Gil McVean AU - A. Sarah Walker AU - Tim E.A. Peto AU - Derrick W. Crook AU - Zamin Iqbal Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/04/26/018564.abstract N2 - Rapid and accurate detection of antibiotic resistance in pathogens is an urgent need, affecting both patient care and population-scale control. Microbial genome sequencing promises much, but many barriers exist to its routine deployment. Here, we address these challenges, using a de Bruijn graph comparison of clinical isolate and curated knowledge-base to identify species and predict resistance profile, including minor populations. This is implemented in a package, Mykrobe predictor, for S. aureus and M. tuberculosis, running in under three minutes on a laptop from raw data. For S. aureus, we train and validate in 495/471 samples respectively, finding error rates comparable to gold-standard phenotypic methods, with sensitivity/specificity of 99.3%/99.5% across 12 drugs. For M. tuberculosis, we identify species and predict resistance with specificity of 98.5% (training/validating on 1920/1609 samples). Sensitivity of 82.6% is limited by current understanding of genetic mechanisms. Finally, we demonstrate feasibility of an emerging single-molecule sequencing technique. ER -