RT Journal Article
SR Electronic
T1 Rapid antibiotic resistance predictions from genome sequence data for <em>S. aureus</em> and <em>M. tuberculosis</em>
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 018564
DO 10.1101/018564
A1 Phelim Bradley
A1 N. Claire Gordon
A1 Timothy M. Walker
A1 Laura Dunn
A1 Simon Heys
A1 Bill Huang
A1 Sarah Earle
A1 Louise J. Pankhurst
A1 Luke Anson
A1 Mariateresa de Cesare
A1 Paolo Piazza
A1 Antonina A. Votintseva
A1 Tanya Golubchik
A1 Daniel J. Wilson
A1 David H. Wyllie
A1 Roland Diel
A1 Stefan Niemann
A1 Silke Feuerriegel
A1 Thomas A. Kohl
A1 Nazir Ismail
A1 Shaheed V. Omar
A1 E. Grace Smith
A1 David Buck
A1 Gil McVean
A1 A. Sarah Walker
A1 Tim E.A. Peto
A1 Derrick W. Crook
A1 Zamin Iqbal
YR 2015
UL http://biorxiv.org/content/early/2015/04/26/018564.abstract
AB 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.