@article {649992, title = {Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe}, journal = {Wellcome Open Research}, year = {2019}, abstract = {Two billion people are infected with\ Mycobacterium tuberculosis, leading to 10 million new cases of active tuberculosis and 1.5 million deaths annually. Universal access to drug susceptibility testing (DST) has become a World Health Organization priority. We previously developed a software tool,\ Mykrobe predictor, which provided offline species identification and drug resistance predictions for\ M. tuberculosis\ from whole genome sequencing (WGS) data. Performance was insufficient to support the use of WGS as an alternative to conventional phenotype-based DST, due to mutation catalogue limitations.\ Here we present a new tool,\ Mykrobe, which provides the same functionality based on a new software implementation. Improvements include i) an updated mutation catalogue giving greater sensitivity to detect pyrazinamide resistance, ii) support for user-defined resistance catalogues, iii) improved identification of non-tuberculous mycobacterial species, and iv) an updated statistical model for Oxford Nanopore Technologies sequencing data.\ Mykrobe\ is released under MIT license at https://github.com/mykrobe-tools/mykrobe. We incorporate mutation catalogues from the CRyPTIC consortium et al. (2018) and from Walker et al. (2015), and make improvements based on performance on an initial set of 3206 and an independent set of 5845\ M. tuberculosis\ Illumina sequences. To give estimates of error rates, we use a prospectively collected dataset of 4362\ M. tuberculosis isolates. Using culture based DST as the reference, we estimate\ Mykrobe\ to be 100\%, 95\%, 82\%, 99\% sensitive and 99\%, 100\%, 99\%, 99\% specific for rifampicin, isoniazid, pyrazinamide and ethambutol resistance prediction respectively. We benchmark against four other tools on 10207 (=5845+4362) samples, and also show that\ Mykrobe\ gives concordant results with nanopore data.\ We measure the ability of\ Mykrobe-based DST to guide personalized therapeutic regimen design in the context of complex drug susceptibility profiles, showing 94\% concordance of implied regimen with that driven by phenotypic DST, higher than all other benchmarked tools.}, url = {https://wellcomeopenresearch.org/articles/4-191}, author = {Hunt M and Bradley P and Grandjean Lapierre S and Heys S and THomsit M and Hall M and Malone K and Wintringer P and Walker T and Cirillo D and Comas I and Farhat M and Fowler P and Gardy J and Ismail N and Kohl T and Mathys V and Merker M and Niemann S and Vally Omar S and Sintchenko V and Smith G and van Soolingen D and Supply P and Tahseen S and Wilcox M and Arandjelovic I and Peto T and Crook D and Iqbal Z} }