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Genome Med. 2015 May 27;7(1):51. doi: 10.1186/s13073-015-0164-0. eCollection 2015.

Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences.

Author information

1
Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT UK.
2
Advanced Data Analysis Centre, University of Nottingham, Wollaton Road, Nottingham, NG8 1BB UK.
3
Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia ; Sydney Emerging Infections and Biosecurity Institute and School of Public Health, University of Sydney, Sydney, Australia.
4
Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
5
Tuberculosis Laboratory, Instituto Nacional de Saude Dr. Ricardo Jorge, Porto, Portugal.
6
Centro de Patogénese Molecular, Faculdade de Farmácia da Universidade de Lisboa, Lisbon, Portugal.
7
Grupo de Micobactérias, Unidade de Microbiologia Médica, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal.
8
Department of Pathology & Microbiology, Aga Khan University Hospital, Karachi, Pakistan.
9
Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT UK ; Karonga Prevention Study, Chilumba, Malawi.
10
Department of Computer Science, Birkbeck College, University of London, Malet Street, London, WC1E 7HX UK.

Abstract

Mycobacterium tuberculosis drug resistance (DR) challenges effective tuberculosis disease control. Current molecular tests examine limited numbers of mutations, and although whole genome sequencing approaches could fully characterise DR, data complexity has restricted their clinical application. A library (1,325 mutations) predictive of DR for 15 anti-tuberculosis drugs was compiled and validated for 11 of them using genomic-phenotypic data from 792 strains. A rapid online 'TB-Profiler' tool was developed to report DR and strain-type profiles directly from raw sequences. Using our DR mutation library, in silico diagnostic accuracy was superior to some commercial diagnostics and alternative databases. The library will facilitate sequence-based drug-susceptibility testing.

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