Resistance sniffer : an online tool for prediction of drug resistance patterns of Mycobacterium tuberculosis isolates using next generation sequencing data

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dc.contributor.author Muzondiwa, Dillon
dc.contributor.author Mutshembele, Awelani
dc.contributor.author Pierneef, Rian
dc.contributor.author Reva, Oleg N.
dc.date.accessioned 2020-05-28T15:01:33Z
dc.date.available 2020-05-28T15:01:33Z
dc.date.issued 2020-02
dc.description.abstract The effective control of multidrug resistant tuberculosis (MDR-TB) relies upon the timely diagnosis and correct treatment of all tuberculosis cases. Whole genome sequencing (WGS) has great potential as a method for the rapid diagnosis of drug resistant Mycobacterium tuberculosis (Mtb) isolates. This method overcomes most of the problems that are associated with current phenotypic drug susceptibility testing. However, the application of WGS in the clinical setting has been deterred by data complexities and skill requirements for implementing the technologies as well as clinical interpretation of the next generation sequencing (NGS) data. The proposed diagnostic application was drawn upon recent discoveries of patterns of Mtb clade-specific genetic polymorphisms associated with antibiotic resistance. A catalogue of genetic determinants of resistance to thirteen anti-TB drugs for each phylogenetic clade was created. A computational algorithm for the identification of states of diagnostic polymorphisms was implemented as an online software tool, Resistance Sniffer (http://resistance-sniffer.bi.up. ac.za/), and as a stand-alone software tool to predict drug resistance in Mtb isolates using complete or partial genome datasets in different file formats including raw Illumina fastq read files. The program was validated on sequenced Mtb isolates with data on antibiotic resistance trials available from GMTV database and from the TB Platform of South African Medical Research Council (SAMRC), Pretoria. The program proved to be suitable for probabilistic prediction of drug resistance profiles of individual strains and large sequence data sets. en_ZA
dc.description.department Biochemistry en_ZA
dc.description.department Genetics en_ZA
dc.description.department Microbiology and Plant Pathology en_ZA
dc.description.librarian am2020 en_ZA
dc.description.sponsorship The South African National Research Foundation (NRF) en_ZA
dc.description.uri https://www.elsevier.com/locate/ijmm en_ZA
dc.identifier.citation Muzondiwa, D., Mutshembele, A., Pierneef, R.E. et al. 2020, 'Resistance sniffer : an online tool for prediction of drug resistance patterns of Mycobacterium tuberculosis isolates using next generation sequencing data', International Journal of Medical Microbiology, vol. 310, art. 151399, pp. 1-11. en_ZA
dc.identifier.issn 1438-4221 (print)
dc.identifier.issn 1618-0607 (online)
dc.identifier.other 10.1016/j.ijmm.2020.151399
dc.identifier.uri http://hdl.handle.net/2263/74779
dc.language.iso en en_ZA
dc.publisher Elsevier en_ZA
dc.rights © 2020 The Authors. Published by Elsevier GmbH. This is an open access article under the CC BY license. en_ZA
dc.subject Antibiotic en_ZA
dc.subject Resistance en_ZA
dc.subject Clade specific en_ZA
dc.subject Single nucleotide polymorphism en_ZA
dc.subject Mycobacterium tuberculosis (MTB) en_ZA
dc.subject Multidrug resistant tuberculosis (MDR-TB) en_ZA
dc.subject Whole genome sequencing (WGS) en_ZA
dc.title Resistance sniffer : an online tool for prediction of drug resistance patterns of Mycobacterium tuberculosis isolates using next generation sequencing data en_ZA
dc.type Article en_ZA


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