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Evaluation of the application value of the whole genome sequence analysis tools,TB Profiler v2.8.0, Mykrobe v0.7.0 and PhyResSE v1.0, in testing drug-resistant tuberculosis
Authors:LI Bing-ying  ZHENG Xu-bin  HU Yi  XU Biao
Affiliation:Department of Epidemiology, School of Public Health, Key Lab of Health Technology Assessment, National Health Commission, Fudan University, Shanghai 200032, China
Abstract:Objective To evaluate the application of three whole genome sequence (WGS) analysis tools developed for Mycobacterium tuberculosis (MTB), TB Profiler v2.8.0, Mykrobe v0.7.0 and PhyResSE v1.0, in testing drug-resistant of tuberculosis. Methods WGS data and drug susceptibility testing (DST) results of 534 MTB clinical isolates from two previous studies were collected from National Center for Biotechnology Information Sequence Read Archive (NCBI SRA) database. Of the 534 MTB clinical isolates, 457 were multi-resistant and 77 were susceptible. Using DST as reference, WGS data were analyzed by TB Profiler, Mykrobe and PhyResSE to access the performance on predicting resistance to first-line and second-line anti-tuberculosis drugs. Results Taking DST results as reference, the sensitivities of TB Profiler, Mykrobe and PhyResSE for rifampicin resistance were similar, which were 90.81% (415/457), 87.75% (401/457) and 90.81% (415/457), respectively. The sensitivities of Mykrobe and PhyResSE for isoniazid resistance were 76.42% (350/458) and 76.20% (349/458), slightly higher than that of TB Profiler (69.43%, 318/458). The sensitivities of the three tools for ethambutol and streptomycin resistance were similar, ranging from 76.00% to 81.61%. As to pyrazinamide, the sensitivity of TB Profiler (72.82%, 150/206) was higher than those of Mykrobe (61.65%, 127/206) and PhyResSE (50.97%, 105/206). For fluoroquinolones and amikacin, the sensitivities of PhyResSE were 88.27% (143/162) and 60.00% (27/45), higher than those of TB Profiler (81.48% (132/162) and 48.89% (22/45)) and Mykrobe (82.10% (133/162) and 55.56% (25/45). The specificities of the three tools for detecting drug resistance of anti-tuberculosis drugs were similar and high (higher than 90%), except for ethambutol which was 82.42%-83.88%. Conclusion All of the three bioinformatics tools have a good performance on rapid detection of drug-resistant tuberculosis with a promising prospect of future application. The relatively low sensitivity of the tools to pyrazinamide and some second-line anti-tuberculosis drugs suggests that studies on resistance mechanism of these drugs should be enhanced.
Keywords:Mycobacterium tuberculosis  Genome-wide sequencing  Diagnosis  Drug resistance  
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