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Kim D, Shin JI, Yoo IY, Jo S, Chu J, Cho WY, Shin SH, Chung YJ, Park YJ, Jung SH. GenoMycAnalyzer: a web-based tool for species and drug resistance prediction for Mycobacterium genomes. BMC Genomics 2024; 25:387. [PMID: 38643090 PMCID: PMC11031912 DOI: 10.1186/s12864-024-10320-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/17/2024] [Indexed: 04/22/2024] Open
Abstract
BACKGROUND Drug-resistant tuberculosis (TB) is a major threat to global public health. Whole-genome sequencing (WGS) is a useful tool for species identification and drug resistance prediction, and many clinical laboratories are transitioning to WGS as a routine diagnostic tool. However, user-friendly and high-confidence automated bioinformatics tools are needed to rapidly identify M. tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM), detect drug resistance, and further guide treatment options. RESULTS We developed GenoMycAnalyzer, a web-based software that integrates functions for identifying MTBC and NTM species, lineage and spoligotype prediction, variant calling, annotation, drug-resistance determination, and data visualization. The accuracy of GenoMycAnalyzer for genotypic drug susceptibility testing (gDST) was evaluated using 5,473 MTBC isolates that underwent phenotypic DST (pDST). The GenoMycAnalyzer database was built to predict the gDST for 15 antituberculosis drugs using the World Health Organization mutational catalogue. Compared to pDST, the sensitivity of drug susceptibilities by the GenoMycAnalyzer for first-line drugs ranged from 95.9% for rifampicin (95% CI 94.8-96.7%) to 79.6% for pyrazinamide (95% CI 76.9-82.2%), whereas those for second-line drugs ranged from 98.2% for levofloxacin (95% CI 90.1-100.0%) to 74.9% for capreomycin (95% CI 69.3-80.0%). Notably, the integration of large deletions of the four resistance-conferring genes increased gDST sensitivity. The specificity of drug susceptibilities by the GenoMycAnalyzer ranged from 98.7% for amikacin (95% CI 97.8-99.3%) to 79.5% for ethionamide (95% CI 76.4-82.3%). The incorporated Kraken2 software identified 1,284 mycobacterial species with an accuracy of 98.8%. GenoMycAnalyzer also perfectly predicted lineages for 1,935 MTBC and spoligotypes for 54 MTBC. CONCLUSIONS GenoMycAnalyzer offers both web-based and graphical user interfaces, which can help biologists with limited access to high-performance computing systems or limited bioinformatics skills. By streamlining the interpretation of WGS data, the GenoMycAnalyzer has the potential to significantly impact TB management and contribute to global efforts to combat this infectious disease. GenoMycAnalyzer is available at http://www.mycochase.org .
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Affiliation(s)
- Doyoung Kim
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jeong-Ih Shin
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Integrated Research Center for Genomic Polymorphism, Precision Medicine Research Center, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - In Young Yoo
- Department of Laboratory Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sungjin Jo
- Department of Laboratory Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jiyon Chu
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | | | | | - Yeun-Jun Chung
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Integrated Research Center for Genomic Polymorphism, Precision Medicine Research Center, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Departments of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yeon-Joon Park
- Department of Laboratory Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Seung-Hyun Jung
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea.
- Integrated Research Center for Genomic Polymorphism, Precision Medicine Research Center, College of Medicine, The Catholic University of Korea, Seoul, Korea.
- Departments of Biochemistry, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seoch-Gu, Seoul, 06591, Republic of Korea.
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