ArticlePLOS global public health2025
Diagnostic yield of nine user-friendly bioinformatics tools for predicting Mycobacterium tuberculosis drug resistance: A systematic review and network meta-analysis.
Article in PLOS global public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Open-access genomic drug resistance prediction tools forJournal of clinical microbiology · 2026Pooled it
- Algorithmic Determinants of Performance Heterogeneity in Whole-Genome Sequencing-Based Prediction of Drug Resistance inMicroorganisms · 2026Review
- First-line drug-resistant tuberculosis among children under 15 years in Ethiopia: insights from phenotypic and whole-genome sequencing approaches.BMC infectious diseases · 2026Article
- Targeted Next-Generation Sequencing in Drug-Resistant Tuberculosis: WHO Guidance and Practical Implementation Priorities.Biomedicines · 2026Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To compare the diagnostic yield of various bioinformatics tools for predicting Mycobacterium tuberculosis drug resistance. A systematic review of PubMed, Embase, Scopus, Web of Science, CINAHL and the Cochrane Library was performed to identify studies reporting the effectiveness of bioinformatic tools for predicting resistance to anti-tuberculosis (TB) drugs. Data were collected and pooled using random-effects meta-analysis and Bayesian network meta-analysis (NMA). Summary receiver operating characteristic curves (SROCs) analysis were performed, and superiority index (SI) and area under the curve (AUC) were calculated. Thirty-three studies evaluated 9 different bioinformatics tools for predicting resistance to 14 anti-TB drugs. NMA and SROCs demonstrated that TBProfiler, TGS-TB, Mykrobe, PhyResSE, and SAM-TB all exhibited satisfactory performance. Remarkably, TBProfiler stood out with its exceptional ability to predict resistance to the majority of anti-TB drugs, including isoniazid (SI: 3.39 [95% confidence interval (CI): 0.20, 11.00]; AUC: 0.97 [0.95, 0.98]), rifampicin (SI: 6.38 [0.60, 15.00]; AUC: 0.99 [0.98, 1.00]), ethambutol (SI: 5.15 [0.60, 13.00]; AUC: 0.96 [0.94, 0.97]), streptomycin (SI: 3.67 [0.60, 11.00]; AUC: 0.97 [0.95, 0.98], amikacin (SI: 2.49 [0.14, 11.00]; AUC: 0.97 [0.96, 0.99]), kanamycin (SI: 2.26 [0.14, 9.00]; AUC: 0.98 [0.97, 0.99]), levofloxacin (SI: 1.87 [0.11, 9.00]; AUC: 0.95 [0.93, 0.97]), and prothionamide (SI: 2.73 [0.20, 7.00]; AUC: 0.87 [0.84, 0.90]). Meanwhile, Mykrobe demonstrated superior accuracy specifically for moxifloxacin (SI: 3.96 [0.11, 13.00]; AUC: 0.97 [0.95, 0.98]). Lastly, TGS-TB had the best efficacy in predicting resistance to pyrazinamide (SI: 12.53 [1.67, 17.00]; AUC: 0.97 [0.95, 0.98]), capreomycin (SI: 4.22 [0.08, 15.00]; AUC: 1.00 [0.98, 1.00]), and ethionamide (SI: 2.15 [0.33, 7.00]; AUC: 0.96 [0.94, 0.98]). TBProfiler, TGS-TB, Mykrobe, PhyResSE and SAM-TB have all demonstrated outstanding accuracy in predicting resistance to anti-TB drugs. In particular, TBProfiler stood out for its exceptional performance in predicting resistance to most anti-TB drugs, while TGS-TB excelled in predicting resistance to pyrazinamide and certain second-line drugs. The efficacy of SAM-TB requires further investigation to fully establish its reliability and effectiveness. To ensure the accuracy and reliability of genotypic drug susceptibility testing, bioinformatics tools should be refined and adapted continuously to accommodate novel and current resistance-associated mutations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.