ArticleBMC bioinformatics2019
Genomic prediction of tuberculosis drug-resistance: benchmarking existing databases and prediction algorithms.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Open-access genomic drug resistance prediction tools forJournal of clinical microbiology · 2026Pooled it
- Diagnostic yield of nine user-friendly bioinformatics tools for predicting Mycobacterium tuberculosis drug resistance: A systematic review and network meta-analysis.PLOS global public health · 2025Article
- A Profile of Drug-Resistant Mutations inIndian journal of microbiology · 2024Article
- Mutational Dynamics Related to Antibiotic Resistance inMicroorganisms · 2024Article
- Discordance Between Phenotypic and WGS-Based Drug Susceptibility Testing Results for Some Anti-Tuberculosis Drugs: A Snapshot Study of PairedInfection and drug resistance · 2024Article
- Genomic, phenotypic and demographic characterization ofFrontiers in cellular and infection microbiology · 2023Article
- Population-based sequencing ofeLife · 2022Article
- Whole genome sequencing-based drug resistance predictions of multidrug-resistantJAC-antimicrobial resistance · 2022Article
- Evaluation of whole-genome sequence data analysis approaches for short- and long-read sequencing ofMicrobial genomics · 2021Article
- INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis.Algorithms for molecular biology : AMB · 2021Article
- Analysis of Factors Influencing Multidrug-Resistant Tuberculosis and Validation of Whole-Genome Sequencing in Children with Drug-Resistant Tuberculosis.Infection and drug resistance · 2021Article
- Identification and Characterization of Mycobacterial Species Using Whole-Genome Sequences.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Tuberculosis in Liberia: high multidrug-resistance burden, transmission and diversity modelled by multiple importation events.Microbial genomics · 2020Article
- Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues.Nature reviews. Microbiology · 2019Review
- Deciphering drug resistance in Mycobacterium tuberculosis using whole-genome sequencing: progress, promise, and challenges.Genome medicine · 2019Review
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIt is possible to predict whether a tuberculosis (TB) patient will fail to respond to specific antibiotics by sequencing the genome of the infecting Mycobacterium tuberculosis (Mtb) and observing whether the pathogen carries specific mutations at drug-resistance sites. This advancement has led to the collation of TB databases such as PATRIC and ReSeqTB that possess both whole genome sequences and drug resistance phenotypes of infecting Mtb isolates. Bioinformatics tools have also been developed to predict drug resistance from whole genome sequencing (WGS) data. Here, we evaluate the performance of four popular tools (TBProfiler, MyKrobe, KvarQ, PhyResSE) with 6746 isolates compiled from publicly available databases, and subsequently identify highly probable phenotyping errors in the databases by genetically predicting the drug phenotypes using all four software.
resultsOur results show that these bioinformatics tools generally perform well in predicting the resistance status for two key first-line agents (isoniazid, rifampicin), but the accuracy is lower for second-line injectables and fluoroquinolones. The error rates in the databases are also non-trivial, reaching as high as 31.1% for prothionamide, and that phenotypes from ReSeqTB are more susceptible to errors.
conclusionsThe good performance of the automated software for drug resistance prediction from TB WGS data shown in this study further substantiates the usefulness and promise of utilising genetic data to accurately profile TB drug resistance, thereby reducing misdiagnoses arising from error-prone culture-based drug susceptibility testing.
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