ArticleScientific reports2017
Mycobacterium tuberculosis resistance prediction and lineage classification from genome sequencing: comparison of automated analysis tools.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.
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Who cites it
53 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Open-access genomic drug resistance prediction tools forJournal of clinical microbiology · 2026Pooled it
- Systematic Review of Whole-Genome Sequencing Data To Predict Phenotypic Drug Resistance and Susceptibility in Swedish Mycobacterium tuberculosis Isolates, 2016 to 2018.Antimicrobial agents and chemotherapy · 2020Pooled it
- MTBseq-nf: Enabling Scalable Tuberculosis Genomics "Big Data" Analysis Through a User-Friendly Nextflow Wrapper for MTBseq Pipeline.Microorganisms · 2025Article
- Accuracy of SAM-TB Platform as a Diagnostic Assay for Detection of Mycobacterium tuberculosis Drug Resistance Versus TB-Proflier, PhyResSE, and Mykrobe: A Head-to-Head Comparison.Current microbiology · 2025Article
- Feature selection and aggregation for antibiotic resistance GWAS inFrontiers in microbiology · 2025Article
- A Profile of Drug-Resistant Mutations inIndian journal of microbiology · 2024Article
- TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations.BMC genomics · 2024Article
- Use of Whole Genome Sequencing for Mycobacterium tuberculosis Complex Antimicrobial Susceptibility Testing: From Sequence Data to Resistance Profiles.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Direct detection of drug-resistantFrontiers in public health · 2023Article
- Consequences ofAfrican journal of laboratory medicine · 2023Article
- Prevalence and genetic basis of first-line drug resistance ofERJ open research · 2022Article
- Investigating resistance in clinical Mycobacterium tuberculosis complex isolates with genomic and phenotypic antimicrobial susceptibility testing: a multicentre observational study.The Lancet. Microbe · 2022Observational
- Whole genome sequencing analysis to evaluate the influence of T2DM on polymorphisms associated with drug resistance in M. tuberculosis.BMC genomics · 2022Article
- Whole-genome sequencing ofEpidemiology and infection · 2022Article
- Application of Next Generation Sequencing for Diagnosis and Clinical Management of Drug-Resistant Tuberculosis: Updates on Recent Developments in the Field.Frontiers in microbiology · 2022Review
- Comprehensive and accurate genetic variant identification from contaminated and low-coverageMicrobial genomics · 2021Article
- Evaluation of whole-genome sequence data analysis approaches for short- and long-read sequencing ofMicrobial genomics · 2021Article
- Simultaneous Determination of Mycobacterium leprae Drug Resistance and Single-Nucleotide Polymorphism Genotype by Use of Nested Multiplex PCR with Amplicon Sequencing.Journal of clinical microbiology · 2021Article
- INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis.Algorithms for molecular biology : AMB · 2021Article
- Perspectives for systems biology in the management of tuberculosis.European respiratory review : an official journal of the European Respiratory Society · 2021Review
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Authors and funding
5 authors.
Funding
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Abstract
Whole-genome sequencing (WGS) has the potential to accelerate drug-susceptibility testing (DST) to design appropriate regimens for drug-resistant tuberculosis (TB). Several recently developed automated software tools promise to standardize the analysis and interpretation of WGS data. We assessed five tools (CASTB, KvarQ, Mykrobe Predictor TB, PhyResSE, and TBProfiler) with regards to DST and phylogenetic lineage classification, which we compared with phenotypic DST, Sanger sequencing, and traditional typing results for a collection of 91 strains. The lineage classifications by the tools generally only differed in the resolution of the results. However, some strains could not be classified at all and one strain was misclassified. The sensitivities and specificities for isoniazid and rifampicin resistance of the tools were high, whereas the results for ethambutol, pyrazinamide, and streptomycin resistance were more variable. False-susceptible DST results were mainly due to missing mutations in the resistance catalogues that the respective tools employed for data interpretation. Notably, we also found cases of false-resistance because of the misclassification of polymorphisms as resistance mutations. In conclusion, the performance of current WGS analysis tools for DST is highly variable. Sustainable business models and a shared, high-quality catalogue of resistance mutations are needed to ensure the clinical utility of these tools.
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