ArticleBriefings in bioinformatics2022
SAM-TB: a whole genome sequencing data analysis website for detection of Mycobacterium tuberculosis drug resistance and transmission.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 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
- ComparativeMicrobiology spectrum · 2026Article
- First insight of characteristics and prediction of Mycobacterium tuberculosis drug resistance by whole genome sequencing in Fujian Province, China.Scientific reports · 2026Article
- Targeted Next-Generation Sequencing in Drug-Resistant Tuberculosis: WHO Guidance and Practical Implementation Priorities.Biomedicines · 2026Review
- A hospital-based genomic analysis reveals predominance of ST215Frontiers in microbiology · 2026Article
- Fluoroquinolone resistance and mutation profiles inFrontiers in public health · 2026Article
- An explainable artificial intelligence framework reveals mutations associated with drug resistance inBiosafety and health · 2025Article
- First insights into the genetic composition ofJournal of clinical tuberculosis and other mycobacterial diseases · 2025Article
- Whole-genome sequencing for analyzing the transmission characteristics of drug-resistant Mycobacterium tuberculosis in Ganzhou, China.BMC infectious diseases · 2025Article
- mlstverse-web: advancing real-time identification of mycobacteria from sputum using targeted sequencing NALC-Seq.Functional & integrative genomics · 2025Article
- Clinical characteristics and genomic epidemiological survey of tuberculosis in Wuzhou, China, 2022.Microbiology spectrum · 2025Article
- Advantages of updated WHO mutation catalog combined with existing whole-genome sequencing-based approaches for Mycobacterium tuberculosis resistance prediction.Genome medicine · 2025Article
- Global epidemiology of Mycobacterium tuberculosis lineage 4 insights from Ecuadorian genomic data.Scientific reports · 2025Article
- Decoding EcuadorianBiomedicines · 2025Article
- Molecular typing of Mycobacterium tuberculosis: a review of current methods, databases, softwares, and analytical tools.FEMS microbiology reviews · 2025Review
- Machine learning-based approach for identification of new resistance associated mutations from whole genome sequences ofBioinformatics advances · 2025Article
- Cross-District Transmission of Tuberculosis in a High-Mobility City in China: Implications for Regional Collaboration in Infectious Disease Control.Infection and drug resistance · 2025Article
- A duplex one-step recombinase aided PCR assay for the rapid and sensitive detection of the isoniazid resistance genes katG and inhA inFrontiers in microbiology · 2025Article
- Diagnosis of nontuberculous mycobacterial infections using genomics and artificial intelligence-machine learning approaches: scope, progress and challenges.Frontiers in microbiology · 2025Review
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12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Whole genome sequencing (WGS) can provide insight into drug-resistance, transmission chains and the identification of outbreaks, but data analysis remains an obstacle to its routine clinical use. Although several drug-resistance prediction tools have appeared, until now no website integrates drug-resistance prediction with strain genetic relationships and species identification of nontuberculous mycobacteria (NTM). We have established a free, function-rich, user-friendly online platform for MTB WGS data analysis (SAM-TB, http://samtb.szmbzx.com) that integrates drug-resistance prediction for 17 antituberculosis drugs, detection of variants, analysis of genetic relationships and NTM species identification. The accuracy of SAM-TB in predicting drug-resistance was assessed using 3177 sequenced clinical isolates with results of phenotypic drug-susceptibility tests (pDST). Compared to pDST, the sensitivity of SAM-TB for detecting multidrug-resistant tuberculosis was 93.9% [95% confidence interval (CI) 92.6-95.1%] with specificity of 96.2% (95% CI 95.2-97.1%). SAM-TB also analyzes the genetic relationships between multiple strains by reconstructing phylogenetic trees and calculating pairwise single nucleotide polymorphism (SNP) distances to identify genomic clusters. The incorporated mlstverse software identifies NTM species with an accuracy of 98.2% and Kraken2 software can detect mixed MTB and NTM samples. SAM-TB also has the capacity to share both sequence data and analysis between users. SAM-TB is a multifunctional integrated website that uses WGS raw data to accurately predict antituberculosis drug-resistance profiles, analyze genetic relationships between multiple strains and identify NTM species and mixed samples containing both NTM and MTB. SAM-TB is a useful tool for guiding both treatment and epidemiological investigation.
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