ArticleJournal of clinical microbiology2015
PhyResSE: a Web Tool Delineating Mycobacterium tuberculosis Antibiotic Resistance and Lineage from Whole-Genome Sequencing Data.
Article in Journal of clinical microbiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 161 papers, 1 of them a synthesis that pooled it.
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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
161 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Open-access genomic drug resistance prediction tools forJournal of clinical microbiology · 2026Pooled it
- Targeted next-generation sequencing of sputum for diagnosis of drug-resistant TB: results of a national survey in Democratic Republic of the Congo.Scientific reports · 2020Trial
- Algorithmic Determinants of Performance Heterogeneity in Whole-Genome Sequencing-Based Prediction of Drug Resistance inMicroorganisms · 2026Review
- Machine learning method for the prediction of Bedaquiline-resistantLife science alliance · 2026Article
- Identification and drug resistance profiling ofMicrobiology spectrum · 2026Article
- Tuberculosis diagnosis and the complete drug resistance pattern from a single sample within a single day by use of a composite platform of MAX MDR-TB and AmPORE-TB.Journal of clinical microbiology · 2026Article
- Application of FreezeTB, a targeted nanopore sequencing assay, for identification of drug resistance and lineages among pulmonary tuberculosis cases in Alaska.Microbiology spectrum · 2026Article
- Genomic landscape ofNew microbes and new infections · 2025Article
- An explainable artificial intelligence framework reveals mutations associated with drug resistance inBiosafety and health · 2025Article
- Genomic characterization and epidemiology of Mycobacterium tuberculosis lineage 2 isolates from Kazakhstan.Scientific reports · 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
- Reimagining Tuberculosis Control in the Era of Genomics: The Case for Global Investment inPathogens (Basel, Switzerland) · 2025Review
- Prediction of antimicrobial resistance inMicrobiology spectrum · 2025Article
- Evaluation of the role ofMicrobiology spectrum · 2025Article
- Advantages of updated WHO mutation catalog combined with existing whole-genome sequencing-based approaches for Mycobacterium tuberculosis resistance prediction.Genome medicine · 2025Article
- Performance of a broth microdilution assay for routine minimum inhibitory concentration determination of 14 anti-tuberculous drugs against theAntimicrobial agents and chemotherapy · 2025Article
- Molecular typing of Mycobacterium tuberculosis: a review of current methods, databases, softwares, and analytical tools.FEMS microbiology reviews · 2025Review
- 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
- Machine learning-based approach for identification of new resistance associated mutations from whole genome sequences ofBioinformatics advances · 2025Article
- Article
101 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibiotic-resistant tuberculosis poses a global threat, causing the deaths of hundreds of thousands of people annually. While whole-genome sequencing (WGS), with its unprecedented level of detail, promises to play an increasingly important role in diagnosis, data analysis is a daunting challenge. Here, we present a simple-to-use web service (free for academic use at http://phyresse.org). Delineating both lineage and resistance, it provides state-of-the-art methodology to life scientists and physicians untrained in bioinformatics. It combines elaborate data processing and quality control, as befits human diagnostics, with a treasure trove of validated resistance data collected from well-characterized samples in-house and worldwide.
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