ArticleBMC bioinformatics2014
MUBII-TB-DB: a database of mutations associated with antibiotic resistance in Mycobacterium tuberculosis.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 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
43 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- Antimicrobial Resistance: Enzymes, Proteins, and Computational Resources.Current pharmaceutical design · 2026Review
- Genomic characterization of XDR Mycobacterium tuberculosis isolates in Argentina (2006-2015).BMC infectious diseases · 2025Article
- ProtAlign-ARG: antibiotic resistance gene characterization integrating protein language models and alignment-based scoring.Scientific reports · 2025Article
- Gut microbiota and tuberculosis.iMeta · 2025Review
- Nanopore sequencing for precise detection ofJournal of clinical microbiology · 2025Article
- Association Between Diabetes Mellitus-Tuberculosis and the Generation of Drug Resistance.Microorganisms · 2024Review
- The Role of Next-Generation Sequencing (NGS) in the Management of Tuberculosis: Practical Review for Implementation in Routine.Pathogens (Basel, Switzerland) · 2023Review
- Investigation of Multi-SubunitInternational journal of molecular sciences · 2023Article
- Review and Comparison of Antimicrobial Resistance Gene Databases.Antibiotics (Basel, Switzerland) · 2022Review
- The Structural Basis ofMolecules (Basel, Switzerland) · 2022Article
- An anti-tuberculosis compound screen using a zebrafish infection model identifies an aspartyl-tRNA synthetase inhibitor.Disease models & mechanisms · 2021Article
- A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria.The Journal of antibiotics · 2021Review
- A review of the resistome within the digestive tract of livestock.Journal of animal science and biotechnology · 2021Review
- Computational modeling and bioinformatic analyses of functional mutations in drug target genes inComputational and structural biotechnology journal · 2021Article
- MycoVarP: Mycobacterium Variant and Drug Resistance Prediction Pipeline for Whole-Genome Sequence Data Analysis.Frontiers in bioinformatics · 2021Article
- Antibiotic Resistance: One Health One World Outlook.Frontiers in cellular and infection microbiology · 2021Review
- Antibiotic resistance: Time of synthesis in a post-genomic age.Computational and structural biotechnology journal · 2021Review
- Innovative and rapid antimicrobial susceptibility testing systems.Nature reviews. Microbiology · 2020Review
- Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study.Microbial genomics · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTuberculosis is an infectious bacterial disease caused by Mycobacterium tuberculosis. It remains a major health threat, killing over one million people every year worldwide. An early antibiotic therapy is the basis of the treatment, and the emergence and spread of multidrug and extensively drug-resistant mutant strains raise significant challenges. As these bacteria grow very slowly, drug resistance mutations are currently detected using molecular biology techniques. Resistance mutations are identified by sequencing the resistance-linked genes followed by a comparison with the literature data. The only online database is the TB Drug Resistance Mutation database (TBDReaM database); however, it requires mutation detection before use, and its interrogation is complex due to its loose syntax and grammar. DESCRIPTION: The MUBII-TB-DB database is a simple, highly structured text-based database that contains a set of Mycobacterium tuberculosis mutations (DNA and proteins) occurring at seven loci: rpoB, pncA, katG; mabA(fabG1)-inhA, gyrA, gyrB, and rrs. Resistance mutation data were extracted after the systematic review of MEDLINE referenced publications before March 2013. MUBII analyzes the query sequence obtained by PCR-sequencing using two parallel strategies: i) a BLAST search against a set of previously reconstructed mutated sequences and ii) the alignment of the query sequences (DNA and its protein translation) with the wild-type sequences. The post-treatment includes the extraction of the aligned sequences together with their descriptors (position and nature of mutations). The whole procedure is performed using the internet. The results are graphs (alignments) and text (description of the mutation, therapeutic significance). The system is quick and easy to use, even for technicians without bioinformatics training.
conclusionMUBII-TB-DB is a structured database of the mutations occurring at seven loci of major therapeutic value in tuberculosis management. Moreover, the system provides interpretation of the mutations in biological and therapeutic terms and can evolve by the addition of newly described mutations. Its goal is to provide easy and comprehensive access through a client-server model over the Web to an up-to-date database of mutations that lead to the resistance of M. tuberculosis to antibiotics.
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Registered trials
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