ArticleMicrobial genomics2018
MentaLiST - A fast MLST caller for large MLST schemes.
Article in Microbial genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 citing papers in PubMed.
- Short-Term Within-Host Genomic Diversity and Clone Turnover of Carbapenem-ResistantAntibiotics (Basel, Switzerland) · 2026Article
- Study Protocol for Genomic Epidemiology Investigation of Intensive Care Unit Patient Colonization by Antimicrobial-Resistant ESKAPE Pathogens.Methods and protocols · 2025Article
- MiST: rapid, accurate and flexible (core-genome) multi-locus sequence typing (MLST) allele calling from draft genomes.BMC genomics · 2025Article
- Genomic typing, antimicrobial resistance gene, virulence factor and plasmid replicon database for the important pathogenic bacteria Staphylococcus aureus.BMC genomic data · 2025Article
- Genomic Epidemiology of Vancomycin-ResistantAntibiotics (Basel, Switzerland) · 2025Article
- Multi-country and intersectoral assessment of cluster congruence between pipelines for genomics surveillance of foodborne pathogens.Nature communications · 2025Article
- Genomic Functional Analysis and Cellulase Characterization for the Enzyme-Producing StrainMicroorganisms · 2025Article
- Genomic typing, antimicrobial resistance gene, virulence factor and plasmid replicon database for the important pathogenic bacteria Klebsiella pneumoniae.BMC microbiology · 2025Article
- Virulence plasmids in edema disease: Insights from whole-genome analysis of porcine O139:H1 Shiga toxin-producingFrontiers in cellular and infection microbiology · 2025Article
- Unraveling the impact of genome assembly on bacterial typing: a one health perspective.BMC genomics · 2024Article
- Genotypic characterization and antimicrobial susceptibility of humanMicrobiology spectrum · 2024Article
- The integrated genomic surveillance system of Andalusia (SIEGA) provides a One Health regional resource connected with the clinic.Scientific reports · 2024Article
- Multi-locus sequence typing, antimicrobials resistance and virulence profiles of Salmonella enterica isolated from bovine carcasses in Minas Gerais state, Brazil.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2024Article
- Introduction and benchmarking of pyMLST: open-source software for assessing bacterial clonality using core genome MLST.Microbial genomics · 2023Article
- Comparative genomic assessment of members of genus Tenacibaculum: an exploratory study.Molecular genetics and genomics : MGG · 2023Review
- International Clones of High Risk of Acinetobacter Baumannii-Definitions, History, Properties and Perspectives.Microorganisms · 2023Review
- Whole-Genome Sequencing Revealed the Fusion Plasmids Capable of Transmission and Acquisition of Both Antimicrobial Resistance and Hypervirulence Determinants in Multidrug-ResistantMicroorganisms · 2023Article
- Plasmid Composition, Antimicrobial Resistance and Virulence Genes Profiles of Ciprofloxacin- and Third-Generation Cephalosporin-Resistant FoodborneMicroorganisms · 2023Article
- IRIDA-ARIES Genomics, a key player in the One Health surveillance of diseases caused by infectious agents in Italy.Frontiers in public health · 2023Article
- Development and validation of a random forest algorithm for source attribution of animal and humanFrontiers in microbiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MLST (multi-locus sequence typing) is a classic technique for genotyping bacteria, widely applied for pathogen outbreak surveillance. Traditionally, MLST is based on identifying sequence types from a small number of housekeeping genes. With the increasing availability of whole-genome sequencing data, MLST methods have evolved towards larger typing schemes, based on a few hundred genes [core genome MLST (cgMLST)] to a few thousand genes [whole genome MLST (wgMLST)]. Such large-scale MLST schemes have been shown to provide a finer resolution and are increasingly used in various contexts such as hospital outbreaks or foodborne pathogen outbreaks. This methodological shift raises new computational challenges, especially given the large size of the schemes involved. Very few available MLST callers are currently capable of dealing with large MLST schemes. We introduce MentaLiST, a new MLST caller, based on a k-mer voting algorithm and written in the Julia language, specifically designed and implemented to handle large typing schemes. We test it on real and simulated data to show that MentaLiST is faster than any other available MLST caller while providing the same or better accuracy, and is capable of dealing with MLST schemes with up to thousands of genes while requiring limited computational resources. MentaLiST source code and easy installation instructions using a Conda package are available at https://github.com/WGS-TB/MentaLiST.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.