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Molecular Typing

Genetics and Genomics · NLM MeSH · updated 2026-10-29 · version 1

Citations verified against PubMedNot yet clinician-reviewed10 referencesApproximately 4 min read

Using molecular biology techniques, such as dna sequence analysis; pulsed-field gel electrophoresis; and dna fingerprinting, to identify, classify, and compare organisms and their subtypes.

Table of Contents

1. Definition2. Research landscape3. Reading the evidence4. Open-access studies5. Key reviews6. References7. About this article

Definition and research guide

This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.

Content available: 4 complete abstracts (785 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.

Definition

Using molecular biology techniques, such as dna sequence analysis; pulsed-field gel electrophoresis; and dna fingerprinting, to identify, classify, and compare organisms and their subtypes.

Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.

Also known as: Typing, Molecular.

Where it fits in the MeSH classification

Broader topics
Bacterial Typing Techniques; Genetic Techniques
Narrower topics
Multilocus Sequence Typing; DNA Barcoding, Taxonomic
See also
Molecular Epidemiology

Related concepts

  • Molecular Biology
  • Dna Sequence Analysis
  • Pulsed-Field Gel Electrophoresis
  • DNA Fingerprinting

Research landscape

PubMed indexes 22,659 records tagged with the MeSH heading “Molecular Typing” (counts retrieved 2026-10-29). 7,456 of them (32.9%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 15,416 records.

Reviews account for 713 records (3.1%) and meta-analyses for 28. Randomized controlled trials number 32 (0.1%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.

PubMed records by publication periodthrough 1999: 0 records; 2000-2009: 27 records; 2010-2019: 15,416 records; 2020 onward: 7,456 records0through 1999272000-200915,4162010-20197,4562020 onward
Records in PubMed indexed with this heading, by publication period. Real counts from NCBI E-utilities; the final period covers only part of the current year. Volume reflects how much has been published, not how strong the evidence is.
Show the data
PeriodRecords
through 19990
2000-200927
2010-201915,416
2020 onward7,456
Share of records by publication typeReviews: 3.1 percent; Meta-analyses: 0.1 percent; Randomized controlled trials: 0.1 percentReviews3.1% (713)Meta-analyses0.1% (28)Randomized controlled trials0.1% (32)
Share of all PubMed records on this heading that carry each publication type. Types overlap with other records, so the shares do not sum to 100%.
Publication-type chart data
Publication types among 22,659 PubMed records
TypeRecordsShare
Reviews7133.1%
Meta-analyses280.1%
Randomized controlled trials320.1%

Method: PubMed query "Molecular Typing"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.

Reading the evidence in genetics and genomics

Genetic findings vary widely in strength. A variant linked to a condition in one study may not replicate in other ancestries, and most common variants have small effects that are meaningful only in aggregate. Studies of rare, high-impact variants can support strong causal claims about a gene, while association studies describe statistical links that need functional and independent replication. Check the sample size, the ancestry of participants, whether results were replicated, and whether a laboratory or clinical outcome was actually measured.

Evidence from open-access studies

These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.

Study comparison — selected abstracts, not a systematic review
Study / authorsYearSource label / designEurope PMC citationsLicence / source
Investigation on the mechanisms of carbapenem resistance among the non-carbapenemase-producing carbapenem-resistant Klebsiella pneumoniae
Lee YQ, Sri La Sri Ponnampalavanar S, Wong JH, Kong ZX, Ngoi ST, Karunakaran R, et al.
2024Research article
Design not recorded in structured metadata
15cc by
PMID 39359938 · DOI · PMC11445613
Observation Bias in Metabarcoding
Shaffer MR, Andruszkiewicz Allan E, Van Cise AM, Parsons KM, Shelton AO, Kelly RP
2025Research article
Design not recorded in structured metadata
13cc by
PMID 40375355 · DOI · PMC12415808
Molecular typing of Mycobacterium tuberculosis: a review of current methods, databases, softwares, and analytical tools
Couvin D, Allaguy AS, Ez-Zari A, Jagielski T, Rastogi N
2025Review article
Design not recorded in structured metadata
12cc by
PMID 40287399 · DOI · PMC12065434
Quantifying host-microbe interactions with bacterial lineage tracing
Campbell IW, Hullahalli K, Waldor MK
2026Review article
Design not recorded in structured metadata
4cc by
PMID 41477882 · DOI · PMC12854280

Investigation on the mechanisms of carbapenem resistance among the non-carbapenemase-producing carbapenem-resistant Klebsiella pneumoniae [1]

Research article Lee YQ, Sri La Sri Ponnampalavanar S, Wong JH, Kong ZX, Ngoi ST, Karunakaran R, et al. · · 2024 · cited 15 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
BackgroundIn Malaysia, an increase in non-carbapenemase-producing carbapenem-resistant Klebsiella pneumoniae (NC-CRKP) has been observed over the years.
Closing excerpt
Our findings highlighted the significance of implementing whole-genome sequencing into clinical practice to promote the surveillance of carbapenem resistance mechanisms among NC-CRKP.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (247 words)

BackgroundIn Malaysia, an increase in non-carbapenemase-producing carbapenem-resistant Klebsiella pneumoniae (NC-CRKP) has been observed over the years. Previously, four NC-CRKP with increased susceptibility to ciprofloxacin in the presence of phenylalanine-arginine β-naphthylamide (PAβN) were identified. However, no contribution of the PAβN-inhibited efflux pump to carbapenem resistance was observed. All four NC-CRKP harboured non-carbapenemase β-lactamase, with two also exhibiting porin loss. In this study, we further investigated the genomic features and resistance mechanisms of these four isolates.MethodsAll four NC-CRKP were subjected to whole-genome sequencing, followed by comparative genomic and phylogenetic analyses.ResultsMulti-locus sequence typing (MLST) analysis divided the four NC-CRKP into different sequence types: ST392, ST45, ST14, and ST5947. Neither major nor rare carbapenemase genes were detected. Given the presence of non-carbapenemase β-lactamase in all isolates, we further investigated the potential mechanisms of resistance by identifying related chromosomal mutations. Deletion mutation was detected in the cation efflux system protein CusF. Insertion mutation was identified in the nickel/cobalt efflux protein RcnA. Missense mutation of ompK36 porin was detected in two isolates, while the loss of ompK36 porin was observed in another two isolates.ConclusionsThis study revealed that NC-CRKP may confer carbapenem resistance through a combination of non-carbapenemase β-lactamase and potential chromosomal mutations including missense mutation or loss of ompK36 porin and/or a frameshift missense mutation in efflux pump systems, such as cation efflux system protein CusF and nickel/cobalt efflux protein RcnA. Our findings highlighted the significance of implementing whole-genome sequencing into clinical practice to promote the surveillance of carbapenem resistance mechanisms among NC-CRKP.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 39359938 · DOI · PMC11445613

Observation Bias in Metabarcoding [2]

Research article Shaffer MR, Andruszkiewicz Allan E, Van Cise AM, Parsons KM, Shelton AO, Kelly RP · · 2025 · cited 13 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
DNA metabarcoding is subject to observation bias associated with PCR and sequencing, which can result in observed read proportions differing from actual species proportions in the DNA extract.
Closing excerpt
These findings identify key factors that should be considered when designing studies that aim to apply metabarcoding data quantitatively.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (239 words)

DNA metabarcoding is subject to observation bias associated with PCR and sequencing, which can result in observed read proportions differing from actual species proportions in the DNA extract. Here, we amplify and sequence a mock community of known composition containing marine fishes and cetaceans using four different primer sets and a variety of PCR conditions. We first compare metabarcoding observations to two different sets of expected species proportions based on total genomic DNA and on target mitochondrial template DNA. We find that calibrating observed read proportions based on template DNA concentration is most appropriate as it isolates PCR amplification bias; calibration with total genomic DNA results in bias that can be attributed to both PCR amplification bias and differing ratios of template to total genomic DNA. We then model the remaining amplification bias and find that approximately 60% can be explained by inherent species-specific DNA characteristics. These include primer-template mismatches, amplicon fragment length, and GC content, which vary somewhat across Taq polymerases. Finally, we investigate how different PCR protocols influence community composition regardless of expected proportions and find that changing protocols most strongly influence the amplification of templates with primer mismatches. Our findings suggest that using primer-template pairs without mismatches and targeting a narrow taxonomic group can yield more repeatable and accurate estimates of species' true, underlying DNA template proportions. These findings identify key factors that should be considered when designing studies that aim to apply metabarcoding data quantitatively.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 40375355 · DOI · PMC12415808

Molecular typing of Mycobacterium tuberculosis: a review of current methods, databases, softwares, and analytical tools [3]

Review article Couvin D, Allaguy AS, Ez-Zari A, Jagielski T, Rastogi N · · 2025 · cited 12 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
Studies on the epidemiology and clinical relevance of Mycobacterium tuberculosis complex (MTBC) have immensely benefited from molecular typing methods, associated software applications, and bioinformatics tools.
Closing excerpt
A comprehensive comparison of available tools and databases is provided to guide future research on the epidemiology of TB and pathogen-associated variables (drug resistance or virulence) and public health initiatives.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (180 words)

Studies on the epidemiology and clinical relevance of Mycobacterium tuberculosis complex (MTBC) have immensely benefited from molecular typing methods, associated software applications, and bioinformatics tools. Over the last two decades, the Pasteur Institute of Guadeloupe has developed a range of bioinformatic resources, including databases and software, to advance understanding of TB epidemiology. Traditional methods, such as IS6110-RFLP, MIRU-VNTR typing, and spoligotyping, have been instrumental but are increasingly supplanted by more precise and high-throughput techniques. These typing methods offer relatively good discrimination and reproducibility, making them popular choices for epidemiological studies. However, the advent of whole-genome sequencing (WGS) has revolutionized Mycobacterium tuberculosis complex (MTBC) typing, providing unparalleled resolution and data analysis depth. WGS enables the identification of single nucleotide polymorphisms and other genetic variations, facilitating robust phylogenetic reconstructions, and detailed outbreak investigations. This review summarizes current molecular typing methods, as well as databases and software tools used for MTBC data analysis. A comprehensive comparison of available tools and databases is provided to guide future research on the epidemiology of TB and pathogen-associated variables (drug resistance or virulence) and public health initiatives.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 40287399 · DOI · PMC12065434

Quantifying host-microbe interactions with bacterial lineage tracing [4]

Review article Campbell IW, Hullahalli K, Waldor MK · · 2026 · cited 4 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
Using genomic barcodes to trace bacterial lineages within a host reveals previously unobservable dynamics of infection, including the impact of infection bottlenecks, routes of bacterial dissemination, and patterns of within-host evolution.
Closing excerpt
Collectively, barcoding studies quantify the hidden parameters that underlie bacterial colonization and create a quantitative framework for modeling and preventing infectious disease.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (119 words)

Using genomic barcodes to trace bacterial lineages within a host reveals previously unobservable dynamics of infection, including the impact of infection bottlenecks, routes of bacterial dissemination, and patterns of within-host evolution. Barcoding introduces trackable diversity to otherwise isogenic bacterial populations. Comparing the barcodes within an inoculum to those within the host quantifies the "founding population," which reveals the magnitude of population collapse caused by host bottlenecks. Furthermore, comparisons of the founders between tissues can reveal the patterns of pathogen dissemination. On longer timescales, the emergence of dominant barcoded lineages can also be used to detect within-host evolution. Collectively, barcoding studies quantify the hidden parameters that underlie bacterial colonization and create a quantitative framework for modeling and preventing infectious disease.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41477882 · DOI · PMC12854280

Key reviews and meta-analyses

The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.

  • Research article Environmental DNA metabarcoding: Transforming how we survey animal and plant communities [5]
    Deiner K, Bik HM, Mächler E, Seymour M, Lacoursière-Roussel A, Altermatt F, et al. · Mol Ecol · 2017 · cited 866 times (Europe PMC)
  • Research article Multiresistant Gram-negative bacteria: the role of high-risk clones in the dissemination of antibiotic resistance [6]
    Woodford N, Turton JF, Livermore DM · FEMS Microbiol Rev · 2011 · cited 650 times (Europe PMC)
  • Research article Choosing and using a plant DNA barcode [7]
    Hollingsworth PM, Graham SW, Little DP · PLoS One · 2011 · cited 622 times (Europe PMC)
  • Research article Plant DNA barcoding: from gene to genome [8]
    Li X, Yang Y, Henry RJ, Rossetto M, Wang Y, Chen S · Biol Rev Camb Philos Soc · 2015 · cited 500 times (Europe PMC)
  • Research article Experimental and analytical tools for studying the human microbiome [9]
    Kuczynski J, Lauber CL, Walters WA, Parfrey LW, Clemente JC, Gevers D, et al. · Nat Rev Genet · 2011 · cited 500 times (Europe PMC)
  • Research article Global evolution of multidrug-resistant Acinetobacter baumannii clonal lineages [10]
    Zarrilli R, Pournaras S, Giannouli M, Tsakris A · Int J Antimicrob Agents · 2013 · cited 385 times (Europe PMC)

References

  1. Lee YQ, Sri La Sri Ponnampalavanar S, Wong JH, Kong ZX, Ngoi ST, Karunakaran R, et al.. Investigation on the mechanisms of carbapenem resistance among the non-carbapenemase-producing carbapenem-resistant Klebsiella pneumoniae. . 2024. PMID 39359938 · DOI · PMC11445613
  2. Shaffer MR, Andruszkiewicz Allan E, Van Cise AM, Parsons KM, Shelton AO, Kelly RP. Observation Bias in Metabarcoding. . 2025. PMID 40375355 · DOI · PMC12415808
  3. Couvin D, Allaguy AS, Ez-Zari A, Jagielski T, Rastogi N. Molecular typing of Mycobacterium tuberculosis: a review of current methods, databases, softwares, and analytical tools. . 2025. PMID 40287399 · DOI · PMC12065434
  4. Campbell IW, Hullahalli K, Waldor MK. Quantifying host-microbe interactions with bacterial lineage tracing. . 2026. PMID 41477882 · DOI · PMC12854280
  5. Deiner K, Bik HM, Mächler E, Seymour M, Lacoursière-Roussel A, Altermatt F, et al.. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol Ecol. 2017. PMID 28921802 · DOI
  6. Woodford N, Turton JF, Livermore DM. Multiresistant Gram-negative bacteria: the role of high-risk clones in the dissemination of antibiotic resistance. FEMS Microbiol Rev. 2011. PMID 21303394 · DOI
  7. Hollingsworth PM, Graham SW, Little DP. Choosing and using a plant DNA barcode. PLoS One. 2011. PMID 21637336 · DOI · PMC3102656
  8. Li X, Yang Y, Henry RJ, Rossetto M, Wang Y, Chen S. Plant DNA barcoding: from gene to genome. Biol Rev Camb Philos Soc. 2015. PMID 24666563 · DOI
  9. Kuczynski J, Lauber CL, Walters WA, Parfrey LW, Clemente JC, Gevers D, et al.. Experimental and analytical tools for studying the human microbiome. Nat Rev Genet. 2011. PMID 22179717 · DOI · PMC5119550
  10. Zarrilli R, Pournaras S, Giannouli M, Tsakris A. Global evolution of multidrug-resistant Acinetobacter baumannii clonal lineages. Int J Antimicrob Agents. 2013. PMID 23127486 · DOI

About this article

Sources
NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
Verification
Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
Review state
automated source verified; no human expert review has been recorded yet.
Version
1, last updated 2026-10-29.

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At a glance

22,659
PubMed records on this heading
33%
published since 2020
713
review articles
32
randomized trials
10
verified references below

Counts come from PubMed on 2026-10-29; they show how much has been written, not how strong the evidence is.

Related topics See all →

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  • Cytogenetic AnalysisGenetics and Genomics
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  • In Situ HybridizationGenetics and Genomics

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