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 (595 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
A technique for identifying individuals of a species that is based on the uniqueness of their DNA sequence. Uniqueness is determined by identifying which combination of allelic variations occur in the individual at a statistically relevant number of different loci. In forensic studies, restriction fragment length polymorphism of multiple, highly polymorphic vntr loci or microsatellite repeat loci are analyzed. The number of loci used for the profile depends on the allele frequency in the population.
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: DNA Fingerprintings; Fingerprinting, DNA; Fingerprintings, DNA; DNA Profiling; DNA Profilings; Profiling, DNA; DNA Typing; DNA Typings.
Where it fits in the MeSH classification
- Broader topics
- Biometric Identification; Genetic Techniques; Forensic Medicine
- Narrower topics
- Amplified Fragment Length Polymorphism Analysis; DNA Contamination; Ribotyping
- See also
- Polymorphism, Restriction Fragment Length; Minisatellite Repeats; Databases, Nucleic Acid; Genetic Privacy; Genotyping Techniques
Related concepts
- Restriction Fragment Length Polymorphism
- Vntr Loci
- Microsatellite Repeat
- Allele Frequency
Research landscape
PubMed indexes 21,646 records tagged with the MeSH heading “DNA Fingerprinting” (counts retrieved 2026-10-29). 2,404 of them (11.1%) were published from 2020 onward, and the busiest period so far is 2000-2009 with 8,686 records.
Reviews account for 946 records (4.4%) and meta-analyses for 19. Randomized controlled trials number 45 (0.2%) 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.
Show the data
| Period | Records |
|---|---|
| through 1999 | 2,914 |
| 2000-2009 | 8,686 |
| 2010-2019 | 7,642 |
| 2020 onward | 2,404 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 946 | 4.4% |
| Meta-analyses | 19 | 0.1% |
| Randomized controlled trials | 45 | 0.2% |
Method: PubMed query "DNA Fingerprinting"[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 / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Mash Screen: high-throughput sequence containment estimation for genome discovery | 2019 | Research article Design not recorded in structured metadata | 254 | cc by PMID 31690338 · DOI · PMC6833257 |
| Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank | 2020 | Research article Design not recorded in structured metadata | 202 | cc by PMID 32398145 · DOI · PMC7218494 |
| Contaminant DNA in bacterial sequencing experiments is a major source of false genetic variability | 2020 | Research article Design not recorded in structured metadata | 79 | cc by PMID 32122347 · DOI · PMC7053099 |
| Artificial intelligence enables scale, consistency, and rigor in forensic identity inference | 2026 | Review article Design not recorded in structured metadata | 1 | cc by PMID 42286905 · PMC13247748 |
Mash Screen: high-throughput sequence containment estimation for genome discovery [1]
Abstract reading summary — source excerpts
- Opening excerpt
- The MinHash algorithm has proven effective for rapidly estimating the resemblance of two genomes or metagenomes.
- Closing excerpt
- Using this tool, we provide containment estimates for every NCBI RefSeq genome within every SRA metagenome and demonstrate the identification of a novel polyomavirus species from a public metagenome.
- 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 (99 words)
The MinHash algorithm has proven effective for rapidly estimating the resemblance of two genomes or metagenomes. However, this method cannot reliably estimate the containment of a genome within a metagenome. Here, we describe an online algorithm capable of measuring the containment of genomes and proteomes within either assembled or unassembled sequencing read sets. We describe several use cases, including contamination screening and retrospective analysis of metagenomes for novel genome discovery. Using this tool, we provide containment estimates for every NCBI RefSeq genome within every SRA metagenome and demonstrate the identification of a novel polyomavirus species from a public metagenome.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 31690338 · DOI · PMC6833257
Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Genomic analyses are sensitive to contamination in public databases caused by incorrectly labeled reference sequences.
- Closing excerpt
- com/martin-steinegger/conterminator.
- 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 (96 words)
Genomic analyses are sensitive to contamination in public databases caused by incorrectly labeled reference sequences. Here, we describe Conterminator, an efficient method to detect and remove incorrectly labeled sequences by an exhaustive all-against-all sequence comparison. Our analysis reports contamination of 2,161,746, 114,035, and 14,148 sequences in the RefSeq, GenBank, and NR databases, respectively, spanning the whole range from draft to "complete" model organism genomes. Our method scales linearly with input size and can process 3.3 TB in 12 days on a 32-core computer. Conterminator can help ensure the quality of reference databases. Source code (GPLv3): https://github.com/martin-steinegger/conterminator.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 32398145 · DOI · PMC7218494
Contaminant DNA in bacterial sequencing experiments is a major source of false genetic variability [3]
Abstract reading summary — source excerpts
- Opening excerpt
- BackgroundContaminant DNA is a well-known confounding factor in molecular biology and in genomic repositories.
- Closing excerpt
- Taxonomic classifiers are a powerful tool to implement such pipelines.
- 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 (203 words)
BackgroundContaminant DNA is a well-known confounding factor in molecular biology and in genomic repositories. Strikingly, analysis workflows for whole-genome sequencing (WGS) data commonly do not account for errors potentially introduced by contamination, which could lead to the wrong assessment of allele frequency both in basic and clinical research.ResultsWe used a taxonomic filter to remove contaminant reads from more than 4000 bacterial samples from 20 different studies and performed a comprehensive evaluation of the extent and impact of contaminant DNA in WGS. We found that contamination is pervasive and can introduce large biases in variant analysis. We showed that these biases can result in hundreds of false positive and negative SNPs, even for samples with slight contamination. Studies investigating complex biological traits from sequencing data can be completely biased if contamination is neglected during the bioinformatic analysis, and we demonstrate that removing contaminant reads with a taxonomic classifier permits more accurate variant calling. We used both real and simulated data to evaluate and implement reliable, contamination-aware analysis pipelines.ConclusionAs sequencing technologies consolidate as precision tools that are increasingly adopted in the research and clinical context, our results urge for the implementation of contamination-aware analysis pipelines. Taxonomic classifiers are a powerful tool to implement such pipelines.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 32122347 · DOI · PMC7053099
Artificial intelligence enables scale, consistency, and rigor in forensic identity inference [4]
Abstract reading summary — source excerpts
- Opening excerpt
- Advances in forensic genomics, which include massively parallel sequencing, dense single nucleotide polymorphism testing, and forensic genetic genealogy (FGG), have greatly expanded the range of cases in which DNA evidence can generate investigative leads.
- Closing excerpt
- Properly designed AI-enabled systems offer a path to sustainably scaling FGG while supporting scientific rigor, accountability, and public trust.
- 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 (197 words)
Advances in forensic genomics, which include massively parallel sequencing, dense single nucleotide polymorphism testing, and forensic genetic genealogy (FGG), have greatly expanded the range of cases in which DNA evidence can generate investigative leads. As a result, the primary limitations in modern forensic DNA analysis are no longer analytical sensitivity or marker availability, but the ability to reason consistently, transparently, and at scale over increasingly complex genetic, genealogical, and contextual information. Current forensic workflows remain predominantly human-centered, relying on manual reasoning that is difficult to standardize, reproduce, document, or scale across growing case inventories. The potential of artificial intelligence (AI) is described as an enabling layer for forensic identity inference, defined here as computational decision-support systems that structure, prioritize, and document reasoning over genetic associations, genealogical structures, and investigative context during identity hypothesis development. While formal statistical inference quantifies evidentiary weight, identity inference governs how evidence is explored, combined, and acted upon during investigations. AI-assisted systems can augment, but not replace, expert judgment by supporting scalable prioritization, relational reasoning, and systematic documentation of analytical decisions, while reducing bias. Properly designed AI-enabled systems offer a path to sustainably scaling FGG while supporting scientific rigor, accountability, and public trust.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42286905 · PMC13247748
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 Contamination in Low Microbial Biomass Microbiome Studies: Issues and Recommendations [5]
- Research article Single-cell identification in microbial communities by improved fluorescence in situ hybridization techniques [6]
- Research article Genetics and genomics of core short tandem repeat loci used in human identity testing [7]
- Research article PCR-DGGE fingerprinting: novel strategies for detection of microbes in food [8]
- Research article Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies [9]
- Research article Improving human forensics through advances in genetics, genomics and molecular biology [10]
References
- Ondov BD, Starrett GJ, Sappington A, Kostic A, Koren S, Buck CB, et al.. Mash Screen: high-throughput sequence containment estimation for genome discovery. . 2019. PMID 31690338 · DOI · PMC6833257
- Steinegger M, Salzberg SL. Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank. . 2020. PMID 32398145 · DOI · PMC7218494
- Goig GA, Blanco S, Garcia-Basteiro AL, Comas I. Contaminant DNA in bacterial sequencing experiments is a major source of false genetic variability. . 2020. PMID 32122347 · DOI · PMC7053099
- Budowle B, Newman S, Jones N, Marra M, Mittelman K, Mittelman D. Artificial intelligence enables scale, consistency, and rigor in forensic identity inference. . 2026. PMID 42286905 · PMC13247748
- Eisenhofer R, Minich JJ, Marotz C, Cooper A, Knight R, Weyrich LS. Contamination in Low Microbial Biomass Microbiome Studies: Issues and Recommendations. Trends Microbiol. 2019. PMID 30497919 · DOI
- Amann R, Fuchs BM. Single-cell identification in microbial communities by improved fluorescence in situ hybridization techniques. Nat Rev Microbiol. 2008. PMID 18414500 · DOI
- Butler JM. Genetics and genomics of core short tandem repeat loci used in human identity testing. J Forensic Sci. 2006. PMID 16566758 · DOI
- Ercolini D. PCR-DGGE fingerprinting: novel strategies for detection of microbes in food. J Microbiol Methods. 2004. PMID 14967221 · DOI
- Kennedy KM, de Goffau MC, Perez-Muñoz ME, Arrieta MC, Bäckhed F, Bork P, et al.. Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies. Nature. 2023. PMID 36697862 · DOI · PMC11333990
- Kayser M, de Knijff P. Improving human forensics through advances in genetics, genomics and molecular biology. Nat Rev Genet. 2011. PMID 21331090 · 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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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.