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Cited by 678

The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.

High-Resolution DNA Melting Analysis for Simple and Efficient Molecular Diagnostics

Reed, Gudrun H, Kent, Jana O, Wittwer, Carl T·2007·Pharmacogenomics

Abstract

High-resolution melting of DNA is a simple solution for genotyping, mutation scanning and sequence matching. The melting profile of a PCR product depends on its GC content, length, sequence and heterozygosity and is best monitored with saturating dyes that fluoresce in the presence of double-stranded DNA. Genotyping of most variants is possible by the melting temperature of the PCR products, while all variants can be genotyped with unlabeled probes. Mutation scanning and sequence matching depend on sequence differences that result in heteroduplexes that change the shape of the melting curve. High-resolution DNA melting has several advantages over other genotyping and scanning methods, including an inexpensive closed tube format that is homogenous, accurate and rapid. Owing to its simplicity and speed, the method is a good fit for personalized medicine as a rapid, inexpensive method to predict therapeutic response.

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Metadata

Authors
Reed, G. H., Kent, J. O., & Wittwer, C. T.
Venue
Pharmacogenomics
Published
2007-06
Volume
8
Issue
6
Pages
597-608
ISSN
  • 1462-2416 (ISSN-L)
  • 1744-8042
OpenAlex
W2112444375
Cited by
678
References
47
FWCI
12.21
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: High-Resolution DNA Melting Analysis for Simple and Efficient Molecular Diagnostics. https://app.cochat.ai/papers/10.2217/14622416.8.6.597
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Reed, G. H., Kent, J. O., & Wittwer, C. T. (2007). High-Resolution DNA Melting Analysis for Simple and Efficient Molecular Diagnostics. Pharmacogenomics. https://doi.org/10.2217/14622416.8.6.597

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.