ArticlePeerJ2020
Application of High Resolution Melt analysis (HRM) for screening haplotype variation in a non-model plant genus: Cyclopia (Honeybush).
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Rapid detection of the SARS-CoV-2 omicron variants based on high-resolution melting curve analysis.Scientific reports · 2024Article
- Application of MaxEnt Modeling and HRM Analysis to Support the Conservation and Domestication ofPlants (Basel, Switzerland) · 2022Article
- Article
- Applied phylogeography ofPeerJ · 2020Article
- Article
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Authors and funding
2 authors.
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Abstract
aimThis study has three broad aims: to (a) develop genus-specific primers for High Resolution Melt analysis (HRM) of members of Cyclopia Vent., (b) test the haplotype discrimination of HRM compared to Sanger sequencing, and (c) provide an example of using HRM to detect novel haplotype variation in wild LOCATION: The Cape Floristic Region (CFR), located along the southern Cape of South Africa.
methodsPolymorphic loci were detected through a screening process of sequencing 12 non-coding chloroplast DNA segments across 14 Cyclopia species. Twelve genus-specific primer combinations were designed around variable cpDNA loci, four of which failed to amplify under PCR; the eight remaining were applied to test the specificity, sensitivity and accuracy of HRM. The three top performing HRM Primer combinations were then applied to detect novel haplotypes in wild
resultsWe present a framework for applying HRM to non-model systems. HRM accuracy varied across the PCR products screened using the genus-specific primers developed, ranging between 56 and 100%. The nucleotide variation failing to produce distinct melt curves is discussed. The top three performing regions, having 100% specificity (i.e. different haplotypes were never grouped into the same cluster, no false negatives), were able to detect novel haplotypes in wild
conclusionsAfter screening for regions with high HRM clustering specificity-akin to the screening process associated with most PCR based markers-the technology was found to be a high throughput tool for detecting genetic variation in non-model plants.
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