ArticleBMC bioinformatics2008
Mixture models for analysis of melting temperature data.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Latent class distributional regression for the estimation of non-linear reference limits from contaminated data sources.BMC bioinformatics · 2020Article
- Gaussian decomposition of high-resolution melt curve derivatives for measuring genome-editing efficiency.PloS one · 2018Article
- Expression profiling of repetitive elements by melting temperature analysis: variation in HERV-W gag expression across human individuals and tissues.BMC genomics · 2009Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIn addition to their use in detecting undesired real-time PCR products, melting temperatures are useful for detecting variations in the desired target sequences. Methodological improvements in recent years allow the generation of high-resolution melting-temperature (Tm) data. However, there is currently no convention on how to statistically analyze such high-resolution Tm data.
resultsMixture model analysis was applied to Tm data. Models were selected based on Akaike's information criterion. Mixture model analysis correctly identified categories in Tm data obtained for known plasmid targets. Using simulated data, we investigated the number of observations required for model construction. The precision of the reported mixing proportions from data fitted to a preconstructed model was also evaluated.
conclusionMixture model analysis of Tm data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined. This approach allows Tm data to be analyzed, classified, and compared in an unbiased manner.
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