| Summary: | <p>Abstract</p> <p>Background</p> <p>In 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 (T<sub>m</sub>) data. However, there is currently no convention on how to statistically analyze such high-resolution T<sub>m </sub>data.</p> <p>Results</p> <p>Mixture model analysis was applied to T<sub>m </sub>data. Models were selected based on Akaike's information criterion. Mixture model analysis correctly identified categories in T<sub>m </sub>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.</p> <p>Conclusion</p> <p>Mixture model analysis of T<sub>m </sub>data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined. This approach allows T<sub>m </sub>data to be analyzed, classified, and compared in an unbiased manner.</p>
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