Mixture models for analysis of melting temperature data

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