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 T(m )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.