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Mixture models for analysis of melting temperature data
Stockholm University, Faculty of Science, Department of Mathematics. Matematisk statistik.
2008 (English)In: BMC Bioinformatics, Vol. 9:370Article in journal (Refereed) Published
Abstract [en]

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 (Tm) data. However, there is currently no convention on how to statistically analyze such high-resolution Tm 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 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.

Conclusion

Mixture 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.

Place, publisher, year, edition, pages
2008. Vol. 9:370
National Category
Neurosciences Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:su:diva-16405DOI: doi:10.1186/1471-2105-9-370ISI: 000260077000001OAI: oai:DiVA.org:su-16405DiVA: diva2:182925
Available from: 2008-12-17 Created: 2008-12-17 Last updated: 2011-01-10Bibliographically approved

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