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BMC Bioinformatics

, 9:370

First Online: 11 September 2008Received: 05 March 2008Accepted: 11 September 2008

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.

AbbreviationsTmMelting temperature

AICAkaike-s information criterion

HERVhuman endogenous retrovirus

EMexpectation maximization.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-9-370 contains supplementary material, which is available to authorized users.

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Author: Christoffer Nellåker - Fredrik Uhrzander - Joanna Tyrcha - Håkan Karlsson

Source: https://link.springer.com/







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