用于分析解链温度数据的混合模型。
Mixture models for analysis of melting temperature data.
作者信息
Nellåker Christoffer, Uhrzander Fredrik, Tyrcha Joanna, Karlsson Håkan
机构信息
Department of Neuroscience, Karolinska Institutet, Retzius Väg, Stockholm, Sweden.
出版信息
BMC Bioinformatics. 2008 Sep 11;9:370. doi: 10.1186/1471-2105-9-370.
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.
背景
除了用于检测不需要的实时聚合酶链反应（PCR）产物外，熔解温度对于检测所需靶序列的变异也很有用。近年来的方法改进使得能够生成高分辨率熔解温度（Tm）数据。然而，目前对于如何对这种高分辨率Tm数据进行统计分析尚无统一规范。
结果
将混合模型分析应用于Tm数据。基于赤池信息准则选择模型。混合模型分析正确识别了已知质粒靶标的Tm数据中的类别。使用模拟数据，我们研究了模型构建所需的观察次数。还评估了拟合到预先构建模型的数据所报告的混合比例的精度。
结论
对Tm数据进行混合模型分析可以确定一组扩增子中不同序列的最小数量及其相对频率。这种方法能够以无偏方式对Tm数据进行分析、分类和比较。
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