Bio Republic, 14 Birriwa Street Greystanes, NSW 2145, Australia.
Methods. 2010 Apr;50(4):S10-4. doi: 10.1016/j.ymeth.2010.02.006. Epub 2010 Feb 8.
High resolution melting (HRM) is an emerging new method for interrogating and characterizing DNA samples. An important aspect of this technology is data analysis. Traditional HRM curves can be difficult to interpret and the method has been criticized for lack of statistical interrogation and arbitrary interpretation of results.
Here we report the basic principles and first applications of a new statistical approach to HRM analysis addressing these concerns. Our method allows automated genotyping of unknown samples coupled with formal statistical information on the likelihood, if an unknown sample is of a known genotype (by discriminant analysis or "supervised learning"). It can also determine the assortment of alleles present (by cluster analysis or "unsupervised learning") without a priori knowledge of the genotypes present.
The new algorithms provide highly sensitive and specific auto-calling of genotypes from HRM data in both supervised an unsupervised analysis mode. The method is based on pure statistical interrogation of the data set with a high degree of standardization. The hypothesis-free unsupervised mode offers various possibilities for de novo HRM applications such as mutation discovery.
高分辨率熔解（HRM）是一种新兴的 DNA 样本检测和特征分析方法。该技术的一个重要方面是数据分析。传统的 HRM 曲线难以解释，该方法因缺乏统计检验和对结果的任意解释而受到批评。
在这里，我们报告了一种新的统计方法的基本原理和初步应用，该方法解决了这些问题。我们的方法允许对未知样本进行自动基因分型，并结合有关未知样本是否为已知基因型的正式统计信息（通过判别分析或“有监督学习”）。它还可以在没有先验基因型知识的情况下确定存在的等位基因组合（通过聚类分析或“无监督学习”）。
新算法提供了高度敏感和特异性的 HRM 数据自动基因分型，无论是在有监督还是无监督分析模式下。该方法基于对数据集的纯统计检验，具有高度的标准化。无监督模式下的无假设提供了各种新的 HRM 应用可能性，例如突变发现。
