BACKGROUND: 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. METHODS: 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. CONCLUSION: 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. Copyright 2010. Published by Elsevier Inc.
BACKGROUND: 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. METHODS: 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. CONCLUSION: 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. Copyright 2010. Published by Elsevier Inc.
Authors: Elisabeth M Weiss; Günter Schulter; Andreas Fink; Eva M Reiser; Erich Mittenecker; Harald Niederstätter; Simone Nagl; Walther Parson; Ilona Papousek Journal: PLoS One Date: 2014-01-20 Impact factor: 3.240
Authors: Ilona Papousek; Eva M Reiser; Günter Schulter; Andreas Fink; Emily A Holmes; Harald Niederstätter; Simone Nagl; Walther Parson; Elisabeth M Weiss Journal: Emotion Date: 2013-09-16