Critical success factors for HRM performance
Factors – influencing success in HRM experiments
Optimized HRM kits from QIAGEN
Unlike HRM kits from other suppliers, the unique features of the Type-it HRM PCR Kit ensure amplification and discrimination of even the most challenging, subtle sequence differences, including class IV SNPs (see figure Successful genotyping of an A/T class IV SNP). Highly sensitive detection of low levels of methylated DNA is enabled by the EpiTect HRM PCR Kit (see figure Highly sensitive results — detection of even low percentages of methylated DNA).
Template quality and amount
Successful primer and assay design
Instrument, software, and analysis settings
To exploit the extensive information content of HRM experiments and enable reliable HRM analysis and data interpretation, powerful HRM software packages are required. Typical HRM data analysis discriminates between genotypes by comparing the position and shape of melting curves of different samples. For example melting curves of different homozygotes differ in their melting points (Tm) and melting curves of different heterozygotes differ in their shape and melting points (see figure HRM analysis of homozygous and heterozygous samples). In standard HRM software packages, variations in melt curve shape and Tm position compared to a control are used to differentiate between samples and to assign the corresponding genotype. A typical workflow as performed with the Rotor-Gene Q standard software package is depicted in the figure Guidelines for successful HRM. While this standard method of HRM analysis works well for many genetic variations, it has some limitations. Often, difficult-to-interpret results require additional time-consuming manual data interpretation and controls are required for accurate genotype classification. In contrast, Rotor-Gene ScreenClust HRM Software uses innovative mathematical algorithms to characterize samples and group them into clusters. This statistical approach exploits the entire information content of the HRM experiment, which enables discrimination of even the most difficult class IV A/T SNPs with differences in melting temperatures as low as 0.1°C in a standardized way. Additionally, the statistical approach allows a classification of genotypes without control samples using the unsupervised mode of the Rotor-Gene ScreenClust HRM Software. This hypothesis-free method enables discovery of new mutations in the data, when there is limited or even no knowledge of the genotypes present in the samples.