Reorder now!
Reorder from your past orders in just one click.
GO TO MY QIAGEN
Order by Quote
Add quote number from your quote document
Add customer number from your quote document
Order by Catalog Number
Use mass upload
Looking for a quick way to design experiments?
Try the Workflow Configurator. A convenient tool to build experimental workflows and find products to match your needs.

Rotor-Gene ScreenClust HRM Software

For highly effective high-resolution melting analysis

Features

  • Innovative mathematical approach to HRM analysis
  • Highly accurate identification of genotypes in supervised mode
  • Automatic detection of new mutations in unsupervised mode
  • Robust statistics for classifying and interpreting HRM data
  • Minimal effort and standardized processes for data interpretation
undefined

✓ 24/7 automatic processing of online orders

✓ Knowledgeable and professional Product & Technical Support

✓ Fast and reliable (re)-ordering

Rotor-Gene ScreenClust HRM Software

Cat. No. / ID: 9020147

Software CD, user guide
The Rotor-Gene ScreenClust HRM Software is intended for molecular biology applications. This product is not intended for the diagnosis, prevention, or treatment of a disease.

✓ 24/7 automatic processing of online orders

✓ Knowledgeable and professional Product & Technical Support

✓ Fast and reliable (re)-ordering

Product Details

Rotor-Gene ScreenClust HRM Software is a powerful tool for analysis of high-resolution melting (HRM) data from the Rotor-Gene Q or Rotor-Gene 6000 cycler. By grouping samples into clusters, Rotor-Gene ScreenClust HRM Software enables applications such as genotyping and mutation screening.

 

Note: Rotor-Gene ScreenClust HRM Software is compatible with Windows 7 but is not supported by operating system versions after Windows 7, such as Windows 10.

Performance

These Rotor-Gene Q cycler, in combination with Rotor-Gene ScreenClust HRM Software, enables identification of even difficult class IV A/T SNPs which can have differences in melting temperatures as low as 0.1°C (see figure " I dentification of a class IV SNP").

In this mutation detection experiment, various gene mutations resulting form insertions/deletions in the EGFR gene exon 19 were analyzed (see figure " Successful mutation detection"). Rotor-Gene ScreenClust HRM Software accurately separated multiple close and partially overlapping melt profiles by successfully extracting data characteristics into the 3 first principal components. All 6 pseudo-unknowns and the wild-type sample were correctly identified.

See figures

Principle

HRM is an innovative technique that characterizes double-stranded PCR products based on their melting (dissociation) behavior as they transition from double-stranded DNA (dsDNA) to single-stranded DNA (ssDNA) with increasing temperature. First, the target sequence is amplified by PCR to a high copy number. Next, high-precision melting of PCR products enables discrimination of samples according to sequence, length, GC content, or strand complementarity, down to single base-pair changes. No prior sequence information is needed, enabling detection of previously unknown and even complex sequence variations in a simple and straightforward way.

Reliable HRM analysis requires a suitable HRM instrument, chemistry, and data analysis software. The Rotor-Gene Q cycler has a unique rotary design that provides outstanding thermal and optical performance, making it ideal for use in HRM analysis. The Type-it HRM PCR Kit provides optimized chemistry for accurate resolution of sequence variations and unambiguous allelic discrimination. Rotor-Gene ScreenClust HRM Software enables reliable interpretation of data.

HRM data analysis discriminates between genotypes by comparing the position and shape of melting curves of different samples. Melting curves of heterozygotes and homozygotes differ in their shapes and melting points (Tm). In standard HRM software packages, variations in melt curve shape and position compared to a control are used to differentiate between samples. This method can cause unreliable, difficult-to-interpret results, and time-consuming manual data interpretation may be necessary. In contrast, Rotor-Gene ScreenClust HRM Software uses innovative mathematical algorithms to characterize samples and group them into clusters. 

Procedure

Rotor-Gene ScreenClust HRM Software analyzes HRM data in 4 steps:

  • Normalization
  • Generation of a residual plot
  • Principal component analysis
  • Clustering

The software guides the user through all the steps, giving information about any choices that can be made at each step.

HRM performed on the Rotor-Gene cycler produces raw data (*.rex files) that can be further analyzed using Rotor-Gene ScreenClust HRM Software. In the first step in analysis, raw data are normalized by applying curve scaling to a line of best fit so that the highest fluorescence value is equal to 100 and the lowest is equal to zero. Next, the curves are differentiated and a composite median curve is constructed using the median fluorescence of all samples. The melt traces for each sample are subtracted from this composite median curve to draw a residual plot. The individual sample characteristics are extracted by principal component analysis from the residual plot. Principal component analysis is a well-established method of data analysis. However, Rotor-Gene ScreenClust HRM Software is the first software application to apply principal component analysis to HRM data. Principal component analysis highlights similarities and differences in the data and is used to create a cluster plot in supervised or unsupervised mode (see figure " Identification of a class IV SNP"). Clustering (grouping) of data is performed according to allele.

Supervised mode is often used for SNP genotyping, where the genotypes are known. In supervised mode, the user assigns one or more control samples for each cluster and the software classifies (autocalls) all unknown samples to clusters according to their characteristics. The unsupervised mode is used to find new mutations in the data when there is no prior knowledge or only partial knowledge of the genotypes present in the samples. In unsupervised mode, the software calculates the optimum number of clusters by itself. This feature is an excellent tool for the discovery of new polymorphisms.

The result of analysis in both modes is displayed as an easy-to-interpret cluster plot (see figure " I dentification of a class IV SNP"). Statistical probabilities and typicalities are provided to allow easy comparison of results from different experiments. All data and graphs can be conveniently exported in various formats such as JPG, PDF, CSV, or XLS file formats and are summarized in an experimental report.

See figures

Applications

HRM analysis using Rotor-Gene ScreenClust HRM Software provides enormous potential for a wide range of applications. SNP genotyping, and mutation scanning or detection experiments can especially benefit from the power of this technology.

Supporting data and figures

Resources

Brochures & Guides (1)
Second edition — innovative tools
Instrument User Manuals (1)
Technical Information (1)

FAQ

What is the difference between probability and typicality in the Rotor-Gene ScreenClust HRM Software?

'Probability' is the likelihood or chance that a sample is a member of each available cluster. The combined probabilities of a single sample add up to 1.00.

'Typicality' in the Rotor-Gene ScreenClust HRM analysis is a measure of how well a sample fits into its assigned cluster. It can also be seen as a measure of how far away a sample is from the centre of the cluster. Typicality values range from 0 to 1, the higher the value the closer it is to the centre of its assigned cluster. If a sample has a typicality value of 0.5, it means that approximately half of all samples within that cluster will be closer to the centre and the other half will be further away. In reality, this might not be the case as small samples numbers can have skewed distributions.

 

FAQ ID -2203
Why are some of my samples outside of the cluster using Rotor-Gene ScreenClust HRM Software?

Clusters in Rotor-Gene ScreenClust HRM Software are graphically represented by ellipses/ellipsoids which act as a visual aid. They are not designed to cover all of the samples. They are a good tool for compare differences between clusters. To judge how well individual samples fit within their clusters use the typicality scores.

 

 

FAQ ID -2204
Why are most of my samples outside of the cluster in supervised mode using Rotor-Gene ScreenClust HRM Software?

The controls define the centre of the clusters in supervised mode using Rotor-Gene ScreenClust HRM Software. If the control samples lie on the fringe of the cluster then the cluster centre can be shifted away from the bulk of the samples within the cluster. Controls should provide a good representation of the expected behavior of unknown samples. If this is not the case the experimental setup should be re-evaluated.

 

FAQ ID -2205
Why do I need normalization using Rotor-Gene ScreenClust HRM Software?

Normalization using Rotor-Gene ScreenClust HRM Software is required since HRM melt curves can have different starting points. Therefore the scale of each melt curve can be different. Comparison can only occur if all samples are on the same scale, so each curve needs to be normalized.

 

FAQ ID -2198
What are Principal Components analyzed in Rotor-Gene ScreenClust HRM Software?

Principal component analysis is a well-established method of data analysis for multivariate data sets, such as obtained from, e.g.,  microarray analysis or image analysis. However, it is new in ScreenClust for HRM data. Principal Components (PCs) are extracted from the residuals plot so that the first Principle Component (PC1) represents the greatest variability or difference between all samples. The second (PC2) represents the regions of difference not already present in PC1. The third (PC3) represents differences not in PC1 and PC2.

 

FAQ ID -2200
Which mode should I use in the Rotor-Gene ScreenClust HRM Software, supervised or unsupervised?

The supervised mode in the Rotor-Gene ScreenClust HRM Software is designed for data sets with a known number of clusters where each cluster has defined controls. All samples will be grouped into one of the defined clusters.

The unsupervised mode is used if there are no controls for each cluster or if the number of clusters is not known. Based on the data presented, ScreenClust will return the recommended number of clusters and whether to separate the data in 2D or 3D. A user may choose to change either of both of these values.

 

FAQ ID -2202
What are clusters analyzed in the Rotor-Gene ScreenClust HRM Software?

Clusters analyzed in the Rotor-Gene ScreenClust HRM Software are groups of samples with the same melt characteristics. For example, in a single nucleotide polymorphism (SNP) analysis the clusters may represent the genotypes 'wild type', 'mutant' and 'heterozygote'. ScreenClust is designed to group samples into clusters after they are separated based on differences in their melt curves. The clusters can either be defined by control samples (supervised mode) or ScreenClust can determine the appropriate number of clusters (unsupervised mode).

 

FAQ ID -2201
What is the residuals plot in the Rotor-Gene ScreenClust HRM Software?

Once all melt curves are normalized, they are on a comparable basis. To make the information within each curve more useful for comparison, each curve is differentiated. The Residuals Plot is a plot of the difference between each sample and the composite median of all the samples after differentiation. The Residuals Plot of the Rotor-Gene ScreenClust HRM Software is different from a "Difference Plot" known from standard HRM software packages.

 

FAQ ID -2199