Integromics RealTime StatMiner®

Category Genomics>Gene Expression Analysis/Profiling/Tools

Abstract RealTime StatMiner 3.0 is a custom bioinformatics application that is focused on preprocessing, normalization and interpretation of raw real-time PCR data from Applied Biosystems’™ instrumentation (Ct values from SDS) including TaqMan® Low-Density Arrays (TLDAs).

Throughout the analysis process, the step-by-step guide facilitates statistical analysis, making it possible for users of all levels to produce meaningful and reliable results.

RealTime StatMiner is fully compatible with all Applied Biosystems platforms, including 7900HT, 7500, StepOne™ and StepOnePlus™.

RealTime StatMiner includes PCR efficiency in differential expression calculations for SYBR® Green experiments.

RealTime StatMiner analyzes all cards designed for MicroRNAs and includes per-port normalization and links to MicroRNA databases.

RealTime StatMiner Key Features include:

1) RealTime StatMiner is the solution for RT-qPCR users.

2) Compatible with TaqMan and SYBR® Green.

3) Analyze data using a Step-by-Step guided workflow.

4) Integrates Quality Controls and Filtering Criteria.

5) Study gene expression and miRNA regulation.

5) Automatically select the best Endogenous Controls.

6) Statistical power with parametric and non-parametric test, paired tests.

7) Multi-factor analysis with 2-way ANOVA.

8) Hierarchical clustering on samples and experimental groups.

9) Automatic report and log options.

Quality -- A complete diagnosis of your input data increases your confidence in the results. You can easily identify outliers and remove wrong samples using the advanced Quality Controls feature.

Optimal -- RealTime StatMiner analyzes Endogenous Control genes and ranks them using GeNorm and NormFinder (see below...). The optimal combination is always selected by default.

Statistics -- RealTime StatMiner’s statistical functionalities perform parametric and non-parametric tests. The paired T-test and 2-way ANOVA analysis are fully supported.

Clustering -- RealTime StatMiner incorporates the advanced and convenient Hierarchical Clustering functionality when performing Biomarker discovery studies.

Normalization -- In order to estimate the best normalization genes, RealTime StatMiner 3.0 offers three (3) methods to evaluate gene expression stability: NormFinder, GeNorm and Minimum Variance Median.

1) NormFinder - This method described by Andersen C.L. et al. 2004 first estimates the intra-group and then the inter-group expression variation. Both of these sources of variation are combined into a stability value.

Genes with the lowest stability value have the most stable expression. The sample set should minimally contain 8 samples/group, and the number of candidates should be at least 3, but 5–10 are recommended.

It is a further requirement that the candidates are chosen from a set of genes with No prior expectation of expression difference between groups.

Andersen CL, Jensen JL, Ørntoft TF. Normalisation of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalisation, applied to bladder and colon cancer data sets. Cancer Res. 2004 Aug 1;64(15): 5245-50. [PMID: 15289330].

2) GeNorm - GeNorm (Vandesompele J. et al., 2002) estimates the pairwise variation (standard deviation of the logarithmically transformed expression ratio) of a control gene with all the other control genes of the experiment. Following this step, a gene stability measure M is calculated as the average pairwise variation.

Genes with the lowest M values have the most stable expression. Assuming the control genes are not co-regulated, stepwise exclusion of the gene with the highest M value, results in a combination of two constitutively expressed control genes that have the most stable expression in the tested samples.

The recommended number of samples for selecting the control genes is 10, in order to select three of the most stable expressed genes as controls from which the geometric mean is calculated.

Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F. Accurate normalisation of real-time quantitative RT- PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002 Jun 18;3(7):RESEARCH0034. Epub 2002 Jun 18. [PMID: 12184808].

3) Minimum Variance Median - Is a method in which the median of the expression levels is computed for each group. Finding the best housekeeping detector is based on finding the detector for which the influence of the group on the detector remains constant.

The median of the expression levels is computed for each group, and the variability for each detector is measured by the means of the variance. Genes with the lowest stability value, have the most stable expression.

If you are working with Microarrays, you can combine RealTime StatMiner with Integromics Biomarker Discovery™, the manufacturer's analysis tool for Microarray technologies powered by Tibco Spotfire - (see G6G Abstract Number 20102). Integromics also provides data management solutions through ArrayHub®, the ultimate gene expression platform for service providers.

Note: See G6G Abstract Number 20101 for additional product info from this manufacturer.

System Requirements

Manufacturer

Manufacturer Web Site Integromics RealTime StatMiner

Price Contact manufacturer.

G6G Abstract Number 20204A

G6G Manufacturer Number 101520