Simple and accurate analysis of Real-Time PCR data using Bar Harbor Biotechnology, Inc's Global Pattern Recognition™ software
Bar Harbor Biotechnology, Inc. has solved one of the most fundamental problems facing experimentation using Real-Time PCR. How do I analyze the data and determine REAL changes in gene expression? The answer to this question is found in Bar Harbor Biotechnology, Inc.'s patent pending Global Pattern Recognition™ algorithm, which makes gene expression analysis simple, fast and reliable. Here are some reasons why we developed this algorithm.

Real-time dogma #1 - using single gene normalizers
The traditional approach to measure gene expression changes from Real-Time PCR data has been to normalize the results of a gene of interest with respect to a housekeeping gene (aka. a reference or normalizer gene). The general assumption is that the level of expression of the normalizer gene does not change in the context of the experiment and can be used to normalize the variability in RNA quantity between individual samples. By normalizing to a housekeeping gene, in theory, a magnitude of change can be calculated between groups of samples for a gene of interest. However, this mode of analysis is greatly complicated by the fact that housekeeping genes commonly used as normalizers (e.g., GAPDH, β-actin, and HPRT) themselves can change in apparent expression when comparing tissues or cells in different states (Bustin 2000; Schmittgen et al. 2000; Goidin et al. 2001; Hamalainen et al. 2001). 18S rRNA is another normalizer that intuitively and experimentally seems more stable, but even 18S can vary in comparison to other genes when analyzed by sensitive Real-Time PCR techniques (Bustin 2000, Akilesh et al., 2003). Any small variation in the normalizer amplification would therefore compromise the analysis of the complete Real-Time PCR data set.

Real-time dogma #2 - ranking genes strictly by fold change
When a single gene normalizer is selected, gene expression changes are typically ranked by their magnitude of change using the ΔΔCt method, with those genes showing the largest fold changes ranked as most significant. Unfortunately, these large changes in gene expression may mask small, but biologically important changes in gene expression, such as master regulator genes (e.g., transcription factors). In biology, however, larger is not always synonymous with importance. To combat the above mentioned problems, Bar Harbor Biotechnology, Inc. developed a modified Global Pattern Recognition™ algorithm (Akilesh et al., 2003), which is optimally suited to generate a ranked list of significantly changed genes within a Real-Time PCR dataset. This unique algorithm and accompanying software overcomes the problem of identifying invariant normalizers and the pitfalls of producing faulty statistics based merely on magnitude of change. Global Pattern Recognition™ provides a true statistical analysis of results based on consistency in the data, which makes Global Pattern Recognition™ optimally suited to detect small, but reproducible changes. Only after the genes are statistically ranked is the magnitude of the change calculated. A typical experiment would utilize 'biological replicates' (Bio-Reps). Bio-Reps are defined as samples collected from separate and closely matched biological samples. They are processed individually under closely matched conditions. Advisedly, it is best to analyze at least 3 bio-reps in each of two groups, representing for example '3 sick vs. 3 healthy' or '3 treated vs. 3 untreated' groups (but Global Pattern Recognition™ can handle much larger data sets). Global Pattern Recognition™ processes the data derived from groups of Bio-Reps and reveals the 'constellation' of changing genes. Each constellation can be evaluated for the most likely biological context providing the researcher with a better understanding of the overall results. Just as early sea navigators used the stars to triangulate their position on the ocean, Global Pattern Recognition™ globally positions the expression level of each gene with respect to all genes within an experiment. This can be done without prior assumption that a gene (normalizer) has an invariant expression level. Global Pattern Recognition™ is unbiased in that it enables the experimental data to define the invariant normalizer genes, not the experimenter. The use of any gene as a potential normalizer also maximizes the use of the limited real-estate on a StellARray™ plate by eliminating the loss of wells used to contain potentially erroneously predefined normalizers.

Global Pattern Recognition™ is extremely simple to use and reliably tabulates statistical significance (p-value) of gene expression changes on the fly allowing you to immediately focus on the real biology. Simply log into GPR, select the StellARray™ that you ran on your Real-Time PCR instrument, upload your data and submit for analysis. An HTML or Excel® formatted file will be generated that gives a ranked list of genes by p-value, fold change value, and links to MGI and NCBI gene pages. With each purchase of a StellARray™ pack your account will be receive analysis query credits sufficient to analyze each plate.