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a novel algorithm that allows the generation of an internal reference signal for copy number alteration detection. This method is referred to as Virtual Normals.
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Background: Genomic instability in cancer leads to abnormal genome copy number alterations that are associated with the development and behaviour of tumours. Despite advances in microarray technology to detect these alterations, challenges still exist in accurately identifying alteration regions due to the number of measured signals being used and their accompanying noise. Signal-to-noise ratios can increase even further when different batches and different labs are used to analyze data. Therefore, unless there is a reference set to base data on, the use of alteration data is inherently limited. Technology Description: Innovators at the University of Pittsburgh have designed a novel algorithm that allows the generation of an internal reference signal for copy number alteration detection. This method, referred to as Virtual Normals, allows for the construction of an unbiased reference signal, free of aberrations, from the test samples of a given experiment. This approach has been proven to obtain the best signal-to-noise ratio of alteration values, as validated by an Applied Biosystems Taqman copy number analysis approach. Applications: 1) Identify copy number alterations in tumor specimens for which a reference sample set is not available Advantages: 1) Eliminates need of a normal reference set 2) Eliminates variability due to copy number aberrations Stage of Development: 1) Software created and tested