Is normalisation of raw data necessary? Truly Automated Analysis of qPCR Data Using the AzurePCR Method

David Kennard, Ze’ev Russak
Azure PCR Limited, United Kingdom

Taking on molecular diagnostics industry challenges of interpreting cycle threshold, creating normalised curves and using ‘pretty pictures’ to facilitate manual interpretation of results, Azure PCR will demonstrate a novel and unique externally validated method for automated analysis of real-time (qPCR) data, including classification and quantification. Unlike existing software included within PCR cyclers, the AzurePCR (TM) automated analysis method is assumption-free and does not require setting of user parameters and, thus delivering time savings for both the researcher and the clinician. This automated analysis method demonstrates close to 100% and even 100% accuracy of detection, as confirmed by recent validation studies, including for data sets with high levels of background noise. We will address current industry failings by comparing our results against those of analysis software packages bundled with popular PCR cyclers. This will be done using recent empirical validations of the AzurePCR automated analysis method conducted with well-known research hospitals and commercial laboratories.

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