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Novel, Fully Automated Method Allows Efficient Analysis of qPCR Data for Qualitative Calling Based on Comparative Cq
Poster

Novel, Fully Automated Method Allows Efficient Analysis of qPCR Data for Qualitative Calling Based on Comparative Cq

Novel, Fully Automated Method Allows Efficient Analysis of qPCR Data for Qualitative Calling Based on Comparative Cq
Poster

Novel, Fully Automated Method Allows Efficient Analysis of qPCR Data for Qualitative Calling Based on Comparative Cq

qPCR is extensively applied to determine the identity of genetically modified (GM) seeds and plants. Analysis and interpretation of qPCR data for any application is limited by sample variability1, poor assay performance1 and arbitrarily set thresholds2 which can all lead to ambiguous and subjective result calling that is reliant upon the expertise and experience of the scientist interpreting the assay. Processing of raw data output from thermal cyclers using analysis software, to provide data more easily interpretable by the human eye, can lead to loss of data3 and error prone sample calling4,5. In this study we retrospectively examined data produced in qPCR-based plant genotyping tests at Pioneer Hi-Bred to assess if patent-pending AzurePCR automated analysis produced similar results without the need for manual input of analysis parameters or manipulation of raw data.

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