Whether a laboratory is new or well-established, LGC Biosearch shares its expertise on how to build the best qPCR assays possible.
Sponsored Content by LGC Biosearch TechnologiesReviewed by Louis CastelJun 17 2024 When working in a lab, launching a quantitative PCR experiment demands a comprehensive understanding of complicated protocols, principles, and methodologies. Analyzing the large amounts of published qPCR data to choose the ideal experimental framework for your objectives means devoting significant expertise, time, and resources.
Primer and probe selection Select or design primers using priorities and research objectives. As well as following general design guidelines for choosing melting temperature , GC content, optimal primer length, etc., it is crucial to consider these other aspects of the target sequence: Standard curve The most common method for assessing assay performance is generating a standard curve, which relates the cycle quantification values of a known concentration of your template to the corresponding initial template concentrations.
This ratio reduces variability, enabling the use of the same pipette and tips while allowing efficient sample mixing in the reaction mix.Assay normalization Normalization to a reference gene accounts for variance in how much sample is added to the reaction. Normalization is intended to account for the potential technical variability in each step of the qPCR protocol. The best practice is to leverage a mixture of normalization methods to address challenges at every protocol step.
Data analysis Data analysis in qPCR consists of collecting and reviewing the raw data, assessing its quality and reliability, and generating reported results. Researchers should relay detailed information about the data analysis strategies and confidence, including software, statistical methods, and accuracy assessments.
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