QuantiNova LNA PCR Custom Panels use SYBR ® Green-based detection, which, when used with a highly specific PCR system, well-designed primers and optimized reaction conditions, offers several advantages over hydrolysis probe detection. High-performance SYBR ® Green-based detection This gives you robust and reliable quantification, even for AU-rich targets, low-abundance transcripts, targets with high secondary structure and highly complex samples. The high binding affinity of LNA bases increases the flexibility of primer placement on the transcript, so we are able to use intelligent positioning to design assays for otherwise difficult-to-analyze targets. Increased specificity from the clever placement of LNA gives you a high signal-to-noise ratio, allowing discrimination of sequences that differ by only a single nucleotide and eliminating non-specific amplification and primer-dimer formation.
The increased sensitivity ensures excellent amplification efficiencies down to 1 RNA molecule, making it easier for you to detect low-abundance targets such as lncRNAs from less starting material (see figure QuantiNova LNA PCR Assays provide accurate, sensitive and linear quantification of targets over a wide dynamic range and figure QuantiNova LNA PCR Assays enable both high-expression and low-expression detection of mRNA). LNA enhancement enables T m normalization across the panel giving the primers higher binding affinity, dramatically increasing assay sensitivity and specificity. Each primer set delivers the highest specificity and efficiency for the most reliable and accurate results. Twenty years of LNA design experience has enabled us to optimize our sophisticated LNA design algorithm, which incorporates over 50 different parameters to guarantee the most optimal assay for successful target detection. A web tool for quality evaluation, dPCalibRate, is available.Īccuracy Digital PCR Linearity Precision Quality control.Expert LNA enhancement for top qPCR performanceĪll QuantiNova LNA PCR Assays are developed using stringent design criteria and lab-validated algorithms. The proposed assessments are also applicable to other analyses, such as the comparison of results obtained from qPCR and dPCR. We find that a robust weighted least-squares approach is highly advisable, yet may also suffer from an inflated false-positive rate. Further, typically presented plots and statistics may not reveal problems with linearity, accuracy, or precision. We present simulation results and a case study supporting the importance of a thorough evaluation. We study the pitfalls associated with the evaluation of such an experiment, and provide guidelines for the assessment of linearity, accuracy, and precision in dPCR experiments. This necessitates an approach akin to the construction of standard curves. Ignoring these quality issues may lead to erroneous quantification. Nevertheless, evaluation of the linear dynamic range, accuracy, and precision of an assay or platform is recommended, as there are several potential causes of important non-linearity, bias, and imprecision. Digital polymerase chain reaction (digital PCR, dPCR) is a direct nucleic acid quantification method, thus requiring no standard curves unlike quantitative real-time PCR (qPCR).
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