PCR elbow determination using curvature analysis of a double sigmoid

Abstract

Systems and methods for determining characteristic transition values such as elbow values in sigmoid or growth-type curves, such as the cycle threshold (Ct) value in PCR amplification curves. A double sigmoid function with parameters determined by a Levenberg-Marquardt (LM) regression process is used to find an approximation to a curve that fits a PCR dataset. Once the parameters have been determined, the curve can be normalized using one or more of the determined parameters. After normalization, the normalized curve is processed to determine the curvature of the curve at some or all points along the curve, e.g., to produce a dataset or plot representing the curvature v. the cycle number. The cycle number at which the maximum curvature occurs corresponds to the Ct value. The Ct value is then returned and may be displayed or otherwise used for further processing.

Description

[0001] This application is a divisional application of the application number 200610169374.1, the application date is December 19, 2006, and the invention title is "PCR Elbow Determination Using Double S-shaped Curvature Analysis". technical field [0002] The present invention relates broadly to systems and methods for processing data representing sigmoid curves or growth curves, and more particularly to methods for determining characteristic cycle thresholds (Ct) or elbow values ​​or Systems and methods for elbow values ​​in other growth curves. Background technique [0003] Polymerase chain reaction (PCR) is an ex vivo method for enzymatic synthesis or amplification of defined nucleic acid sequences. The reaction typically utilizes two oligonucleotide primers that hybridize to opposing strands and flank the template or target DNA sequence to be amplified. Extension of these primers is catalyzed by thermostable DNA polymerases. A series of repeated cycles involving denat...

AI Technical Summary

Problems solved by technology

For example, some methods are sensitive to abnormal (noisy) data, and the AFL value method is not suitable for datasets with high baseline

Especially in the case of high titers, it is used to determine figure 1 The traditional approach of the baseline stop (or the end point of the baseline) of the growth curve shown in does not work satisfactorily

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