Automatic threshold setting and baseline determination for real-time PCR
View Patent ↗The invention discloses a system and methods for quantitating the presence of nucleic acid sequences by evaluation of amplification data generated using real-time PCR. In one aspect, the methods may be adapted to identify a threshold and threshold cycle for one or more reactions based upon evaluation of exponential and baseline regions for each amplification reaction. The methodology used in the analysis may be readily automated such that subjective user interpretation of the data is substantially reduced or eliminated.
1. A method for target sequence quantitation, comprising:
amplifying a plurality of target sequences by PCR in the presence of a detectable reporter construct;
acquiring signal information for each target sequence of the plurality of target sequences amplified by detecting the associated detectable reporter construct;
identifying a plurality of exponential regions, wherein each exponential region is associated with substantial increases in the signal information for each target sequence amplification and wherein each identified exponential region comprises an associated upper and lower bound;
normalizing the signal information for each target sequence amplification using an identified baseline component for the signal information to compensate for non-specific fluorescence in the signal information;
determining an exponential region threshold based on a comparison of the plurality of exponential regions, wherein each exponential region of the plurality of exponential regions is associated with a target sequence amplification of the plurality of target sequence amplifications;
performing a fitting operation to at least an interval of the exponential region including the exponential region threshold;
identifying, based on the exponential region threshold and an associated polynomial root, a threshold cycle for the at least one target sequence amplification.
2. The method of claim 1 , wherein identification of the exponential region upper bound comprises performing a derivative operation.
3. The method of claim 2 , wherein the derivative operation comprises obtaining a second derivative using the signal information.
4. The method of claim 3 , wherein the identification of the exponential region lower bound comprises detecting incremental differences in the signal information starting at the exponential region upper bound.
5. The method of claim 4 , wherein the incremental differences comprise at least one of differences in a ratio of detected intensity of successive values of the signal information and differences in detected intensity of successive values of the signal information.
6. The method of claim 1 , wherein the normalizing comprises:
identifying a baseline region based on the exponential region lower bound;
performing a regression analysis of the baseline region to generate a characteristic equation; and
differencing the baseline component using the characteristic equation to normalize the signal information.
7. The method of claim 1 , wherein the fitting operation is a polynomial fitting operation.
8. The method of claim 7 , wherein the polynomial fitting operation comprises:
performing a polynomial fitting operation over a selected number of cycles including the threshold cycle to identify a threshold equation;
factoring the threshold equation to identify a real root of the threshold equation; and
associating the real root with the threshold cycle.
9. The method of claim 8 , wherein the polynomial fitting operation comprises applying a Savitzky-Golay smoothing operation.
10. A system for target sequence quantitation, comprising:
a data collection module configured to collect signal information including detected reporter label intensities generated by amplifying a plurality of target sequences in the presence of a detectable reporter construct; and
a data processing module configured to:
identify a plurality of exponential regions, wherein each exponential region is associated with substantial increases in the signal information for the at least one target sequence amplification, and each exponential region comprising an upper bound and a lower bound,
normalize the signal information for each target sequence amplification using an identified baseline component for the signal information to compensate for non-specific fluorescence in the signal information,
determine an exponential region threshold based on a comparison of the plurality of exponential regions, wherein each exponential region of the plurality of exponential regions is associated with a target sequence amplification of the plurality of target sequence amplifications,
perform a fitting operation to fit a polynomial to at least an interval of the exponential region including the exponential region threshold, and
identify, based on the exponential region threshold and an associated polynomial root, a threshold cycle for the at least one target sequence amplification.
11. The system of claim 10 , wherein to identify the plurality of exponential regions, the data processing module is further configured to perform-a derivative operation.
12. The system of claim 10 , wherein to normalize, the data processing module is further configured to:
identify a baseline region based on the exponential region lower bound;
perform a regression analysis of the baseline region to generate a characteristic equation; and
difference the baseline component using the characteristic equation to normalize the signal information.
13. The system of claim 10 , wherein the fitting operation is a polynomial fitting operation.
14. The system of claim 13 , wherein the data processing module is further for performing a polynomial fitting operation, wherein performing the polynomial fitting operation comprises:
performing a polynomial fitting operation over a selected number of cycles including the threshold cycle to identify a threshold equation;
factoring the threshold equation to identify a real root of the threshold equation; and
associating the real root with the threshold cycle.