RNA Processing Data Analysis Lisa Bloomer Green April 26, 2010

1. RNA Processing Data Analysis Lisa Bloomer Green April 26, 2010

2. RNA Processing Data Analysis Real Time PCR Splicing The Problem Solving the Simplified Problem Making it Complicated Again

3. Real-time PCR DNA is copied again and again for exponential growth in quantity present. http://pathmicro.med.sc.edu/pcr/realtime-home.htm

4. Real-time PCR Output is the number of cycles it takes to pass a certain threshold. http://pathmicro.med.sc.edu/pcr/realtime-home.htm

5. The Problem Use real-time PCR to help discover how often alternative splicing occurs in a given region of RNA. Alternative Splicing: A mechanism by which different forms of mature mRNAs (messengers RNAs) are generated from the same gene. http://www.medterms.com/script/main/art.asp?articlekey=16831

6. Rpo chloroplast operon rpoB rpoC1 rpoC2 mRNA rpoB rpoC1 rpoC1 rpoC2 rpoB rpoC1 rpoC2

7. Preliminary Data rpoB rpoC1 rpoC2

8. psb chloroplast operon psbI psbK mRNA psbI psbK

9. Preliminary Data psbI psbK

10. A Simplified Version Model the curve using an exponential function.

11. A Simplified Version Because we use the same threshold for each quantity, we will assume that Nt is the same for each quantity.

12. Example

13. Example

14. Adding Complexity Need to estimate variability for N0 The process may not be 100% efficient. We probably get less than double the amount with each cycle. The shape of the curve shows behavior that does not fit the exponential model. Ntmight be different for the different quantities.

15. Variability Estimation Standard deviation of Ct is estimated between 0.036 cycles and 0.367 cycles, with an average of 0.183 cycles. (Rutledge and Cote)

16. Variability Estimation To get confidence intervals for the percentages, we need to know how these cycle numbers interact.

17. Efficiency E is a number between 0 and 1 that quantifies the efficiency of the doubling process. E can be estimated from the standard curve. Fit a line to the log curve. Es=e^(-slope)+1 Estimates of E range from 0.85 to 1. (Fronhoffs. et. al.)

18. Example

19. Efficiency Can we assume that the efficiency is the same for each quantity? How does efficiency affect variability? (There is evidence that efficiency and threshold cycle are dependent.)

20. The Curve is Not Exponential Logistic growth? Does this change the percentages?

21. Is Nt the same for the different quantities? Probably not. The flourescence measured is affected by mass as well as number. Are the masses known? If not, can we assume that the masses are similar?

22. Summary A simplified version of the problem has a straight-forward solution, which may be enough for general purposes. Reinserting the complexity into the problem leads to interesting statistical issues.

23. References Fronhoffs, et. al. “A method for the rapid construction of cRNA standard curves in quantitative real-time reverse transcription polymerase chain reaction,” Molecular and Cellular Probes (2002) 16, 99-110. Rutledge and Côté “Mathematics of quantitative kinetic PCR and the application of standard curves,” Nucleic Acids Research (2003) 31, no. 16. Swillens, et. al. “Instant evaluation of the absolute initial nuber of cDNA copies from a single real-time PCR curve,” Nucleic Acids Research (2004) 32, no. 6.