Bayesian Learning of MicroRNA Targets from Sequence and Expression Data

1. Bayesian Learning of MicroRNA Targets from Sequence and Expression Data Author: Jim C. Huang etc. Lecturer: Dong Yue Director: Dr. Yufei Huang

2. Outline • The background of miRNA. • The biology model • A Bayesian model for mRNA regulation • Algorithm evaluation • Validating GenMiR++-predicted let-7b targets

3. What is miRNA? • MicroRNAs (miRNA) are single-stranded RNA molecules of about 21-23 nucleotides in length thought to regulate the expression of other genes.

4. The Process of miRNA

5. A movie to explain miRNA. • http://www.rosettagenomics.com/?CategoryID=174

6. Biology Problem • Biologist can not do experiment for all mRNAs and miRNAs . • Some many targets been predicted by TargetScan which is a very popular miRNA target algorithm. • TargetScan use sequence level data, can we use so other kind of data?

7. Microarray data • Red(solid) curve means the expression of mRNA which was repressed by miRNA in specific tissue. • Blue(dashed) curve means the background distribution which is the normal expression mRNA.

8. Thinking the Model biologically for only one target • Looking at the miRNA target which is predicted by TargetScan. What is TargetScan. • 2 If this miRNA is highly expressed in a given tissue? • 3. Whether the expression of a targeted transcript is negatively shifted with respect to a background expression level. • If 2,3 is Yes, it is very likely a target in reality.

9. In situation of Multiple miRNAs • The down-regulation of target mRNAs can subject to the action of multiple miRNAs. • miRNA scores are given according to how much the miRNA expression profile contributed to explaining downregulation of the mRNA expression.

10. Process in general

11. Definition of Bayesian Model is a positive tissue scaling parameter which accounts for differences in hybridization conditions and normalization between the miRNA and mRNA expression data. X and Z are the sets of expression profiles for mRNAs and miRNAs. C is the set of candidate miRNA targets. Prior distribution

12. The goal of Bayesian Model • Find the posterior probability:

13. Graphical model

14. A Bayesian model for mRNA regulation

15. Why we need an approximate method • Require integrating over the parameters • Sum over an exponential number of combinations of miRNA interactions per mRNA.

16. Variational Bayesian Learning of miRNA targets • Observed variables v: X, Z, and C • Unobserved variables u: S • Model parameters η:ΛΓ • The exact posterior is: • The sorrogate distribution is: KL-divergence:

17. Variational Bayesian Learning of miRNA targets • Define: • represents the probability that miRNA k targets mRNA g given the data. • represents the expected values of the regulatory weights. • represent the means and variances of the tissue scaling parameters.

18. Variational Bayesian Learning of miRNA targets

19. How to calculate the score

20. Algorithm evaluation What is fraction of targets detected? # of candidate interactions detected/ # of candidate interactions.

21. Validating GenMiR++-predicted let-7b targets • Predict target for let-7b misregulation in retinoblastoma. • No neural tissue was represented in the expression data used to build GenMiR++.

22. Validating GenMiR++-predicted let-7b targets • Use microarrays to profile 3 retinoblastoma samples and 1 healthy samples. • Let-7b was on average ~50-fold lower in abundance in retinoblastoma verss healthy retina.

23. Validating GenMiR++-predicted let-7b targets

24. Reference [1] Jim C. Huang etc, Bayesian Inference of MicroRNA Targets from Sequence and Expression Data. J. Comput. Biol. 14, 550–563 (2007). [2] Jim C. Huang etc, Using Expression Profiling Data to Identify Human microRNA Targets, nature methods, VOL.4 NO.12, DEC 2007, p1045

25. Summarization • The background of miRNA. • The biology model • A Bayesian model for mRNA regulation • Algorithm evaluation • Validating GenMiR++-predicted let-7b targets.

26. Questions Or Comments?