1. Improving miRNA Target Genes Prediction Rikky Wenang Purbojati

2. miRNA  MicroRNA (miRNA) is a class of RNA which is believed to play important roles in gene regulation.  It’s a short (21- to 23-nt) RNAs that bind to the 3 ′ untranslated regions (3 ′ UTRs) of target genes.

3. miRNA Functions  miRNA plays a major role in RNA Induced Silencing Complex (RISC).  miRNAs control the expression of large numbers of genes by:  mRNA degradation  Translational repression  Recent studies indicates it plays a role in cancer development:  Surplus of miRNA might inhibit cell apoptosis process  Deficit of miRNA might cause excess of certain oncogenes

4. RNA Induced Silencing Complex  mRNA degradation  Breaks the structural integrity of a mRNA.  Translational repression  Prevent the mRNA from being translated.

5. Characteristics of miRNA  Short (22-25nts)  Transcripted from a miRNA gene  Intragenic: miRNA gene is located inside a host gene (usually intron region)  Intergenic: miRNA gene is located outside gene bodies  A consistent 5’ and 3’ boundary:  Transcription Start Site  5’ Cap  Poly(A) tail

6. Development of miRNA

7. miRNA General Research Question  Much attention has been directed in miRNA processing and targeting.  Computational-wise, one basic challenge of miRNA: Given a miRNA sequence, what are its target genes?

8. miRNA sequence target prediction  Predict target genes by matching the complement of miRNA sequence.  Two types of complement:  Perfect complement  Imperfect complement Find perfect match for seed (2-8nt)

9. miRNA sequence target prediction  Several requirements for matching:  Strong Watson-Crick base pairing of the 5’ seed (2-8 nts)  Conservation of the miRNA binding site across species  Another approach: thermodynamic rule  Local miRNA-mRNA interaction with positive balance of minimum free energy

10. Problems and Opportunities  Problem: Pure computational target genes prediction produces a lot of candidates  No unifying theory for target gene prediction yet  Most of them are not validated yet  Common assumption is that most of them are false positives  Can we shorten the list to include only the strong candidates ?

11. Problems and Opportunities  Opportunity: Lots of publicly available experimental dataset i.e. cDNA microarray, miRNA microarray, etc.  Use the dataset to computationally validate some of the target genes  Current Research: Preliminary research tries to utilizes the abundance of publicly available microarray data.

12. Assumptions  miRNA works by silencing target genes, thus miRNA gene and target genes should be anti-correlated  Intragenic miRNA are expressed along with the host gene.  a host gene should be anti-correlated with a target gene  Intergenic miRNA does not have a host gene, but we might be able to use available composite (miRNA microarray + cDNA microarray) dataset  If a miRNA is up-regulated in miRNA microarray, then its target genes should be down-regulated in cDNA microarray

13. Current Work  There have been some works related to this idea (i.e. HOCTAR)  However, we can improve it by:  Using a stricter criteria across the microarray data  Using a more diverse data  We expect we will get a much better specifity than the previous method

14. Hoctar Method  Get a list of target genes from 3 different tools (pictar, TargetScan,miranda)  Uses Pearson correlation to determine the correlation coefficient between 2 genes  Include target genes which have correlation below some threshold (-)  Only works for intragenic miRNA

15. Hoctar Method

16. Shortcomings of Hoctar  Uses all probes data even though they are not consistent  Uses only one target gene prediction algorithm approach  Depends on Pearson Correlation, which is sensitive to outliers

17. Improvement Idea (1)  Use only subset of data which probes are all consistent  Treat each probes as different experiments

18. Improvement Idea (2)  Pearson correlation is very sensitive to outliers, alternative solutions:  Uses Rank correlation coefficients instead of Pearson correlation coefficients  Normalize the dataset to normal distribution  Ignore outliers

19. Improvement Idea (3)  In addition to probes consistency and rank correlation, we might use entropy rule in eliminating candidate target genes  Assumption:  Transcript level can be approximated from expression level data  One miRNA transcript can only degrade one mRNA transcript  Thus miRNA expression changes should not be much different from mRNA expression changes

20. Improvement Idea (4)  Uses a larger amount of microarray data  We might be able to include miRNA microarray to further refine target genes list for several miRNA

21. Preliminary Result  GSE9234 dataset (hipoxia/normoxia)  Using only consistency criteria miRNAHost GeneKnown Target Gene HOCTARRefined miR-103-2PANK3GPD1YES miR-103-2PANK3FBW1BNOYES miR-140WWP2HDAC4YES miR-224GABREAPI5NO

22. Refining Intergenic miRNA prediction  Refining intergenic miRNA prediction using microarray dataset is not a trivial task  Microarray can only be used to measure the expression of target genes, but not the miRNA gene  Might have to rely on additional data:  Proxy measurement  miRNA microarray

23. Intergenic miRNA proxy measurement  Putative target gene approximation  use the expression level of a known target genes for that specific intergenic miRNA  If its target genes are consistently down-regulated, then we can assume that the expression level of the intergenic miRNA gene is up-regulated  Cluster miRNA approximation  Some intergenic miRNAs are clustered with each other; according to (Saini et al. 2007) most of these clusters use the same pri-mirNA transcript  Use method 1 for neighboring miRNA to get the intergenic miRNA expression approximation

24. Further Work  Implementation and evaluation  Standardizing composite dataset repository