1. Improving Intergenic 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 Characteristics  Short (22-25nts)  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  Expression of miRNA will reduce the expression of its target genes  Intergenic miRNA gene is located outside gene bodies

4. Basic miRNA problem  Finding miRNA true target genes is not a trivial task  One approach is to make a computational prediction before validating it in wet-lab experiments  one basic challenge of miRNA: Given a miRNA sequence, what is its target genes?

5. 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  Local miRNA-mRNA interaction with positive balance of minimum free energy  Available tools for target genes prediction: PicTar, TargetScan, miRanda,microT, etc.  Most tool’s prediction does not complement each other, because they use different criteria

6. Problem and Opportunity  Problem: Pure computational target genes prediction produces a lot of candidates  Most of them are not validated  Common assumption is that most of them are false positives  Can we shorten the list to include only the strong candidates ?  Opportunity: Lots of publicly available experimental dataset i.e. cDNA microarray, miRNA microarray, etc.  Use the dataset to computationally invalidate some of the target genes

7. 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 its real target genes should be correlated together  The real target genes should be down-expressed whenever the intergenic miRNA is expressed.

8. How to invalidate a target gene prediction  A target gene prediction can be invalidated by using a set of microarray datasets  For Intragenic miRNA target gene:  If a target gene’s expressions has no correlation with the host gene’s expression, we assume that the target gene does not influenced by the host gene  For Intergenic miRNA target gene:  If a target gene behaves inconsistently compared to other target genes, we assume that it might not be affected by the miRNA gene

9. Filtering Intergenic miRNA Target Gene Prediction  Use a combination of 8 prediction tools to produce the initial predictions (union & intersection)  Use a collection of 190 microarray datasets to invalidate some of the predictions  Use a greedy method to approximate the final subset of high-confidence target genes

10. Consistent Target Genes  We need to establish the meaning of consistent target genes  In this context, target gene A and target gene B is consistent if:  For all microarray datasets in which gene A is down-regulated, then gene B is also down-regulated M1M2M3M4…Mk DHX9 ↑↓↑↓↑ ASTE1 ↓↓↓↓↑ C20ORF133 ↓↓↑↓↓ PARP11 ↑↓↓↓↓ SLC32A1 ↓↓↓↓↑ PPAPDC2 ↓↓↑↓↓ SCHIP1 ↑↓↓↓↑ MPST ↑↓↓↓↓

11. Greedy Method  Given a set of target gene predictions, and a collection of microarray dataset:  We wanted to find:  The longest subset of consistent target genes  The highest number of down-regulated target genes in the subset

12. Reasoning  Why we wanted to find:  The longest subset of consistent target genes?  Consistent target genes, on large number of microarray dataset with different experiments, might indicate that they are affected by a common factor, which may be microRNA  The longest subset ensures high probability of including the true target genes  The highest number of down-regulated target genes in the subset?  Since miRNA works by down-regulating target genes, it is desirable to find the largest subset of consistently down-regulated target genes

13. Current Algorithm for i = 0 to K A <- G[i] SigA <- signature(A) Temp_Subset = {SigA} down = countDownExpressedMicroarray(A) for j = 0 to K B <- G[j] SigB <= signature(B) if SigA == SigB Temp_Subset U {SigB} end if end for if (length(Temp_subset) > length(Subset)) && (down > downexpr_cnt) subset = Temp_Subset downexpr_cnt = down end if end for

14. Algorithm Limitations  The algorithm result might be biased based on the first pivot gene expression signature :  Might get stuck on local maxima  Can be solved by prioritizing, sorting of target gene down- expression value, or random selection of pivot gene  The subset is an approximation of high-confidence target genes, but it doesn’t necessarily include all real target genes (because of supporting data limitation)

15. Benchmarking  Compare the performance with other prediction tools, based on:  Number of correct predictions (based on validated target genes)  Number of predictions  The algorithm will use an initial target predictions with:  2, 3, and 4 prediction tools support

16. Performance Comparison

17. Sensitivity-Specificity Comparison

18. Conclusion  In general, the approximation method shows better sensitivity compared to other prediction tools  Specificity can be improved by including only target gene that is supported by more than 2 prediction tools

19. Further Work  Adjusting the scoring function to find the optimum balance between the length of the subset and the number of down-regulated target genes  Implementing a threshold on target gene signaturing to further reduce the specificity