1. Predicting MicroRNA Genes and Target Site using Structural and Sequence Features: Machine Learning Approach Malik Yousef Institute of Applied Research, The Galilee Society, Israel Institute of Applied Research, The Galilee Society, Israel Louise Showe LabLouise Showe Lab Wistar Institute, UPENN, USA Wistar Institute, UPENN, USA Neurocomputation Laboratory of CRI-Haifa

2. microRNA Precursor >hsa-mir-1-1 MI0000651 UGGGAAACAUACUUCUUUAUAUGCCCAUAUGGACCUGCUAAGCUA UGGAAUGUAAAGAAGUAUGUA UCUCA Mature > hsa-mir-1-1 uggaauguaaagaaguaugua Cancer genomics: Small RNAs with BIG impacts Paul S. Meltzer, Nature During the past few years, molecular biologists have been stunned by the discovery of hundreds of genes that encode small RNA molecules. MicroRNA expression profiles classify human cancers Jun Lu. Et al,Nature 2005 MicroRNA in cancer analysis of microRNA expression in over 300 individuals shows that microRNA profiles could be of value in cancer diagnosis

3. 2 MicroRNAs Promote Spread of Tumor Cells : By blocking the translation of tumor suppressor genes, miRNAs have been shown to facilitate the development of many types of cancer. Small RNAs Can Prevent Spread of Breast Cancer :The tiny RNAs prevent the spread of cancer by interfering with the expression of genes that give cancer cells the ability to proliferate and migrate MicroRNAs May Be Key To HIV's Ability To Hide, Evade Drugs

4. miRNA processing

5. PART I MICRORNA PREDICTION

6. BayesMiRNAfind: Naïve Bayes For miRNA Gene Prediction 873 outside jobs were processed in 2 weeks on the Wistar Bioinformatics Core cluster. http://bioinfo.wistar.upenn.edu/miRNA/miRNA

7. One-ClassMirnaFind : One-Class microRNA gene prediction Web Server http://wotan.wistar.upenn.edu/mirna_one_class BMC-Algorithms for Molecular Biology

8. Advantage of our tools  Allowing predicting miRNAs for multi-species [ Vir- mir db,Li et al 2007], most of the other tools are species-specific  The input is not limited! (full genome)  Predict also non-conserved miRNA  The features seems to be more accurate describing the miRNA class.

9. Two-Class : The Computation procedure components

10. BayesMiRNA: Mouse Genome (one strand)  Out of 212 mature miRNAs from the mouse genome 135 are at the DNA + strand  Running on a parallel compute cluster with 100 nodes  (http://core.pcbi.upenn.edu/tools/liniactools.html),  The whole computation procedure took about 6.5 days to complete

11. PART II MICRORNA TARGET SITE PREDICTION Bioinformatics Journal

12. miRNA target site prediction Morten Lindow miRNA-group, Bioinformatics Centre University of Copenhagen

13. Performance of NBmiRTar

14. 3’UTR microRNAs MiRanda Naïve Bayes classifier Predicted microRNA targets Orthologs score Summary of microRNA target prediction Classifier Mouse 3’UTR (mavid) Sequence alignment Database Human 3’ UTR Miranda score naïve Bayes score Folding energy Filters Filter

15. NBmiRTar http://wotan.wistar.upenn.edu/NBmiRNAtarget/login.php

16. Results with Human Known Targets miRNANumber of confirmed targets Miranda Predictions Recovery by Miranda BayesMirnaTarget Predictions Recovery by BayesMirna Target NB-filter 0.9 1 1401592 1/1 878430/160108 2 264984 2/2 343802/227239 3 2321312 2/2 806322/260967 4 249556 2/2 240901/219013 5 1563477 1/1 2594231/1202339 6 1294255 1/1 84153161725 7 1596411 1/1 92337162118 8 1381933 1/1 54636034138 9 1329770 1/1 42736145447 10 1328120 1/1 73852147663 Sum1333314101383408210620757 NBmiRTar reduces Miranda prediction by about 75% with recovery rate of 77%. NBmiRTar + NB-filter (threshold 0.9) reduces Miranda prediction by about 81% with recovery rate of 77%.

17. 427 known human mature miRNA MiRanda 2620700 Predictions 50 genes (59 TFs). human 3’UTR Showe Lab Experiment NBmiRTar 32199 Predictions 3909 69 Filters: MiRanda 110 NB 0.9 Orthologs Mouse 3’UTR Genes MiRanda 110 NB 0.9 50 genes that have been shown to be down regulated at the message level after treatment

18. Malik Yousef, Segun Jung, Louise C Showe and Michael K Showe, Learning from Positive Examples when the Negative Class is Undetermined- microRNA gene identification. Algorithms for Molecular Biology, (Accepted)(2008). Malik Yousef, Segun Jung, Andrew V. Kossenkov, Louise C. Showe and Michael K. Showe, Na ï ve Bayes classifier for microRNA target gene identification, Bioinformatics, 15 November 2007; 23: 2987 - 2992 Malik Yousef, Hagit Shatkay, Michael Nebozhyn, Louise C. Showe and Michael K. Showe, Combining Multi-Species Genomic Data for MicroRNA Identification Using Na ï ve Bayes Classifier. Bioinformatics, Vol. 22, No. 11, p. 1325-1334 (2006) Related publications