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Computational prediction of miRNA and miRNA-disease relationship

Computational prediction of miRNA and miRNA-disease relationship. Quan Zou ( 邹权 ) PH.D.&Professor School of Computer Sci&Tech Tianjin University, China. Contents. background microRNA identification isomiR microRNA and disease outlook. Background- miRNA.

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Computational prediction of miRNA and miRNA-disease relationship

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  1. Computational prediction of miRNA and miRNA-disease relationship Quan Zou (邹权) PH.D.&Professor School of Computer Sci&Tech Tianjin University, China

  2. Contents • background • microRNA identification • isomiR • microRNA and disease • outlook

  3. Background-miRNA Crucial regulatory molecule： 1/3 human genes cell development cell proliferation cell apoptosis tumorigenesis …

  4. Precursor, Pre-miRNA 1. mining the pre-miRNA, miRNA cell nucleus mature miRNA cytoplasm 2. predicting the targets target

  5. Identification of microRNA AUCGUGCAGAGACUAGACUGACAUCGUGCAGAGACUAGACUGACAUCGUGCAGAGACUAGACUGACAUCGUGCAGAGA CUAGACUGACAUCGUGCAGAGACUAG ACUGAC >1 tgcgcgaauucacccauggauccauucaucuuccaagggcaccagc >2 agcgcgaauuccaagucacccauggauccauucaucuggcagcgu >3 agucgcgaauucaucaucuuccaagggcacccauggauccaucca

  6. Ref: Xue C, et al. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics, 2005, 6(1): 310.

  7. Ref: Xue C, et al. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics, 2005, 6(1): 310.

  8. microRNA prediction based on machine learning obvious differences weak generalization

  9. Importance of negative samples Negative Testing Set Positive Training Set Decision Boundary Negative Training Set

  10. Importance of negative samples Negative Testing Set Positive Training Set New Negative Training Set New Decision Boundary

  11. Flow Human CDs Extend Blast 100nt 100nt Human Mature microRNAs Mature-like Reads Compute Secondary Structures Extract Parameter Filter Prediction Model Rebuilt Original Negative Set Mined Sequences innovation point Replace

  12. Leyi Wei, Minghong Liao, Yue Gao, Rongrong Ji, Zengyou He*, Quan Zou(邹权)*. Improved and Promising Identification of Human MicroRNAs by Incorporating a High-quality Negative Set. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2014, 11(1):192-201 (SCI, IF2011=1.543)

  13. Novel miRNA found by our method 1

  14. Dinoflagellates genome (甲藻) Lin, et al. The Symbiodinium kawagutii genome illuminates dinoflagellate gene expression and coral symbiosis. Science. 2015, 350(6261): 691-694.

  15. miRNAfamily classification • PFAM(~2000)VS miRNAfamily(~2000) • Troubles • Multiple classes • Few samples • imbalaned 1

  16. Quan Zou*, Yaozong Mao, Lingling Hu, Yunfeng Wu, Zhiliang Ji*. miRClassify: An advanced web server for miRNA family classification and annotation. Computers in Biology and Medicine. 2014, 45:157-160.(SCI, IF2011=1.089) ESIhigh cited paper 1

  17. Question UGAGGUAGUAGGUUGUAUAGUU UACACUGUGGAUCCGGUGAGGUAGUAGGUUGUAUAGUUUGGAAUAUUACCACCGGUGAACUAUGCAAUUUUCUACCUUACCGGAGACAGAACUCUUCGA ------uaca gga U --- aaua cugu uccggUGAGGUAGAGGUUGUAUAGUUu gg u |||| ||||||||||||| |||||||||||||| || gaca aggccauuccauc uuuaacguaucaag cc u agcuucucaa --g u ugg acca 1

  18. 1

  19. Contents • background • microRNA identification • isomiR • microRNA and disease • outlook

  20. Why called isomiR? • isoform vs isomiR

  21. Background-isomiR • miRNA variants, isomiRs, physiological isoforms • Various length distributions, 5’/3’ ends The annotated miRNA sequence is only one specific isomiR in the miRNA locus • Imprecise and alternative cleavage • Modification/addition events • SNP • RNA editing

  22. Materials and methods Public databases, in-house sequencing datasets, published data Bioinformatics & biostatistics Software/script Molecular biology method Data analysis: Biology/interaction Data analysis: Method/prediction Data analysis: Evolution/miRNA*

  23. Where does isomiR happen? • across different species • normal vs cancer • isomiR data - TCGA

  24. isomiR difference in cancer • 3’ addition: not dominant • IsomiR expression: Stable across different samples • Abnormal isomiR pattern in cancer cells and tissues

  25. Contents • background • microRNA identification • isomiR • microRNA and disease • outlook

  26. Ref:Quan Zou, et al. Prediction of microRNA-disease associations based on social network analysis methods. BioMed Research International. 2015, 2015: 810514

  27. Similarity between two microRNAs (B) (C) (A) targets of miR2 targets of miR1 targets of miR2 targets of miR2 targets of miR1 targets of miR1

  28. miR1 Strength g2 0.7 0.4 0.5 0.8 0 0.8 g1 0.9 g4 0.6 0.7 g3 targets network Strength（wij） miR1 Strength Function similarity of targets 0 0.5 0.8 0.7 g1 g2 g3 g4 FSmiR miR2 miR2 Ref: Yungang Xu, et al. Inferring the Soybean (Glycine max) microRNA functional network based on target gene network . Bioinformatics, 2014, 30 (1):94-103.

  29. Outlook How many novel microRNAs are still left? All the microRNA research methods can be extended to ncRNA and lncRNA isomiR would be the next hot topic in microRNA research Diseases would be the hot spots for ever!

  30. Thanks! Quan Zou, PhD&Professor School of Computer Science and Technology Tianjin University Email: zouquan@nclab.net http://lab.malab.cn/~zq/

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