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

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

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

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

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.

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.

microRNA prediction based on machine learning obvious differences weak generalization

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

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

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

Leyi Wei, Minghong Liao, Yue Gao, Rongrong Ji, Zengyou He 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) 2017/4/26

Novel miRNA found by our method 1

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

miRNA family classification PFAM(~2000) VS miRNA family(~2000) Troubles Multiple classes Few samples imbalaned 1

Quan Zou. , Yaozong Mao, Lingling Hu, Yunfeng Wu, Zhiliang Ji 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) ESI high cited paper 2017/4/26 1

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

1

Contents background microRNA identification isomiR microRNA and disease outlook

Why called isomiR? isoform vs isomiR

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

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*

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

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

Contents background microRNA identification isomiR microRNA and disease outlook

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

Similarity between two microRNAs targets of miR1 targets of miR2 targets of miR1 targets of miR2 targets of miR1 targets of miR2

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

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!

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