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

4. 2. predicting the targets
1. mining the pre-miRNA, miRNA
Precursor, Pre-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 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

12. 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): (SCI, IF2011=1.543)
2017/4/26

13. Novel miRNA found by our method1

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

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

16. 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: (SCI, IF2011=1.089) ESI high cited paper
2017/4/26 1

17. Question
UACACUGUGGAUCCGGUGAGGUAGUAGGUUGUAUAGUUUGGAAUAUUACCACCGGUGAACUAUGCAAUUUUCUACCUUACCGGAGACAGAACUCUUCGA
UGAGGUAGUAGGUUGUAUAGUU
------uaca gga U aaua
cugu uccggUGAGGUAG AGGUUGUAUAGUUu 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:

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

28. 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
miR2
miR1
FSmiR
Strength（wij）
Function similarity of targets
Ref: Yungang Xu, et al. Inferring the Soybean (Glycine max) microRNA functional network based on target gene network . Bioinformatics, 2014, 30 (1):

30. 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!

31. Thanks! Quan Zou, PhD&Professor
School of Computer Science and Technology
Tianjin University