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

2

Background-miRNA

3

Crucial regulatory molecule：

1/3 human genes

cell development

cell proliferation

cell apoptosis

tumorigenesis …

DNA

···

······

mRNA

Precursor, Pre-miRNA

target

mature miRNA

1. mining the pre-miRNA, miRNA

2. predicting the targets

cell nucleus

cytoplasm

Identification of microRNAAUCGUGCAGAGACUAGACUGACAUCGUGCAGAGACUAGACUGACAUCGUGCAGAGACUAGACUGACAUCGUGCAGAGACUAGACUGACAUCGUGCAGAGACUAGACUGAC

>1tgcgcgaauucacccauggauccauucaucuuccaagggcaccagc>2agcgcgaauuccaagucacccauggauccauucaucuggcagcgu>3agucgcgaauucaucaucuuccaagggcacccauggauccaucca

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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.

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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

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Importance of negative samples

Decision Boundary

Positive Training Set

Negative Training Set

Negative Testing Set

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Importance of negative samples

New Decision Boundary

Positive Training Set

New Negative Training Set

Negative Testing Set

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Flow

100nt100nt

Parameter Filter

Prediction Model

Extend

Compute Secondary Structures

Extract

Human CDs

Human Mature microRNAs

Blast

Mature-like Reads

Original NegativeSet

Mined Sequences

Rebuilt

Replaceinnovation point

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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)23/5/3 12/30

Novel miRNA found by our method

1 13/30

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

1 15/30

• PFAM(~2000) VS miRNA family(~2000)

• Troubles– Multiple classes– Few samples– imbalaned

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

23/5/3 1 16/30

Pre-miRNA vs PseudoFasta Fileinput

T19 or Other Family

T99 or Other Family

Family

First layer

Second layer

Third layer

output – prediction result

PseudoPseudo hairpins

like miRNA

T19

T99

Result

Pre-miRNA

Other

Other

miRNA family, such as mir-2

miRNA family, such as let-7

miRNA family, such as lin-4

hierarchical prediction model

Question

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------uaca gga U --- aaua cugu uccggUGAGGUAG AGGUUGUAUAGUUu gg u |||| ||||||||||||| |||||||||||||| || gaca aggccauuccauc uuuaacguaucaag cc uagcuucucaa --g u ugg acca

UACACUGUGGAUCCGGUGAGGUAGUAGGUUGUAUAGUUUGGAAUAUUACCACCGGUGAACUAUGCAAUUUUCUACCUUACCGGAGACAGAACUCUUCGA UGAGGUAGUAGGUUGUAUAGUU

1 18/30

Contents

background

microRNA identification

isomiR

microRNA and disease

outlook

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Why called isomiR?

isoform vs isomiR

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Imprecise and alternative cleavage Modification/addition events SNP RNA editing

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

Materials and methods

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Public databases, in-house sequencing datasets, published data

Bioinformatics & biostatistics Software/script

Molecular biology method

Where does isomiR happen?

across different species normal vs cancer

isomiR data - TCGA

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

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Contents

background

microRNA identification

isomiR

microRNA and disease

outlook

25

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

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Similarity between two microRNAs

(A) (B) (C)

targets of miR1

targets of miR1

targets of miR1

targets of miR2

targets of miR2

targets of miR2

miR2

miR1

miR1

miR2

g1 g2 g4g3

targets network

0.70.8

0.70.9

0.6g1

g2

g4g3

Strength

Strength

Strength （ wij）

Function similarity of targets

0.4 0.5 0.8 0

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!

Quan Zou, PhD&ProfessorSchool of Computer Science and TechnologyTianjin UniversityEmail: zouquan@nclab.nethttp://lab.malab.cn/~zq/

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