computational prediction of mirna and mirna-disease relationship
DESCRIPTION
Contents background microRNA identification isomiR microRNA and disease outlookTRANSCRIPT
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
8
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
1 17/30
------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
19
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
22
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: [email protected]://lab.malab.cn/~zq/
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