SlideShare a Scribd company logo
1 of 15
Inference of target gene regulation via m iRNAs
   during cell senescence by MiRaGE S  erver

               Y-h. Taguchi
            Departm ent of Physics
              Chuo University
1. What is cell senescence?

2. What is m iRNA?

3. Previous works (wet)

4. MiRaGE Method

5. Correlation between target gene regulation
by m iRNA and m iRNA expression change
during cell senescence
1. What is cell senescence?
T cell division cannot continue forever
 he
for incubated cell lines. It m ust stop after
several rounds of proliferation.

                     ⇓
T is called “cell senescence”, which is
 his
believed to be related to aging.
                          aging
Thus, cell senescence is caused by the
interruption of cell divisions, typically by
 cell cycle arrests.
2. What is miRNA?

m iRNA is a kind of non-coding RNA.
m iRNAs are believed to suppress target gene
expression by degradation of m RNAs.
Im portant features:
・T   ypically, there are hundreds kinds of m iRNAs found for
 each species (c.a., ≧1000 for hum an).
・ Each m iRNA targets m ore than hundreds of genes.
・ m iRNA m ainly contributes to cell type change
  (e.g., cancer, defferentiation, diseases)
・Infulence to target gene expression by m iRNA is subtle
 (〜10%) and contexts dependent.
・In spite of that,
  m iRNA critically contributes to the related processes
3. Previous works (wet)

Several researches suggest the contribution of miRNAs 
to cell senescence.

Upregulation of m iRNAs during cell senescence
m iR-34a,m iR-486-5p,m iR-494,m iR-210...

Downregulation of m iRNAs during cell senescence
m iR-15a/b,m iR-20a,m iR-92,m iR-16b....

Induction of cell senescence by suppression of
m iRNA downregulated during cell senescence.
It is likely true that miRNAs contribute to cell senescence.

However, which one?

Dhahbi et al (2011) recently reported the upregulaton of
141(!) m iRNAs and the downregulation of 131(!!)
m iRNAs during cell senescence by deep sequencing.

T reason why lim ited num ber of m iRNAs revealed
  he
significant expression change during cell senescence seem s
to be due to less sensitivity of microarray analysis.
Is it truly critical the down/upregulation of such large
num ber of m iRNAs for cell senescence?
4. MiRaGE Method

In order to select “Critical ” m iRNAs during
cell senescence, regulation of target genes
by m iRNAs is inferred by MiRaGE S  erver.
For details, see

SIGBIO-25-5: S earch of m iRNAs critical for m edulloblastom a
form ation using MiRaGE m ethod
○Y-h. Taguchi(Chuo Univ.) , Jun Yasuda(T     ohoku Univ.)

SIGBIO-25-30:Gene expression regulation during
differentiation from m urine ES cells due to m icroRNA
MiRaGE :
MiRNA Ranking by Gene Expression

                considered
                m iRNA
                                       target
  m iRNA                                gene

                                          VS


   target
   gene                               significantly
                                   up/downregulated?
                  gene       (t test, Wilcoxon test, KS test)
5. Correlation between target gene regulation
by m iRNA and m iRNA expression change
during cell senescence
 A) Confirm ation of independence of cell line
 T Cell Lines:
  wo
 IMR90 : young (PD 30) vs senescent (PD 48)
                  vs
 MRC5 : young (PDL 28) vs senescent (PDL 63)

             m RNA expression change
              (during cell senescence)

                  MiRaGE ⇓ Server

          P-values attributed to each m iRNA
Intersection between N top-ranked significant m iRNAs
     based upon P-values (t test) IMR90 vs MRC5
                                                                     100%
  down                        10




                                                                            % of com m on m iRNAs
regulation
             binomial: -log
                                                             random
                              P=0.05                                 30%
                              0
                                N 500      1500 N 500       1500
                              10
             P10




                                                                     100%
                              4                             random
   up                                                                20%
regulation           P=0.05
                                   Thus, the results are cell line
                                   independent (possibly robust)
B) Confirm ation of reciprocal relationship between m iRNA
expression change and P­value (upregulation of target genes)
   IMR90,NGS




                                                                 Correlation Coefficient
   # of m iRNAs


                  700                                     0.35


                  100                                     0.05
                        quality score     quality score
           -log




                   2                      m iRDeep2:
                         P=0.05           genom e alignm ent
            10




                   1      01  10 2 10 4   program for m iRNA-seq
           P




                                            Threshold
                        quality score       Value
                        (m iRDeep2)
Candidates of m iRNAs downregulaed
          (target genes are upreglated)




NMRC : Norm alied m iRNA Read Counts P<0.05 RFC>1.0
SCORE : m iRDeeps score
P-value : upregulation of target genes
RFC : Reciprocal Fold Change : young/senescent
Candidates of m iRNAs upregulaed
         (target genes are downreglated)




NMRC : Norm alied m iRNA Read Counts P<0.05 FC>1.0
SCORE : m iRDeeps score
P-value : downregulation of target genes
FC : Fold Change : senescent/young
Candidates of m iRNAs upregulaed
  (target genes are downreglated) continued




NMRC : Norm alied m iRNA Read Counts P<0.05 FC>1.0
SCORE : m iRDeeps score
P-value : downregulation of targe genes
FC : Fold Change : senescent/young
6. Summary & Conclusion

1. Selection of m iRNAs com m only
   up/downregulated during cell senescence

2. Reciprocal relationship between target gene
 regulation and m iRNA expression change

3. Reduction of num ber of critical candidate
   m iRNAs during cell senescence
   (down: 131 ⇒10, up: 141 ⇒ 32)

More Related Content

What's hot

MiRNA Agomir/Antagomir Synthesis Services
MiRNA Agomir/Antagomir Synthesis ServicesMiRNA Agomir/Antagomir Synthesis Services
MiRNA Agomir/Antagomir Synthesis ServicesStella Evelyn
 
MiRNA Agomir & Antagomir synthesis service
MiRNA Agomir & Antagomir synthesis serviceMiRNA Agomir & Antagomir synthesis service
MiRNA Agomir & Antagomir synthesis serviceCreative Biogene
 
Epigenetic silencing of MGMT (O6-methylguanine DNA methyltransferase) gene in...
Epigenetic silencing of MGMT (O6-methylguanine DNA methyltransferase) gene in...Epigenetic silencing of MGMT (O6-methylguanine DNA methyltransferase) gene in...
Epigenetic silencing of MGMT (O6-methylguanine DNA methyltransferase) gene in...arman170701
 
Gentic code
Gentic codeGentic code
Gentic codenazish66
 
Gene silencing techniques for crop improvement
Gene silencing techniques for crop improvementGene silencing techniques for crop improvement
Gene silencing techniques for crop improvementJhilickBanerjee
 
Pre trans splicing gene therapy
Pre trans splicing gene therapyPre trans splicing gene therapy
Pre trans splicing gene therapyfaraharooj
 
Restriction enzymes genetic enginering
Restriction enzymes genetic engineringRestriction enzymes genetic enginering
Restriction enzymes genetic engineringMadhusudhana Malaka
 
Inheritence of dna methylation
Inheritence of dna methylationInheritence of dna methylation
Inheritence of dna methylationsonam mahawar
 
64 eukaryoticgenecontrol2008
64 eukaryoticgenecontrol200864 eukaryoticgenecontrol2008
64 eukaryoticgenecontrol2008sbarkanic
 
Initiation and termination codons , mutation and genetic code
Initiation and termination codons , mutation and genetic codeInitiation and termination codons , mutation and genetic code
Initiation and termination codons , mutation and genetic codegohil sanjay bhagvanji
 
Restriction enzymes treatment of DNA
Restriction enzymes treatment of DNARestriction enzymes treatment of DNA
Restriction enzymes treatment of DNAHadia Haroon
 
Genetic code deciphering propertie and code dictionary.
Genetic code deciphering propertie and code dictionary.Genetic code deciphering propertie and code dictionary.
Genetic code deciphering propertie and code dictionary.HEENA KAUSAR
 

What's hot (20)

MiRNA Agomir/Antagomir Synthesis Services
MiRNA Agomir/Antagomir Synthesis ServicesMiRNA Agomir/Antagomir Synthesis Services
MiRNA Agomir/Antagomir Synthesis Services
 
MiRNA Agomir & Antagomir synthesis service
MiRNA Agomir & Antagomir synthesis serviceMiRNA Agomir & Antagomir synthesis service
MiRNA Agomir & Antagomir synthesis service
 
RNA MEDIATED GENE SILENCING IN PLANT
RNA MEDIATED GENE SILENCING IN PLANTRNA MEDIATED GENE SILENCING IN PLANT
RNA MEDIATED GENE SILENCING IN PLANT
 
Epigenetic silencing of MGMT (O6-methylguanine DNA methyltransferase) gene in...
Epigenetic silencing of MGMT (O6-methylguanine DNA methyltransferase) gene in...Epigenetic silencing of MGMT (O6-methylguanine DNA methyltransferase) gene in...
Epigenetic silencing of MGMT (O6-methylguanine DNA methyltransferase) gene in...
 
Genetic code
Genetic codeGenetic code
Genetic code
 
Rna interfernce ppt
Rna interfernce pptRna interfernce ppt
Rna interfernce ppt
 
Genetic code
Genetic codeGenetic code
Genetic code
 
Gentic code
Gentic codeGentic code
Gentic code
 
Restriction enzymes
Restriction enzymesRestriction enzymes
Restriction enzymes
 
Gene silencing techniques for crop improvement
Gene silencing techniques for crop improvementGene silencing techniques for crop improvement
Gene silencing techniques for crop improvement
 
Pre trans splicing gene therapy
Pre trans splicing gene therapyPre trans splicing gene therapy
Pre trans splicing gene therapy
 
Restriction enzymes genetic enginering
Restriction enzymes genetic engineringRestriction enzymes genetic enginering
Restriction enzymes genetic enginering
 
Genetic code
Genetic codeGenetic code
Genetic code
 
Inheritence of dna methylation
Inheritence of dna methylationInheritence of dna methylation
Inheritence of dna methylation
 
64 eukaryoticgenecontrol2008
64 eukaryoticgenecontrol200864 eukaryoticgenecontrol2008
64 eukaryoticgenecontrol2008
 
Initiation and termination codons , mutation and genetic code
Initiation and termination codons , mutation and genetic codeInitiation and termination codons , mutation and genetic code
Initiation and termination codons , mutation and genetic code
 
Gentic code
Gentic codeGentic code
Gentic code
 
Genitic code
Genitic codeGenitic code
Genitic code
 
Restriction enzymes treatment of DNA
Restriction enzymes treatment of DNARestriction enzymes treatment of DNA
Restriction enzymes treatment of DNA
 
Genetic code deciphering propertie and code dictionary.
Genetic code deciphering propertie and code dictionary.Genetic code deciphering propertie and code dictionary.
Genetic code deciphering propertie and code dictionary.
 

Viewers also liked

Competitive target gene regulation by promoter methylation and miRNA
Competitive target gene regulation by promoter methylation and miRNACompetitive target gene regulation by promoter methylation and miRNA
Competitive target gene regulation by promoter methylation and miRNAY-h Taguchi
 
Principal component analysis for bacterial proteomic analysis
Principal component analysis for bacterial proteomic analysis Principal component analysis for bacterial proteomic analysis
Principal component analysis for bacterial proteomic analysis Y-h Taguchi
 
Analysis of gene expression regulation by miRNA using MiRaGE method
Analysis of gene expression regulation by miRNA using MiRaGE methodAnalysis of gene expression regulation by miRNA using MiRaGE method
Analysis of gene expression regulation by miRNA using MiRaGE methodY-h Taguchi
 
Refined blood-borne miRNome of human diseases via PCA-based feature extraction
Refined blood-borne miRNome of human diseases via PCA-based feature extractionRefined blood-borne miRNome of human diseases via PCA-based feature extraction
Refined blood-borne miRNome of human diseases via PCA-based feature extractionY-h Taguchi
 
Search of miRNAs critical for medulloblastoma formation using MiRaGE method
Search of miRNAs critical for medulloblastoma formation using MiRaGE methodSearch of miRNAs critical for medulloblastoma formation using MiRaGE method
Search of miRNAs critical for medulloblastoma formation using MiRaGE methodY-h Taguchi
 
Inference of gene expression regulation by miRNA using MiRaGE method
Inference of gene expression regulation by miRNA using MiRaGE methodInference of gene expression regulation by miRNA using MiRaGE method
Inference of gene expression regulation by miRNA using MiRaGE methodY-h Taguchi
 
Describe picture using present continuous
Describe picture using present continuousDescribe picture using present continuous
Describe picture using present continuousSergio Gómez Rodriguez
 
MiRaGE: Inference of gene expression regulation via microRNA transfection II
MiRaGE: Inference of gene expression regulation via microRNA transfection IIMiRaGE: Inference of gene expression regulation via microRNA transfection II
MiRaGE: Inference of gene expression regulation via microRNA transfection IIY-h Taguchi
 
Disease suppressive soil has both diverse and uniform ecology : Modeling and ...
Disease suppressive soil has both diverse and uniform ecology : Modeling and ...Disease suppressive soil has both diverse and uniform ecology : Modeling and ...
Disease suppressive soil has both diverse and uniform ecology : Modeling and ...Y-h Taguchi
 
TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasi...
TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasi...TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasi...
TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasi...Y-h Taguchi
 
Describe picture using present continuous
Describe picture using present continuousDescribe picture using present continuous
Describe picture using present continuousSergio Gómez Rodriguez
 
Inference of gene expression regulation by miRNA using MiRaGE method
Inference of gene expression regulation by miRNA using MiRaGE methodInference of gene expression regulation by miRNA using MiRaGE method
Inference of gene expression regulation by miRNA using MiRaGE methodY-h Taguchi
 
中央大学学術講演会(2013年6月15日)ゲノム科学でわかること
中央大学学術講演会(2013年6月15日)ゲノム科学でわかること中央大学学術講演会(2013年6月15日)ゲノム科学でわかること
中央大学学術講演会(2013年6月15日)ゲノム科学でわかることY-h Taguchi
 

Viewers also liked (19)

Ireland powerpint
Ireland powerpintIreland powerpint
Ireland powerpint
 
Competitive target gene regulation by promoter methylation and miRNA
Competitive target gene regulation by promoter methylation and miRNACompetitive target gene regulation by promoter methylation and miRNA
Competitive target gene regulation by promoter methylation and miRNA
 
Principal component analysis for bacterial proteomic analysis
Principal component analysis for bacterial proteomic analysis Principal component analysis for bacterial proteomic analysis
Principal component analysis for bacterial proteomic analysis
 
Analysis of gene expression regulation by miRNA using MiRaGE method
Analysis of gene expression regulation by miRNA using MiRaGE methodAnalysis of gene expression regulation by miRNA using MiRaGE method
Analysis of gene expression regulation by miRNA using MiRaGE method
 
Refined blood-borne miRNome of human diseases via PCA-based feature extraction
Refined blood-borne miRNome of human diseases via PCA-based feature extractionRefined blood-borne miRNome of human diseases via PCA-based feature extraction
Refined blood-borne miRNome of human diseases via PCA-based feature extraction
 
Search of miRNAs critical for medulloblastoma formation using MiRaGE method
Search of miRNAs critical for medulloblastoma formation using MiRaGE methodSearch of miRNAs critical for medulloblastoma formation using MiRaGE method
Search of miRNAs critical for medulloblastoma formation using MiRaGE method
 
Inference of gene expression regulation by miRNA using MiRaGE method
Inference of gene expression regulation by miRNA using MiRaGE methodInference of gene expression regulation by miRNA using MiRaGE method
Inference of gene expression regulation by miRNA using MiRaGE method
 
Vikings
VikingsVikings
Vikings
 
Food
FoodFood
Food
 
Describe picture using present continuous
Describe picture using present continuousDescribe picture using present continuous
Describe picture using present continuous
 
MiRaGE: Inference of gene expression regulation via microRNA transfection II
MiRaGE: Inference of gene expression regulation via microRNA transfection IIMiRaGE: Inference of gene expression regulation via microRNA transfection II
MiRaGE: Inference of gene expression regulation via microRNA transfection II
 
Disease suppressive soil has both diverse and uniform ecology : Modeling and ...
Disease suppressive soil has both diverse and uniform ecology : Modeling and ...Disease suppressive soil has both diverse and uniform ecology : Modeling and ...
Disease suppressive soil has both diverse and uniform ecology : Modeling and ...
 
Tema 3 desarrollado
Tema 3 desarrolladoTema 3 desarrollado
Tema 3 desarrollado
 
Jobs presentation
Jobs presentationJobs presentation
Jobs presentation
 
TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasi...
TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasi...TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasi...
TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasi...
 
Describe picture using present continuous
Describe picture using present continuousDescribe picture using present continuous
Describe picture using present continuous
 
Inference of gene expression regulation by miRNA using MiRaGE method
Inference of gene expression regulation by miRNA using MiRaGE methodInference of gene expression regulation by miRNA using MiRaGE method
Inference of gene expression regulation by miRNA using MiRaGE method
 
中央大学学術講演会(2013年6月15日)ゲノム科学でわかること
中央大学学術講演会(2013年6月15日)ゲノム科学でわかること中央大学学術講演会(2013年6月15日)ゲノム科学でわかること
中央大学学術講演会(2013年6月15日)ゲノム科学でわかること
 
Uas tifl
Uas tiflUas tifl
Uas tifl
 

Similar to Inference of target gene regulation via miRNAs during cell senescence by MiRaGE Server

Non coding RNA as targets in drug discovery.pptx
Non coding RNA as targets in drug discovery.pptxNon coding RNA as targets in drug discovery.pptx
Non coding RNA as targets in drug discovery.pptxLijoMani
 
Gene expression noise, regulation, and noise propagation - Erik van Nimwegen
Gene expression noise, regulation, and noise propagation - Erik van NimwegenGene expression noise, regulation, and noise propagation - Erik van Nimwegen
Gene expression noise, regulation, and noise propagation - Erik van NimwegenLake Como School of Advanced Studies
 
Liu_Jiangyuan_1201662_Presentation
Liu_Jiangyuan_1201662_PresentationLiu_Jiangyuan_1201662_Presentation
Liu_Jiangyuan_1201662_Presentation姜圆 刘
 
DJSobczynski_Regulus_Internship
DJSobczynski_Regulus_InternshipDJSobczynski_Regulus_Internship
DJSobczynski_Regulus_InternshipDaniel Sobczynski
 
Coronavirus and NMD (nonsense mediated mRNA decay)
Coronavirus and NMD (nonsense mediated mRNA decay)Coronavirus and NMD (nonsense mediated mRNA decay)
Coronavirus and NMD (nonsense mediated mRNA decay)MD ROBEL AHMED
 
Rt2 profilerbrochure
Rt2 profilerbrochureRt2 profilerbrochure
Rt2 profilerbrochureElsa von Licy
 
RNAi silencing- miRNA and siRNA and its applications.pdf
RNAi silencing- miRNA and siRNA and its applications.pdfRNAi silencing- miRNA and siRNA and its applications.pdf
RNAi silencing- miRNA and siRNA and its applications.pdfKristu Jayanti College
 
Oligoinformatics And Drug Development
Oligoinformatics And Drug DevelopmentOligoinformatics And Drug Development
Oligoinformatics And Drug DevelopmentMorten Lindow
 
2013 transcription
2013 transcription2013 transcription
2013 transcriptionkuldip sodhi
 
2013 transcription
2013 transcription2013 transcription
2013 transcriptionkuldip sodhi
 
ShRNA-specific regulation of FMNL2 expression in P19 cells
ShRNA-specific regulation of FMNL2 expression in P19 cellsShRNA-specific regulation of FMNL2 expression in P19 cells
ShRNA-specific regulation of FMNL2 expression in P19 cellsYousefLayyous
 
Chronic myeloid leukemia dr. varun
Chronic  myeloid  leukemia  dr. varunChronic  myeloid  leukemia  dr. varun
Chronic myeloid leukemia dr. varunVarun Goel
 
BiPday 2014 --Creanza Teresa
BiPday 2014 --Creanza TeresaBiPday 2014 --Creanza Teresa
BiPday 2014 --Creanza Teresaeventi-ITBbari
 
''Translational Repression and eIF4A2 Activity Are Critical for MicroRNA-Medi...
''Translational Repression and eIF4A2 Activity Are Critical for MicroRNA-Medi...''Translational Repression and eIF4A2 Activity Are Critical for MicroRNA-Medi...
''Translational Repression and eIF4A2 Activity Are Critical for MicroRNA-Medi...Wisdom Deebeke Kate
 

Similar to Inference of target gene regulation via miRNAs during cell senescence by MiRaGE Server (20)

Non coding RNA as targets in drug discovery.pptx
Non coding RNA as targets in drug discovery.pptxNon coding RNA as targets in drug discovery.pptx
Non coding RNA as targets in drug discovery.pptx
 
Gene expression noise, regulation, and noise propagation - Erik van Nimwegen
Gene expression noise, regulation, and noise propagation - Erik van NimwegenGene expression noise, regulation, and noise propagation - Erik van Nimwegen
Gene expression noise, regulation, and noise propagation - Erik van Nimwegen
 
Liu_Jiangyuan_1201662_Presentation
Liu_Jiangyuan_1201662_PresentationLiu_Jiangyuan_1201662_Presentation
Liu_Jiangyuan_1201662_Presentation
 
DJSobczynski_Regulus_Internship
DJSobczynski_Regulus_InternshipDJSobczynski_Regulus_Internship
DJSobczynski_Regulus_Internship
 
Mirna 2017
Mirna 2017Mirna 2017
Mirna 2017
 
Coronavirus and NMD (nonsense mediated mRNA decay)
Coronavirus and NMD (nonsense mediated mRNA decay)Coronavirus and NMD (nonsense mediated mRNA decay)
Coronavirus and NMD (nonsense mediated mRNA decay)
 
P 53 Tumour Biology
P 53 Tumour BiologyP 53 Tumour Biology
P 53 Tumour Biology
 
Rt2 profilerbrochure
Rt2 profilerbrochureRt2 profilerbrochure
Rt2 profilerbrochure
 
sirna and mirna
sirna and mirnasirna and mirna
sirna and mirna
 
RNAi silencing- miRNA and siRNA and its applications.pdf
RNAi silencing- miRNA and siRNA and its applications.pdfRNAi silencing- miRNA and siRNA and its applications.pdf
RNAi silencing- miRNA and siRNA and its applications.pdf
 
Rna synthesis and processing
Rna synthesis  and processing Rna synthesis  and processing
Rna synthesis and processing
 
Oligoinformatics And Drug Development
Oligoinformatics And Drug DevelopmentOligoinformatics And Drug Development
Oligoinformatics And Drug Development
 
2013 transcription
2013 transcription2013 transcription
2013 transcription
 
2013 transcription
2013 transcription2013 transcription
2013 transcription
 
ShRNA-specific regulation of FMNL2 expression in P19 cells
ShRNA-specific regulation of FMNL2 expression in P19 cellsShRNA-specific regulation of FMNL2 expression in P19 cells
ShRNA-specific regulation of FMNL2 expression in P19 cells
 
Inflammation 2013
Inflammation 2013Inflammation 2013
Inflammation 2013
 
Pathways07 mi rna
Pathways07 mi rnaPathways07 mi rna
Pathways07 mi rna
 
Chronic myeloid leukemia dr. varun
Chronic  myeloid  leukemia  dr. varunChronic  myeloid  leukemia  dr. varun
Chronic myeloid leukemia dr. varun
 
BiPday 2014 --Creanza Teresa
BiPday 2014 --Creanza TeresaBiPday 2014 --Creanza Teresa
BiPday 2014 --Creanza Teresa
 
''Translational Repression and eIF4A2 Activity Are Critical for MicroRNA-Medi...
''Translational Repression and eIF4A2 Activity Are Critical for MicroRNA-Medi...''Translational Repression and eIF4A2 Activity Are Critical for MicroRNA-Medi...
''Translational Repression and eIF4A2 Activity Are Critical for MicroRNA-Medi...
 

More from Y-h Taguchi

Tensor decomposition based and principal component analysis based unsupervise...
Tensor decomposition based and principal component analysis based unsupervise...Tensor decomposition based and principal component analysis based unsupervise...
Tensor decomposition based and principal component analysis based unsupervise...Y-h Taguchi
 
主成分分析を用いた教師なし学習による筋萎縮性側索硬化症とがんの遺伝的関連性の解明
主成分分析を用いた教師なし学習による筋萎縮性側索硬化症とがんの遺伝的関連性の解明主成分分析を用いた教師なし学習による筋萎縮性側索硬化症とがんの遺伝的関連性の解明
主成分分析を用いた教師なし学習による筋萎縮性側索硬化症とがんの遺伝的関連性の解明Y-h Taguchi
 
Tensor decomposition­based unsupervised feature extraction identified the un...
Tensor decomposition­based unsupervised  feature extraction identified the un...Tensor decomposition­based unsupervised  feature extraction identified the un...
Tensor decomposition­based unsupervised feature extraction identified the un...Y-h Taguchi
 
Tensor decomposition ­based unsupervised feature extraction applied to matrix...
Tensor decomposition ­based unsupervised feature extraction applied to matrix...Tensor decomposition ­based unsupervised feature extraction applied to matrix...
Tensor decomposition ­based unsupervised feature extraction applied to matrix...Y-h Taguchi
 
遺伝子発現プロファイルからの 薬剤標的タンパクの統計的推定法の開発
遺伝子発現プロファイルからの 薬剤標的タンパクの統計的推定法の開発遺伝子発現プロファイルからの 薬剤標的タンパクの統計的推定法の開発
遺伝子発現プロファイルからの 薬剤標的タンパクの統計的推定法の開発Y-h Taguchi
 
Identification of Candidate Drugs for Heart Failure using Tensor Decompositio...
Identification of Candidate Drugs for Heart Failure using Tensor Decompositio...Identification of Candidate Drugs for Heart Failure using Tensor Decompositio...
Identification of Candidate Drugs for Heart Failure using Tensor Decompositio...Y-h Taguchi
 
Rectified factor networks for biclustering of omics data
Rectified factor networks for biclustering of omics dataRectified factor networks for biclustering of omics data
Rectified factor networks for biclustering of omics dataY-h Taguchi
 
テンソル分解を用いた教師なし学習による変数選択
テンソル分解を用いた教師なし学習による変数選択テンソル分解を用いた教師なし学習による変数選択
テンソル分解を用いた教師なし学習による変数選択Y-h Taguchi
 
主成分分析を用いた教師なし学習による変数選択を用いたヒストン脱アセチル化酵素阻害剤の機能探索
主成分分析を用いた教師なし学習による変数選択を用いたヒストン脱アセチル化酵素阻害剤の機能探索主成分分析を用いた教師なし学習による変数選択を用いたヒストン脱アセチル化酵素阻害剤の機能探索
主成分分析を用いた教師なし学習による変数選択を用いたヒストン脱アセチル化酵素阻害剤の機能探索Y-h Taguchi
 
『主成分分析を用いた教師なし学習による変数選択』 を用いたデング出血熱原因遺伝子の推定
『主成分分析を用いた教師なし学習による変数選択』 を用いたデング出血熱原因遺伝子の推定『主成分分析を用いた教師なし学習による変数選択』 を用いたデング出血熱原因遺伝子の推定
『主成分分析を用いた教師なし学習による変数選択』 を用いたデング出血熱原因遺伝子の推定Y-h Taguchi
 
miRNA-mRNA相互作用同定を用いた 腎芽腫関連遺伝子の推定
miRNA-mRNA相互作用同定を用いた 腎芽腫関連遺伝子の推定miRNA-mRNA相互作用同定を用いた 腎芽腫関連遺伝子の推定
miRNA-mRNA相互作用同定を用いた 腎芽腫関連遺伝子の推定Y-h Taguchi
 
Principal component analysis based unsupervised feature extraction applied to...
Principal component analysis based unsupervised feature extraction applied to...Principal component analysis based unsupervised feature extraction applied to...
Principal component analysis based unsupervised feature extraction applied to...Y-h Taguchi
 
microRNA-mRNA interaction identification in Wilms tumor using principal compo...
microRNA-mRNA interaction identification in Wilms tumor using principal compo...microRNA-mRNA interaction identification in Wilms tumor using principal compo...
microRNA-mRNA interaction identification in Wilms tumor using principal compo...Y-h Taguchi
 
Comprehensive analysis of transcriptome andmetabolome analysis in Intrahepati...
Comprehensive analysis of transcriptome andmetabolome analysis in Intrahepati...Comprehensive analysis of transcriptome andmetabolome analysis in Intrahepati...
Comprehensive analysis of transcriptome andmetabolome analysis in Intrahepati...Y-h Taguchi
 
主成分分析を用いた教師なし学習による出芽酵母 の時間周期遺伝子発現プロファイルの解析
主成分分析を用いた教師なし学習による出芽酵母 の時間周期遺伝子発現プロファイルの解析主成分分析を用いた教師なし学習による出芽酵母 の時間周期遺伝子発現プロファイルの解析
主成分分析を用いた教師なし学習による出芽酵母 の時間周期遺伝子発現プロファイルの解析Y-h Taguchi
 
PCAを用いた2群の有意差検定
PCAを用いた2群の有意差検定PCAを用いた2群の有意差検定
PCAを用いた2群の有意差検定Y-h Taguchi
 
SFRP1 is a possible candidate for epigenetic therapy in non­small cell lung ...
SFRP1 is a possible candidate for epigenetic  therapy in non­small cell lung ...SFRP1 is a possible candidate for epigenetic  therapy in non­small cell lung ...
SFRP1 is a possible candidate for epigenetic therapy in non­small cell lung ...Y-h Taguchi
 
A cross-species bi-clustering approach to identifying conserved co-regulated ...
A cross-species bi-clustering approach to identifying conserved co-regulated ...A cross-species bi-clustering approach to identifying conserved co-regulated ...
A cross-species bi-clustering approach to identifying conserved co-regulated ...Y-h Taguchi
 
主成分分析を用いた教師なし学習による変数選択法を用いたがんにおけるmRNA-miRNA相互作用のより信頼性のある同定
主成分分析を用いた教師なし学習による変数選択法を用いたがんにおけるmRNA-miRNA相互作用のより信頼性のある同定主成分分析を用いた教師なし学習による変数選択法を用いたがんにおけるmRNA-miRNA相互作用のより信頼性のある同定
主成分分析を用いた教師なし学習による変数選択法を用いたがんにおけるmRNA-miRNA相互作用のより信頼性のある同定Y-h Taguchi
 
Identification of aberrant gene expression associated with aberrant promoter ...
Identification of aberrant gene expression associated with aberrant promoter ...Identification of aberrant gene expression associated with aberrant promoter ...
Identification of aberrant gene expression associated with aberrant promoter ...Y-h Taguchi
 

More from Y-h Taguchi (20)

Tensor decomposition based and principal component analysis based unsupervise...
Tensor decomposition based and principal component analysis based unsupervise...Tensor decomposition based and principal component analysis based unsupervise...
Tensor decomposition based and principal component analysis based unsupervise...
 
主成分分析を用いた教師なし学習による筋萎縮性側索硬化症とがんの遺伝的関連性の解明
主成分分析を用いた教師なし学習による筋萎縮性側索硬化症とがんの遺伝的関連性の解明主成分分析を用いた教師なし学習による筋萎縮性側索硬化症とがんの遺伝的関連性の解明
主成分分析を用いた教師なし学習による筋萎縮性側索硬化症とがんの遺伝的関連性の解明
 
Tensor decomposition­based unsupervised feature extraction identified the un...
Tensor decomposition­based unsupervised  feature extraction identified the un...Tensor decomposition­based unsupervised  feature extraction identified the un...
Tensor decomposition­based unsupervised feature extraction identified the un...
 
Tensor decomposition ­based unsupervised feature extraction applied to matrix...
Tensor decomposition ­based unsupervised feature extraction applied to matrix...Tensor decomposition ­based unsupervised feature extraction applied to matrix...
Tensor decomposition ­based unsupervised feature extraction applied to matrix...
 
遺伝子発現プロファイルからの 薬剤標的タンパクの統計的推定法の開発
遺伝子発現プロファイルからの 薬剤標的タンパクの統計的推定法の開発遺伝子発現プロファイルからの 薬剤標的タンパクの統計的推定法の開発
遺伝子発現プロファイルからの 薬剤標的タンパクの統計的推定法の開発
 
Identification of Candidate Drugs for Heart Failure using Tensor Decompositio...
Identification of Candidate Drugs for Heart Failure using Tensor Decompositio...Identification of Candidate Drugs for Heart Failure using Tensor Decompositio...
Identification of Candidate Drugs for Heart Failure using Tensor Decompositio...
 
Rectified factor networks for biclustering of omics data
Rectified factor networks for biclustering of omics dataRectified factor networks for biclustering of omics data
Rectified factor networks for biclustering of omics data
 
テンソル分解を用いた教師なし学習による変数選択
テンソル分解を用いた教師なし学習による変数選択テンソル分解を用いた教師なし学習による変数選択
テンソル分解を用いた教師なし学習による変数選択
 
主成分分析を用いた教師なし学習による変数選択を用いたヒストン脱アセチル化酵素阻害剤の機能探索
主成分分析を用いた教師なし学習による変数選択を用いたヒストン脱アセチル化酵素阻害剤の機能探索主成分分析を用いた教師なし学習による変数選択を用いたヒストン脱アセチル化酵素阻害剤の機能探索
主成分分析を用いた教師なし学習による変数選択を用いたヒストン脱アセチル化酵素阻害剤の機能探索
 
『主成分分析を用いた教師なし学習による変数選択』 を用いたデング出血熱原因遺伝子の推定
『主成分分析を用いた教師なし学習による変数選択』 を用いたデング出血熱原因遺伝子の推定『主成分分析を用いた教師なし学習による変数選択』 を用いたデング出血熱原因遺伝子の推定
『主成分分析を用いた教師なし学習による変数選択』 を用いたデング出血熱原因遺伝子の推定
 
miRNA-mRNA相互作用同定を用いた 腎芽腫関連遺伝子の推定
miRNA-mRNA相互作用同定を用いた 腎芽腫関連遺伝子の推定miRNA-mRNA相互作用同定を用いた 腎芽腫関連遺伝子の推定
miRNA-mRNA相互作用同定を用いた 腎芽腫関連遺伝子の推定
 
Principal component analysis based unsupervised feature extraction applied to...
Principal component analysis based unsupervised feature extraction applied to...Principal component analysis based unsupervised feature extraction applied to...
Principal component analysis based unsupervised feature extraction applied to...
 
microRNA-mRNA interaction identification in Wilms tumor using principal compo...
microRNA-mRNA interaction identification in Wilms tumor using principal compo...microRNA-mRNA interaction identification in Wilms tumor using principal compo...
microRNA-mRNA interaction identification in Wilms tumor using principal compo...
 
Comprehensive analysis of transcriptome andmetabolome analysis in Intrahepati...
Comprehensive analysis of transcriptome andmetabolome analysis in Intrahepati...Comprehensive analysis of transcriptome andmetabolome analysis in Intrahepati...
Comprehensive analysis of transcriptome andmetabolome analysis in Intrahepati...
 
主成分分析を用いた教師なし学習による出芽酵母 の時間周期遺伝子発現プロファイルの解析
主成分分析を用いた教師なし学習による出芽酵母 の時間周期遺伝子発現プロファイルの解析主成分分析を用いた教師なし学習による出芽酵母 の時間周期遺伝子発現プロファイルの解析
主成分分析を用いた教師なし学習による出芽酵母 の時間周期遺伝子発現プロファイルの解析
 
PCAを用いた2群の有意差検定
PCAを用いた2群の有意差検定PCAを用いた2群の有意差検定
PCAを用いた2群の有意差検定
 
SFRP1 is a possible candidate for epigenetic therapy in non­small cell lung ...
SFRP1 is a possible candidate for epigenetic  therapy in non­small cell lung ...SFRP1 is a possible candidate for epigenetic  therapy in non­small cell lung ...
SFRP1 is a possible candidate for epigenetic therapy in non­small cell lung ...
 
A cross-species bi-clustering approach to identifying conserved co-regulated ...
A cross-species bi-clustering approach to identifying conserved co-regulated ...A cross-species bi-clustering approach to identifying conserved co-regulated ...
A cross-species bi-clustering approach to identifying conserved co-regulated ...
 
主成分分析を用いた教師なし学習による変数選択法を用いたがんにおけるmRNA-miRNA相互作用のより信頼性のある同定
主成分分析を用いた教師なし学習による変数選択法を用いたがんにおけるmRNA-miRNA相互作用のより信頼性のある同定主成分分析を用いた教師なし学習による変数選択法を用いたがんにおけるmRNA-miRNA相互作用のより信頼性のある同定
主成分分析を用いた教師なし学習による変数選択法を用いたがんにおけるmRNA-miRNA相互作用のより信頼性のある同定
 
Identification of aberrant gene expression associated with aberrant promoter ...
Identification of aberrant gene expression associated with aberrant promoter ...Identification of aberrant gene expression associated with aberrant promoter ...
Identification of aberrant gene expression associated with aberrant promoter ...
 

Inference of target gene regulation via miRNAs during cell senescence by MiRaGE Server

  • 1. Inference of target gene regulation via m iRNAs during cell senescence by MiRaGE S erver Y-h. Taguchi Departm ent of Physics Chuo University
  • 2. 1. What is cell senescence? 2. What is m iRNA? 3. Previous works (wet) 4. MiRaGE Method 5. Correlation between target gene regulation by m iRNA and m iRNA expression change during cell senescence
  • 3. 1. What is cell senescence? T cell division cannot continue forever he for incubated cell lines. It m ust stop after several rounds of proliferation. ⇓ T is called “cell senescence”, which is his believed to be related to aging. aging Thus, cell senescence is caused by the interruption of cell divisions, typically by cell cycle arrests.
  • 4. 2. What is miRNA? m iRNA is a kind of non-coding RNA. m iRNAs are believed to suppress target gene expression by degradation of m RNAs. Im portant features: ・T ypically, there are hundreds kinds of m iRNAs found for each species (c.a., ≧1000 for hum an). ・ Each m iRNA targets m ore than hundreds of genes. ・ m iRNA m ainly contributes to cell type change (e.g., cancer, defferentiation, diseases) ・Infulence to target gene expression by m iRNA is subtle (〜10%) and contexts dependent. ・In spite of that, m iRNA critically contributes to the related processes
  • 5. 3. Previous works (wet) Several researches suggest the contribution of miRNAs  to cell senescence. Upregulation of m iRNAs during cell senescence m iR-34a,m iR-486-5p,m iR-494,m iR-210... Downregulation of m iRNAs during cell senescence m iR-15a/b,m iR-20a,m iR-92,m iR-16b.... Induction of cell senescence by suppression of m iRNA downregulated during cell senescence.
  • 6. It is likely true that miRNAs contribute to cell senescence. However, which one? Dhahbi et al (2011) recently reported the upregulaton of 141(!) m iRNAs and the downregulation of 131(!!) m iRNAs during cell senescence by deep sequencing. T reason why lim ited num ber of m iRNAs revealed he significant expression change during cell senescence seem s to be due to less sensitivity of microarray analysis. Is it truly critical the down/upregulation of such large num ber of m iRNAs for cell senescence?
  • 7. 4. MiRaGE Method In order to select “Critical ” m iRNAs during cell senescence, regulation of target genes by m iRNAs is inferred by MiRaGE S erver. For details, see SIGBIO-25-5: S earch of m iRNAs critical for m edulloblastom a form ation using MiRaGE m ethod ○Y-h. Taguchi(Chuo Univ.) , Jun Yasuda(T ohoku Univ.) SIGBIO-25-30:Gene expression regulation during differentiation from m urine ES cells due to m icroRNA
  • 8. MiRaGE : MiRNA Ranking by Gene Expression considered m iRNA target m iRNA gene VS target gene significantly up/downregulated? gene (t test, Wilcoxon test, KS test)
  • 9. 5. Correlation between target gene regulation by m iRNA and m iRNA expression change during cell senescence A) Confirm ation of independence of cell line T Cell Lines: wo IMR90 : young (PD 30) vs senescent (PD 48) vs MRC5 : young (PDL 28) vs senescent (PDL 63) m RNA expression change (during cell senescence) MiRaGE ⇓ Server P-values attributed to each m iRNA
  • 10. Intersection between N top-ranked significant m iRNAs based upon P-values (t test) IMR90 vs MRC5 100% down 10 % of com m on m iRNAs regulation binomial: -log random P=0.05 30% 0 N 500 1500 N 500 1500 10 P10 100% 4 random up 20% regulation P=0.05 Thus, the results are cell line independent (possibly robust)
  • 11. B) Confirm ation of reciprocal relationship between m iRNA expression change and P­value (upregulation of target genes) IMR90,NGS Correlation Coefficient # of m iRNAs 700 0.35 100 0.05 quality score quality score -log 2 m iRDeep2: P=0.05 genom e alignm ent 10 1 01 10 2 10 4 program for m iRNA-seq P Threshold quality score Value (m iRDeep2)
  • 12. Candidates of m iRNAs downregulaed (target genes are upreglated) NMRC : Norm alied m iRNA Read Counts P<0.05 RFC>1.0 SCORE : m iRDeeps score P-value : upregulation of target genes RFC : Reciprocal Fold Change : young/senescent
  • 13. Candidates of m iRNAs upregulaed (target genes are downreglated) NMRC : Norm alied m iRNA Read Counts P<0.05 FC>1.0 SCORE : m iRDeeps score P-value : downregulation of target genes FC : Fold Change : senescent/young
  • 14. Candidates of m iRNAs upregulaed (target genes are downreglated) continued NMRC : Norm alied m iRNA Read Counts P<0.05 FC>1.0 SCORE : m iRDeeps score P-value : downregulation of targe genes FC : Fold Change : senescent/young
  • 15. 6. Summary & Conclusion 1. Selection of m iRNAs com m only up/downregulated during cell senescence 2. Reciprocal relationship between target gene regulation and m iRNA expression change 3. Reduction of num ber of critical candidate m iRNAs during cell senescence (down: 131 ⇒10, up: 141 ⇒ 32)