Next Generation Sequencers and Progress on Omics Research
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Transcript of Next Generation Sequencers and Progress on Omics Research
Next Generation Sequencers and Progress on Omics Research
Harukazu Suzuki PhD.Project Director, RIKEN Omics Science Center, Japan
(Yoshihide Hayashizaki, M.D., Ph.D.)
(Director, RIKEN Omics Science Center)
13 April 2010BioVisionAlexandria Conference 2010
Various types of Next Generation Sequencers
SOLiD
HeliScope
Solexa454
RIKEN OSC as the Japanese sequencing center
Base/day
Data production per day with DNA sequencers
Sequencing cost per informationis drastically decreasing every year.
Use of Next Generation Sequencers on Omics research
Apply to the CAGE (Cap Analysis of Gene Expression) technology
Mammalian Transcriptome Analysis Transcription Regulation Network Promoter analysis
Transcription Factor Protein-protein Interactions
Promoter
mRNAs
Tag sequences
Transcription Promoter
20-27 bp
CAGE: Cap Analysis of Gene Expression
Our original technology. CAGE analyzes 5’-end of the capped transcripts by DNA sequencing.1) Precise transcriptional starting sites (TSSs) are clarified.2) Expression profile of each promoter (not gene) could be analyzed.
DeepCAGE: deep sequencing application of CAGE
AGCTAGCTAGCTAGCTAGCTAGAGCTAGGTAGCTAGCTAGCTAGAGCTAGCTAGCTACCTAGCTAG
Large-scale sequencing
Mapping on Genome
Precise transcriptional starting sites (TSSs)
+ Expression profile of each promoter
+ Sequece-based mapping power
+ small RNA sequencing
Nat. Genet. 2009
Nat. Genet. 2009
Nat. Genet. 2009
Transcription Regulation Network Analysis
Cells are programmed in terms of transcription.
What’s the program in the cell?
Genome
cytoplasm
TF gene RNA TF gene
RNA
TF1TF1
TF gene
RNA
TF gene RNA
TF3
TF4
TF gene
RNA TF0
• The stable transcriptional states are maintained by multiple factors and state transition requires the concerted actions of multiple transcription factors & microRNAs. The concentration of these factors is kept constant in each stable state.
• There are programs that maintain equilibrium state of TFs and ncRNAs in the genome(Core Regulation).
• The resulting attractor basins of the cellular states are analogous to local minima in energy landscapes surrounded by slopes.
• These homeostatic interactions can be thought of as providing a kind of inertia that regulates "peripheral genes“. They determine cellular traits.
TF1TF3
TF4
TF5
TF10
TF2
miRNA1
miRNA2
TF8
TF9TF7
TF6
Core regulation
Gene1
Gene5
Gene2
Gene3
Gene4
TF11
TF12
Gene8 Gene7
Gene6
Gene9
Gene10
TF1TF3
TF4
TF5
TF10
TF2
miRNA1
miRNA2
TF8
TF9TF7
TF6
Core regulation
Gene1
Gene5
Gene2
Gene3
Gene4
TF11
TF12
Gene8 Gene7
Gene6
Gene9
Gene10
Core Regulation
Peripheral genes
ncRNA 2
ncRNA 1
ncRNA 3
E
Attractor Basin
1
Attractor Basin
2
Cell Differentiation is a transition from basin to basin
Attractor Basin 1
Attractor Basin 2
Transition stateE
Time
Our goal
1. Development of a pipeline for systematic analysis of transcriptional regulation
2. Acquisition of new biological insights from the analysis
Timecourse data production
Monoblast Monocyte
PMAstimulation
0 h 1 h 2 h 4 h 6 h 12 h 48 h 96 h72 h
H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009)
m mspmps ARe m mspmps ARe
Motif Activity
m1 m1m1 m2 m3
m1 m4
m1 m5
Reaction efficiency• Number of possible binding sites• Degree of conservation of the motif• Chromatin status
Effective concentration
Number of CAGE tags that mapped on the same site
CAGE tageps
H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009)
PU.1 mRNA expressionSlightly up-regulated
Band shift in Western blot.
Nuclear translocation inImmunofluorescence
These changes are caused by protein
phosphrylation.
Band shift-down was observed by phosphatase treatment.
The drastic PU.1 motif activity change is considered to occur by both mRNA up-regulation and post-translational modification.
Motif Activity vs. mRNA expression profile
0
500
1000
1500
2000
2500
3000
1 2 3 4 5 6
3000
2000
1000
0 0 1 4 12 24 96
PU.1 motif activityStrongly induced during THP-1 cell differentiation
Motif activity: promoter regulation activity of TFs that bind the motif.
Check PU.1 protein level expression
Cell cycleMitosisMicrotubele cytoskele
Inflammatory response
Cell adhesion
Immune response
Transcription regulation network consisting of 30 core motifs
Edge supportGreen: siRNARed: literatureBlue: ChIP
Motif activity
Up
Down
Transient
: enriched GO for regulated genes
Enriched GO: from cell growth related to cell function related
Size of nodes :Significance of motifs
55 out of 86 edges were supported by experiments/in the literature. (Novel prediction works well!!)
Monocyte
Monoblast
H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009)
Promoter analysis
Timecourse data production
Promoter Analysis
Number of promoters per gene Number of genes1 36982 20873 12474 7525 4286 2637 1698 1109 96
10 5211 3212 2713 1314 1315 816 1017 418 819 120 221 122 223 024 225 026 027 028 029 1
Total 9026
H. Suzuki et al. Nature Genetics, 41:5, 553-562 (2009)
24.3M tags: detection level of 1 copy/10 cells at 99.9955%
29,857 active promoters (with novel promoters)
23,403 promoters linked to 9,026 genes
Multiple-promoters in approximately 60% of genes
Expression of retrotransposon elements
Mouse Human
RED: overrepresentedGreen: underrepresented
Tissues Tissues
Satellite
Simple
TE
More than 35% show strong tissue specificity (17% for other promoters).
Tissues Tissues P. Carninci et al.
J. Faulkner et al, Nature Genetics, 41:5, 563-571 (2009)
Transcription Factor Protein-protein Interactions(TF-PPIs)
An atlas of combinatorial transcriptional regulation in mouse and man
Typically, Transcription Factors (TFs) do not act independently, but form complexes with other TFs, chromatin modifiers, and co-factor proteins, which together assemble upon the regulatory regions of DNA to affect transcription.
A clear and immediate challenge is to infer how larger combinations of TFs can act together to generate emergent behaviours that are not evident when each factor is considered in isolation.
TF modulators
Basic TFs
TFs(Activators)
T. Ravasi, et al, Cell, 140, 744-752 (2010)
Human Transcription Factor Interaction Map
Natto (fermented beans: Japanese traditional food)Human TF PPIs
T. Ravasi, et al, Cell, 140, 744-752 (2010)
A TF PPI sub-network critical for cell fate
TF network associated with tissue origin
Development is not only regulated by TF expression level, but also TF-PPI!!
TF PPI sub-network between Human and Mouse
Human Frontal Cortex Mouse Frontal Cortex
A sub-network related to Neural Development
Spatio-temporal Similarity between human and mouse TF PPI network
Negative regulation
時間 (hour)
Exp
ress
ion
le
vel
Time (hour)0 hr 96 hrs
Differentiation
Newly found SMAD3-FLI1 interaction likely negatively regulate the differentiation from monoblast to monocyte.
T. Ravasi, et al, Cell, 140, 744-752 (2010)
Summary & Future Perspective
Power of the Next Generation Sequencers is rapidly changing a way for the Omics Research.
Transcriptome Analysis: deepCAGE Transcriptional Regulation Network Analysis Promoter Analysis TF-PPI analysis
Genome: Now $20,000 per person --- $1,000-2,000 within a couple of years. Soon we will know own genome seq.
Common events (Cell diffrentiation, Development) to Abnormality (diseases)
Large Scale data needs powerful Bioinformatics and collaborations.
Acknowledgement
OSC head quarters Yoshihide Hayashizaki Jun Kawai Piero Carninci Carsten Daub
This work has been achieved in the FANTOM4 consortium with support of the Genome Network Project (MEXT).