Post on 11-Aug-2020
The Encyclopedia of DNA Elements (ENCODE) Project
Elise A. Feingold, Ph.D. National Human Genome Research Institute
National Institutes of Health
AgENCODE Workshop January 10, 2014
How can we “read” the human genome sequence?
• Genetic code, but no genomic code • Evolutionary conservation helps to identify
functionally important regions ~5% conserved/ ~1.5% protein coding
What is function of non-coding conserved sequences? What is function of non-conserved sequences?
• Moderately good at identifying protein-coding regions, but fine structures difficult to predict from sequence
• Regulatory regions can be very far away from genes • Need unbiased experimental investigation
ENCODE: Encyclopedia of DNA Elements
Compile a comprehensive encyclopedia of all sequence features in the human genome and in the genomes of selected model organisms
Approach: Apply lessons learned from the success of the
Human Genome Project Start with well-defined pilot project Develop and test high-throughput technologies
Community Resources Use by research community to enhance understanding of:
– regulation of gene expression on a spatial, temporal and quantitative level
– genetic basis of disease
Rapid pre-publication data release Consortia publications Analysis requires development of:
– Common data reporting formats – Data standards – Analytical tools
ENCODE Timeline
ENCODE Products
“Marker” Papers
<>
PLoS Biol (2011) 9:e1001046
modENCODE Publications
19 companion papers in Nature, Genome Research,
Genome Biology and Database
ENCODE 2 Publications
September 2012
modENCODE and ENCODE 2 Final Efforts
modENCODE
• Cross-species Analyses – Transcription – Chromatin – Regulation
• Transfer of data and analyses to ENCODE 3 DCC
ENCODE “2”
• Mouse ENCODE Cross-species Analyses
• Transfer of data and analyses to ENCODE 3 DCC
ENCODE Data
Modified from PLoS Biol 9-e1001046,2011
ENCODE 2 Data
Human Data >2,800 Datasets • >200 Cell types • >250 RNA-seq • 150 DNase • 1,100 Transcription factor
binding • >200 Histone modification • 90 DNAme • GENCODE mRNA • Functional Characterization
Mouse Data >600 Datasets • 100 Cell types • 100 RNA-seq • 50 DNase • 170 Transcription factor
binding • 170 Histone
modification
Cel
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182
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ENCODE Dimensions
Methods/Factors
164 Assays (114 different Chip)
3,010 Experiments 5 TeraBases
1716x of the Human Genome
Ewan Birney
More than 30 papers in • Nature • Genome Research • Genome Biology • Science • Cell
Publishing innovations
• Threads of themes • Virtual machines • iPad app
ENCODE increased our understanding of non-coding DNA and human disease
ENCODE 2 Publications
From www.nature.com/encode
High-Level Findings • Very large fraction of the genome is biochemically active
– 80% of the genome has an ENCODE annotation in at least one cell type
– Fraction that are functional TBD
• GWAS SNPs are enriched within non-coding functional elements – >50% of non-coding GWAS SNPs are near ENCODE-defined
regions – In many cases, disease phenotypes can be associated with a
specific cell type or transcription factor.
• Segmenting the genome into 7 chromatin states predicts ~400,000 enhancers and ~70,000 promoters as well as 1000s of quiescent states
Non-coding DNA Is Important For Disease And Evolution
• Non-coding DNA variants are known to cause human diseases
• Non-coding variants are known to cause changes in drug metabolism
• About 90% of GWAS findings lie outside of protein-coding regions
• More than 80% of recent adaptation signatures in three recent studies are not associated with protein-coding mutations
Stamatoyannopoulos, Science 337-1190, 2012 Kingsley, Nature 484-55,2012; Sabeti, Cell 152-703,2013; Fraser, Genome Research, doi:10.1101/gr.152710.112,2013
Data Access
Data Access
www.encodeproject.org
UCSC Genome Browser
Ensembl
wwww.modENCODE.org
NCBI
FlyBase
WormBase
ENCODE Portal http://encodeproject.org
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Cumulative ENCODE Publications Over Time
Papers from Non-ENCODE Authors
Papers from ENCODE 2 Production Groups
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Cumulative Publications Using ENCODE Data by Non-ENCODE Authors
Basic Biology
Methods Development
Human Disease
Use of ENCODE Data in Linking Genotype to Phenotype
• ENCODE data can be used in hypothesis generation and refinement – What is the causal variant? – What is the target gene? – What is the target cell type? – How does the variant alter the phenotype?
Social Media Facebook
ENCODE (ENCyclopedia Of DNA Elements)
Twitter @ENCODE_NIH
ENCODE Tutorial Pages
http://www.genome.gov/27553900
ENCODE 3
Catalog is incomplete
• Only a small fraction of transcription factors studied
• Deeper analysis across many additional cell types (more primary cells) needed
• Additional data types need to be studied, e.g., RNA-binding proteins, lncRNAs
ENCODE 3 Solicitation • Comprehensive catalogs of functional elements
• Existing capacity for high-throughput, efficient production
• Centralized production, management & coordination
• 7 high priority scientific areas
• More integrated data coordination and analysis
• Primary focus on human, secondary focus on mouse
• Fly/worm allowed if demonstrate need for: – highly centralized effort for specific data type
– Work to be undertaken as part of highly interactive consortium
Priority areas • Maps of all classes of functional RNA molecules • Fine structural genome annotation (of the human and mouse
genomes only) by improving gene models • Maps of sites of open chromatin • Maps of selected histone marks and other relevant chromatin
proteins • Maps of sites of DNA methylation • Maps of all functional sequence elements within RNA
molecules • Maps of the binding sites for more transcription factors, using
a minimum of two cell types for each previously unstudied factor, and additional, well justified cell types as resources permit – For transcription factors for which binding site maps
already exist, development of maps in additional cell types will be considered, but will be of lower priority and expansion of this data set must be strongly justified
ENCODE 3 Structure
Gene Models
RNA TF Binding
Data Coordination Center
Data Analysis Center Analysis Working Group
Element ID
Chromatin States
Histone Mods DNase DNAme
RBP Binding
Computational Analysis Groups
Technology Development Groups
Data Production Groups
Project Management
Project Management • Monthly teleconference calls
• Working groups to address specific issues
• Data Analysis Working Groups
• Annual meetings
• Project oversight by external advisors
Individual Project Management
• Yearly quantitative milestones • Quarterly progress reports
– Track status of experiments and data submission to identify bottlenecks
– Track costs – Additional narrative section to track non-
quantitative milestones, e.g., technology development and to discuss bottlenecks
Participants • Groups funded by ENCODE solicitations • Open to additional data production or data
analysis groups agreeing to criteria for participation – Genome-wide analysis – Full participation in Consortium activities – Abide by data release policy – Demonstrated funding source
• Encourage inter-consortia collaborations • Encourage other collaborations/coordination
Peak Calling
ChIP/CLIP/RIP-seq
Human Subjects
Operational
ENCODE Consortium Activities
Human Resources
Policies/Logistics
Mouse Resources
Data Release and Publications
Outreach
Functional Characterization and Validation
Data Coordination, Analysis, and Interpretation
Analysis Working Group
Datatype Specific Coordination
DNase RNA
Binding DCC
DAC
EDCAC
Consortium
Production PI
ENCODE Wiki
Nature 489-49,2012
Lessons Learned • Plan data collection
– Develop focused project goals and target end users in advance
– Employ high-throughput, robust methods • Keep production and technology development pipelines separate
– Centralize data collection to the extent possible to maximize economies of scale and consistent data quality
– Generate data on common samples to the extent possible • Consider centralized sample collection/distribution • Very powerful to have multiple data types on same samples
– Develop metadata useful for people outside of project – Develop experimental standards, data quality metrics and
uniform data processing • Especially needed if multiple groups are generating data using same
experimental assays • Ensure high (known) data quality • Perform data quality evaluation on ongoing basis
Lessons Learned
• Devote sufficient resources to bioinformatics (data storage, processing and analysis)
– Don’t assume that organism –specific community will come together on its own for analysis without dedicated support
• Be realistic about data analysis and publication timeline – Overestimate by at least 2X
• Create centralized mode of sharing information – e.g, wiki sites, google docs
Lessons Learned • Need for significant, centralized management
– Explicit, written guidelines, standards and rules • e.g., policies for data release, publications
• Balance needs of individual investigators with those of Consortium – Retain ability to publish independently – Focus on global data production and analysis – Beware of focus on individual research agendas and
“interesting biology” • Foster collegial interactions
– Encourage diversity of opinions – Keep consortium open and bring in needed expertise – Avoid “group think” – Have explicit process for decision making
Summary • Set clear goals, articulate to community • Maximize utility of data to the community
– Rapid pre-publication data release – High (knowable) data quality – Data standards – Interoperability with other projects, especially metadata
• Take advantage of high-throughput production capabilities to maximize economies of scale
• Open consortium • Set and monitor production milestones • Facilitate communication between data production groups and
computational analysis • Devote sufficient resources (data production, analysis and
infrastructure)
AgENCODE Considerations
• Focused goals • Number of species • Quality of genome sequence • Number of individuals per species • Number of phenotypes • Number of tissues/cell types
ENCODE Production Centers
Bradley Bernstein (John Rinn, Manolis Kellis)
Thomas Gingeras (Carrie Davis, Roderic Guigo)
Brenton Graveley (Christopher Burge, Xiang-Dong Fu, Eugene Yeo)
Richard Myers (Devin Absher, Gregory Cooper, Shawn Levy, Florencia Pauli Behn, Ross Hardison, Ali Mortazavi, Timothy Reddy, Barbara Wold)
Bing Ren (Joseph Ecker, Len Pennacchio, Axel Visel, Wei Wang)
Michael Snyder (Kevin White, Sherman Weissman, Peggy Farnham)
John Stamatoyannopoulos (Ralph Hansen, Rajinder Kaul, Patrick Navas, George Stamatoyannopoulos, Piper Treuting, Michael Bender, Job Dekker, Mark Groudine)
ENCODE Data Coordination Center
Mike Cherry (Jim Kent)
ENCODE Data Analysis Center
Zhiping Weng (Mark Gerstein, Manolis Kellis, Roderic Guigo, Rafael Irizarry, Xiaole Shirley Liu, William Stafford Noble)
Additional ENCODE Participants
Timothy Hubbard (Mark Gerstein, Roderic Guigo, Jen Harrow, Rachel Harte, David Haussler, Manolis Kellis, Alexandre Reymond, Stephen Searle, Alfonso Valencia)
David Gilbert (Tamer Kahveci)
ENCODE 3 ENCODE Computational Analysis Groups
Peter Bickel (Haiyan Huang, Leonard Lipovich, Bin Yu)
David Gifford (Tommi Jaakkola)
Sunduz Keles (Emery Bresnick, Colin Dewey)
Robert Klein (Christina Leslie, Souma Raychaudhuri, Ross Levine, Kenneth Offit)
Jonathan Pritchard (Yoav Gilad)
Xinshu Xiao
ENCODE Technology Development Groups
Christopher Burge (Wendy Gilbert, Brenton Graveley, Robert Horvitz)
Barak Cohen and Joseph Corbo
Peggy Farnham (Victor Jin, David Jay Segal)
R. David Hawkins
Christina Leslie (Christopher Mason)
Jason Lieb (Karen Mohlke, Eran Segal)
Mats Ljungman (Thomas Wilson)
Tarjei Mikkelsen
Jay Shendure and Nadav Ahituv (Michael McManus)
Alexey Wolfson
Guo-Cheng Yuan (Stuart Orkin)
… and many senior scientists, postdocs, students, technicians, computer scientists, statisticians and administrators in these groups
Current ENCODE participants: http://www.genome.gov/26525220
The ENCODE 3 Consortium
NHGRI Staff
Program Directors Elise Feingold Peter Good Michael Pazin
Deputy Director Mark Guyer
Division Director Jeff Schloss
Program Analysts Sherry Zhou Preetha Nandi