Practical Guide to the
(mod)ENCODE project
February 27 2013
Fundamental Goals
• Improve comprehensiveness and accuracy of
gene annotation
• Define novel protein coding and noncoding gene
products, including variants
• Define noncoding regulatory elements, including
both sequence and epigenetic features
• Begin to measure the extent of tissue-specific
deployment of functional elements
Rationale for the Consortium
• Synergistic expertise of large groups
• Coordinated sample and data collection
procedures
• Systematic data analysis
• Rapid release of the data to the public
• Common data repository
History and Relationship of ENCODE Projects
U. S. National Human Genome Research Institute
2007-2012
modENCODE
(100% of
genome)
2003-2007
pilot human
ENCODE
(1% of genome)
C. elegans
Waterston/Celniker
(transcribed elements)
Piano/Lai
(3’ UTR elements)
2007-20??
human ENCODE
scale-up
(100% of genome)
Drosophila
Snyder/White
(TF binding sites)
Lieb/Karpen
(chromatin function)
Henikoff
(histone replacement)
Model organism advantages…
• Compact, well-annotated “simpler” genome
• Functional elements can be identified in vivo
• Experimental advantages for both generating
and interpreting genomic data
…and disadvantages
• Not human
• Most studies performed in whole animals
modENCODE
Publications of the “half-way point” in Science Dec 2010:
237 C. elegans datasets and >700 Drosophila datasets
Verified data available at http://www.modencode.org
Defining the transcriptome
Extract total RNA, mRNA, and small RNAs from samples taken at distinct
developmental stages and conditions
L4 male
adult hermaphrodite
early embryo
L4
dauer
L3
late embryo
L1
L2
C. elegans transcriptome features and alternative splicing
increase in
splice
junction
confirmation
fractional
differences in
isoform
composition for
12,875 genes in
pair-wise
comparison
across seven
developmental
stages
M B Gerstein et al. Science 2010;330:1775-1787
stage-specific
isoforms
stage-specific
pseudogene
expression
Drosophila coding and noncoding genes and structures
malespecific
expression
combine RNAseq data with
conserved
structures
novel miRNA
found in
protein coding
exon
Roy et al. Science 2010;330:1787-1797
Tagging (worm) vs endogenous (fly) TF-ChIP
Generate
antibodies to
proteins of
interest
Create GFP-tagged
transcription factor
fosmids by
recombineering
Generate transgenic
lines by microparticle
bombardment
Characterize
sensitivity and
specificity
Characterize
expression and culture
large scale preps
culture large
scale preps
Perform ChIP-seq
define binding sites
and analyze data
10
C. elegans Highly Occupied Target (HOT)
Regions
22TFs -> 304 HOT regions with 15+ TFs
M B Gerstein et al. Science 2010;330:1775-1787
tend to be at the promoters of broadly expressed genes
Discovery and characterization of chromatin states and their
functional enrichments in Drosophila
30 discrete ->
9 continuous
chromatin
states
Roy et al. Science 2010;330:1787-1797
Statistical models predicting TF-binding and gene expression
from chromatin features in C. elegans
an example
color represents
accuracy of
statistical model
in which a
chromatin
feature(s) acts as
a predictor for TF
binding/HOT
regions
Chromatin based
predictions for
expression of
both coding
genes (top) and
miRNAs (bottom)
Spearman
correlation
coefficient of
each chromatin
feature with
expression levels
M B Gerstein et al. Science 2010;330:1775-1787
Predictive models of regulator, region, and gene activity in
Drosophila
predicting
cell type
specific
regulators of
chromatin
activity
Roy et al. Science 2010;330:1787-1797
predicting target
gene expression
from regulator
expression
DREM: Dynamic Regulatory Events Miner
Human (and mouse) ENCODE
PLoS Biol 9:e1001046, 2011
ENCODE methods and organization
PLoS Biol 9:e1001046, 2011
PLoS Biol 9:e1001046, 2011
Selected cell lines
Standardized data collection and
processing
•
•
•
•
•
•
cell growth conditions
antibody characterization
requirements for controls
requirements for replicates
assessment of reproducibility
data submission formats
Caveats
•
•
•
•
assays on unsynchronized cell populations
several of the cell lines are karyotypically unstable
some Tier 3 lines could be of heterogenous composition
mappability in the human genome is variable and
repetitive sequences (~15% of the genome) are not
included currently
• variable confidence regarding assigned function for the
different types of elements
• data types lacking focal enrichment (spread over broad
regions) could have variation across the enriched
domain
Programs utilized for data analysis
PLoS Biol 9:e1001046, 2011
Location of data sources
PLoS Biol 9:e1001046, 2011
Exploring the ENCODE analysis
http://www.nature.com/encode/#/threads
Companion Papers
In the same issue of Nature (6 September 2012):
Landscape of transcription in human cells
Djebali, S., Davis, C.A. et al.
The accessible chromatin landscape of the human genome
Thurman, R.E., Rynes, E., Humbert , R. et al.
An expansive human regulatory lexicon encoded in transcription factor footprints
Neph, S., Vierstra, J., Stergachis, A.B., Reynolds, A.P. et al.
Architecture of the human regulatory network derived from ENCODE data
Gerstein, M.B., Kundaje, A., Hariharan, M., Landt, S.G., Yan, K.K. et al.
The long-range interaction landscape of gene promoters
Sanyal, A., Lajoie, B.R. et al.
In Genome Biology (6 September 2012):
Analysis of variation at transcription factor binding sites in Drosophila and humans
Spivakov, M. et al.
Genome Biol.
Cell type-specific binding patterns reveal that TCF7L2 can be tethered to the genome by association with GATA3
Frietze, S. et al.
Classification of human genomic regions based on experimentally determined binding sites of more than 100
transcription related factors
Yip, K.Y. et al.
Functional analysis of transcription factor binding sites in human promoters
Whitfield, T.W. et al.
Analysis of variation at transcription factor binding sites in Drosophila and humans
Spivakov, M. et al.
Modeling gene expression using chromatin features in various cellular contexts
Dong, X. et al.
The GENCODE pseudogene resource
Pei, B. et al.
Companion Papers
In Genome Research (6 September 2012):
Annotation of functional variation in personal genomes using RegulomeDB.
Boyle, A.P. et al.
ChIP-seq guidelines and practices used by the ENCODE and modENCODE consortia.
Landt, S.G. et al.
Deep sequencing of subcellular RNA fractions shows splicing to be predominantly co-transcriptional in the
human genome but inefficient for lncRNAs
Tilgner, H. et al.
Discovery of hundreds of mirtrons in mouse and human small RNA data
Ladewig, E. et al.
GENCODE: The reference human genome annotation for the ENCODE project
Harrow, J. et al.
Linking disease associations with regulatory information in the human genome.
Schaub, M.A. et al.
Long noncoding RNAs are rarely translated in two human cell lines
Bánfai, B. et al.
Sequence and chromatin determinants of cell-type–specific transcription factor binding.
Arvey, A. et al.
Sequence features and chromatin structure around the genomic regions bound by 119 human transcription
factors
Wang, J. et al
Combining RT-PCR-seq and RNA-seq to catalog all genic elements encoded in the human genome
Howald, C. et al.
Personal and population genomics of human regulatory variation.
Vernot, B. et al.
Predicting cell-type–specific gene expression from regions of open chromatin.
Natarajan, A. et al.
RNA editing in the human ENCODE RNA-seq data
Park, E. et al.
GENCODE
Harrow et al., 2012
• GENCODE is a manual/automated curation of genes
• annotation is verified by RT-PCR and RACE experiments
• v7: 20,687 protein-coding genes with, on average, 6.3 alternatively
spliced transcripts (3.9 different protein-coding transcripts) per locus
Frankish et al., Genome Research 2012
TF mapping by ChIP-seq
across 72 cell lines
data is organized in “Factorbook” www.factorbook.org
Encode Project Consortium, Nature 489: 57-74, 2012
Chromatin accessibility mapping
• 2.89 million unique, non-overlapping DNase I
hypersensitive sites (DHSs) by DNase-seq in 125
cell types
• 4.8 million sites across 25 cell types that displayed
reduced nucleosomal crosslinking by FAIRE,
many of which coincide with DHSs
• DNA methylation by RRBS [average of 1.2 million
CpGs in each of 82 cell lines and tissues (8.6% of
non-repetitive genomic CpGs), including CpGs in
intergenic regions, proximal promoters and
intragenic regions (gene bodies)]
Encode Project Consortium, Nature 489: 57-74, 2012
Histone modification mapping
12 histone modifications and variants in 46 cell types, including a complete
matrix of eight modifications across tier 1 and tier 2.
Modelling transcription levels from histone modification and
transcription-factor-binding patterns
histone
modifications
TFs
Encode Project Consortium, Nature 489: 57-74, 2012
Patterns and asymmetry of chromatin modification at
transcription-factor-binding sites
histone modifications show
asymmetric patterns across TFBS
Encode Project Consortium, Nature 489: 57-74, 2012
Co-association between transcription factors
Encode Project Consortium, Nature 489: 57-74, 2012
Integration of ENCODE data by genome-wide segmentation
Label
CTCF
E
PF
R
TSS
T
WE
Description
CTCF-enriched element
Predicted enhancer
Predicted promoter flanking region
Predicted repressed or low-activity region
Predicted promoter region including TSS
Predicted transcribed region
Predicted weak enhancer or open chromatin cis-regulatory
element
Encode Project Consortium, Nature 489: 57-74, 2012
High-resolution segmentation of ENCODE data by selforganizing maps (SOM)
Encode Project Consortium, Nature 489: 57-74, 2012
Allele-specific ENCODE elements
Chrom HMM segments
single genes
Encode Project Consortium, Nature 489: 57-74, 2012
Examining ENCODE elements on a per individual basis in
the normal and cancer genome
Comparison of genome-wide-association-study-identified
loci with ENCODE data
UCSC broswer
Browser interface
http://encodeproject.org
-> Genome Browser link
both hg18 and hg19
genome versions are
available and worth
viewing –
hg18 has the “Integrated
Regulation Track” on by
default, while hg19 has
newer and more datasets
PLoS Biol 9:e1001046, 2011
UCSC browser visualization of
ENCODE data
novel independent transcript in the first intron of TP53
session includes proteogenomics data in conjunction with ENCODE gene,
transcriptome and regulatory data sets
Roadmap Epigenomics Project
next-generation sequencing technologies to map DNA methylation,
histone modifications, chromatin accessibility and small RNA
transcripts
in stem cells and primary ex vivo tissues selected to represent the
normal counterparts of tissues and organ systems frequently involved
in human disease
rapid release of raw sequence data, profiles of epigenomics features
and higher-level integrated maps to the scientific community
development, standardization and dissemination of protocols,
reagents and analytical tools to enable the research community to
utilize, integrate and expand upon this body of data
Epigenomics Data
www.roadmapepigenomics.org/data
Epigenomics Data
www.roadmapepigenomics.org/data
Databases, data visualization, and
access
modENCODE:
http://www.modencode.org
http://www.intermine.modencode.org
http://www.modencode.org/publications/worm_2010pubs/
http://www.wormbase.org
http://www.flybase.org
ENCODE:
http://www.encodeproject.org
http://www.genome.ucsc.edu/ENCODE/
http://www.genome.ucsc.edu/ENCODE/downloads.html
http://www.factorbook.org
Epigenomics RoadMap: http://nihroadmap.nih.gov/epigenomics
http://ncbi.nlm.nih.gov/epigenomics
http://www.epigenomebrowser.org
http://genomebrowser.wustl.edu/
http://epigenomegateway.wustl.edu/