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

U. S. National Human Genome Research Institute

History and Relationship of ENCODE Projects

pilot human ENCODE

(1% of genome)

modENCODE(100% of genome)

C. elegans Drosophila

human ENCODE scale-up

(100% of genome)

Henikoff(histone replacement)

Waterston/Celniker(transcribed elements)

Piano/Lai(3’ UTR elements)

Snyder/White(TF binding sites)

Lieb/Karpen(chromatin function)

2003-2007 2007-2012 2007-20??

Model organism advantages…

• Compact, well-annotated “simpler” genome• Functional elements can be identified in vivo• Experimental advantages for both

generating and interpreting genomic data

• Not human• Most studies performed in whole animals

…and disadvantages

Publications of the “half-way point” in Science Dec 2010: 237 C. elegans datasets and >700 Drosophila datasetsVerified data available at http://www.modencode.org

modENCODE

Defining the transcriptome

early embryo

L1L2

L3

L4

adult hermaphrodite

late embryo

L4 male

dauer

Extract total RNA, mRNA, and small RNAs from samples taken at distinct developmental stages and conditions

C. elegans transcriptome features and alternative splicing

M B Gerstein et al. Science 2010;330:1775-1787

stage-specific isoforms

fractional differences in isoform composition for 12,875 genes in pair-wise comparison across seven developmental stages

stage-specificpseudogeneexpression

increase insplice junctionconfirmation

Drosophila coding and noncoding genes and structures

Roy et al. Science 2010;330:1787-1797

combine RNA-seq data with conserved structures

novel miRNA found in protein coding exon

male-specific expression

10

Tagging (worm) vs endogenous (fly) TF-ChIP

Create GFP-tagged transcription factor fosmids by recombineering

Generate transgenic lines by microparticle bombardment

Characterize expression and culture large scale preps

Perform ChIP-seqdefine binding sites and analyze data

Generate antibodies to proteins of interest

Characterize sensitivity and specificity

culture large scale preps

C. elegans Highly Occupied Target (HOT) Regions

M B Gerstein et al. Science 2010;330:1775-1787

22TFs -> 304 HOT regions with 15+ TFs

tend to be at the promoters of broadly expressed genes

Discovery and characterization of chromatin states and their functional enrichments in Drosophila

Roy et al. Science 2010;330:1787-1797

30 discrete ->9 continuouschromatinstates

Statistical models predicting TF-binding and gene expression from chromatin features in C. elegans

M B Gerstein et al. Science 2010;330:1775-1787

color represents accuracy of statistical model in which a chromatin feature(s) acts as a predictor for TF binding/HOT regions

an example

Spearman correlation coefficient of each chromatin feature with expression levels

Chromatin based predictions for expression of both coding genes (top) and miRNAs (bottom)

Predictive models of regulator, region, and gene activity in Drosophila

Roy et al. Science 2010;330:1787-1797 DREM: Dynamic Regulatory Events Miner

predicting target gene expression from regulator expression

predicting cell type specific regulators of chromatin activity

Human (and mouse) ENCODE

PLoS Biol 9:e1001046, 2011

ENCODE methods and organization

PLoS Biol 9:e1001046, 2011

Selected cell lines

PLoS Biol 9:e1001046, 2011

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 PapersIn 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 PapersIn 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 alCombining 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

• 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

Harrow et al., 2012

Frankish et al., Genome Research 2012

TF mapping by ChIP-seq

across 72 cell lines

data is organized in “Factorbook” www.factorbook.orgEncode 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

histonemodifications

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

Encode Project Consortium, Nature 489: 57-74, 2012

Label DescriptionCTCF CTCF-enriched element

E Predicted enhancerPF Predicted promoter flanking regionR Predicted repressed or low-activity region

TSS Predicted promoter region including TSST Predicted transcribed region

WE Predicted weak enhancer or open chromatin cis-regulatory element

High-resolution segmentation of ENCODE data by self-organizing maps (SOM)

Encode Project Consortium, Nature 489: 57-74, 2012

Allele-specific ENCODE elements

Encode Project Consortium, Nature 489: 57-74, 2012

single genes

Chrom HMM segments

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

PLoS Biol 9:e1001046, 2011

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

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.orghttp://www.intermine.modencode.orghttp://www.modencode.org/publications/worm_2010pubs/http://www.wormbase.orghttp://www.flybase.org

ENCODE: http://www.encodeproject.orghttp://www.genome.ucsc.edu/ENCODE/http://www.genome.ucsc.edu/ENCODE/downloads.html http://www.factorbook.org

Epigenomics RoadMap: http://nihroadmap.nih.gov/epigenomicshttp://ncbi.nlm.nih.gov/epigenomicshttp://www.epigenomebrowser.orghttp://genomebrowser.wustl.edu/http://epigenomegateway.wustl.edu/