Practical Guide to the (mod)ENCODE project

1. Practical Guide to the (mod)ENCODE project February 27 2013

2. 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

3. 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

4. History and Relationship of ENCODE Projects U. S. National Human Genome Research Institute 2007-2012 2003-2007 2007-20?? modENCODE (100% of genome) pilot human ENCODE (1% of genome) human ENCODE scale-up (100% of genome) C. elegans Drosophila Waterston/Celniker (transcribed elements) Piano/Lai (3’ UTR elements) Snyder/White (TF binding sites) Lieb/Karpen (chromatin function) Henikoff (histone replacement)

5. 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

6. 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

7. L4 male adult hermaphrodite early embryo L4 dauer L3 late embryo L1 L2 Defining the transcriptome Extract total RNA, mRNA, and small RNAs from samples taken at distinct developmental stages and conditions

8. C. eleganstranscriptomefeatures and alternative splicing increase in splice junction confirmation stage-specific isoforms stage-specific pseudogene expression 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

9. Drosophila coding and noncoding genes and structures male-specific expression combine RNA-seq data with conserved structures novel miRNA found in protein coding exon Roy et al. Science 2010;330:1787-1797

10. 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

11. 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

12. 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

13. 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

14. Predictive models of regulator, region, and gene activity in Drosophila predicting target gene expression from regulator expression predicting cell type specific regulators of chromatin activity DREM: Dynamic Regulatory Events Miner Roy et al. Science 2010;330:1787-1797

15. Human (and mouse) ENCODE PLoSBiol 9:e1001046, 2011

16. ENCODE methods and organization PLoSBiol 9:e1001046, 2011

17. Selected cell lines PLoSBiol 9:e1001046, 2011

18. Standardized data collection and processing • cell growth conditions • antibody characterization • requirements for controls • requirements for replicates • assessment of reproducibility • data submission formats

19. 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

20. Programs utilized for data analysis PLoSBiol 9:e1001046, 2011

21. Location of data sources PLoSBiol 9:e1001046, 2011

22. Exploring the ENCODE analysis http://www.nature.com/encode/#/threads

23. 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.

24. Companion Papers In Genome Research (6 September 2012): Annotation of functional variation in personal genomes using RegulomeDB. Boyle, A.P. et al. ChIP-seqguidelines 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.

25. 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

26. 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

27. 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 nucleosomalcrosslinking 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

28. 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.

29. Modelling transcription levels from histone modification and transcription-factor-binding patterns histone modifications TFs Encode Project Consortium, Nature 489: 57-74, 2012

30. 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

31. Co-association between transcription factors Encode Project Consortium, Nature 489: 57-74, 2012

32. Integration of ENCODE data by genome-wide segmentation Encode Project Consortium, Nature 489: 57-74, 2012

33. High-resolution segmentation of ENCODE data by self-organizing maps (SOM) Encode Project Consortium, Nature 489: 57-74, 2012

34. Allele-specific ENCODE elements Chrom HMM segments single genes Encode Project Consortium, Nature 489: 57-74, 2012

35. Examining ENCODE elements on a per individual basis in the normal and cancer genome

36. Comparison of genome-wide-association-study-identified loci with ENCODE data

37. UCSC broswer

38. 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 PLoSBiol 9:e1001046, 2011

39. 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

40. 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

41. Epigenomics Data www.roadmapepigenomics.org/data

42. Epigenomics Data www.roadmapepigenomics.org/data

43. 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 EpigenomicsRoadMap: http://nihroadmap.nih.gov/epigenomics http://ncbi.nlm.nih.gov/epigenomics http://www.epigenomebrowser.org http://genomebrowser.wustl.edu/ http://epigenomegateway.wustl.edu/