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New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
New insights into the human genome by encode 14.12.12
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New insights into the human genome by encode 14.12.12

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  • These analyses reveal that the human genome encodes a diversearray of transcripts. For example, in the proto-oncogene TP53locus, RNA-seq data indicate that, while TP53transcripts areaccurately assigned to the minus strand, those for the oppositelytranscribed, adjacent geneWRAP53emanate from the plus strand(Figure 3). An independent transcript within the first intron ofTP53is also observed in both GM12878 and K562 cells (Figure 3).
  • Theupper portion shows the ChIP-seq signal of five sequence-specific transcription factors and RNA Pol2 throughout the 58.5 Mb of the short arm ofhuman chromosome 6 of the human lymphoblastoid cell line GM12878. Input control signal is shown below the RNA Pol2 data. At this level ofresolution, the sites of strongest signal appear as vertical spikes in blue next to the name of each experiment (‘‘BATF,’’ ‘‘EBF,’’ etc.).
  • 116 kb segment of the HLA region is expanded; here, individual sites of occupancy can be seen mappingto specific regions of the three HLA genes shown at the bottom, with asterisks indicating binding sites called by peak calling software. Finally, thelower left region shows a 3,500 bp region around two tandem histone genes, with RNA Pol2 occupancy at both promoters and two of the fivetranscription factors, BATF and cFos, occupying sites nearby.
  • They organized all the information associated with each transcription factor including the ChIP-seq peaks, discovered motifs and associated histone modification patterns in FactorBook (http://www.factorbook.org), a public resource that will be updated as the project proceeds.
  • After curation and review at the Data Coordination Center, all processed ENCODE data are publicly released to the UCSC Genome Browser database (http://genome.ucsc.edu).
  • Three differenttypes of regulatory data are represented for an area of the genome: motif-based predictions, DNase I hypersensitivity peaks, and ChIP-seq peaks. Thisregion contains six SNPs. SNP1 is associated with a phenotype in a genome-wide association study. SNP3 is an eQTL associated with changes in geneexpression in a different study. SNP6 overlaps a predicted motif, a DNase Ihypersensitivity peak, and a ChIP-seq peak. There are, therefore, multiplesources of evidence that SNP6 is in a regulatory region. Furthermore,SNP6 is in perfect linkage disequilibrium (r2=1.0) with SNP1 and SNP3,meaning that there is transitive evidence due to the LD that SNP6 is alsoassociated with the phenotype and is also an eQTL. SNP6 is therefore themost likely functional SNP in this associated region.
  • Aggregate overlap of phenotypes to selected transcription-factor-binding sites (left matrix) or DHSsin selected cell lines (right matrix), with a count of overlaps between thephenotype and the cell line/factor. Values in blue squares pass an empiricalP-value threshold#0.01 (based on the same analysis of overlaps betweenrandomly chosen, GWAS-matched SNPs and these epigenetic features) andhave at least a count of three overlaps. ThePvalue for the total number ofphenotype–transcription factor associations is,0.001
  • several SNPsassociatedwithCrohn’s disease andotherinflammatorydiseases that reside inalarge gene desert on chromosome 5, along with some epigenetic featuresindicative of function. The SNP (rs11742570) strongly associated to Crohn’sdisease overlaps a GATA2 transcription-factor-binding signal determined inHUVECs. This region is also DNase I hypersensitive inHUVECsandT-helperTH1 andTH2 cells. An interactive version of this figure is available in the onlineversion of the paper
  • Users are able to interface with our database by entering lists of SNVs or regions to identify common SNVs at http://www.RegulomeDB.org/ (a). They are then presented with a sorted list of the most important SNVs (b). These SNVs can be examined for the evidence used to rank them as well as a citation for the evidence.
  • Scientists in the Encyclopedia of DNA Elements Consortium have applied 24 experiment types (across) to more than 150 cell lines (down) to assign functions to as many DNA regions as possible — but the project is still far from complete
  • Transcript

    1. New insights into the human genome by ENCODE
    2. What is a gene??? ENCODE • Union of genomic sequences encoding a coherent set of potentially overlapping functional products. (Gerstein et al., 2007)
    3. Its been ten years since scientists sequenced the human genome But What do all these letters????????
    4. 21,000 genes
    5. ENCODE- the Encyclopedia of DNA Elements has ANSWERS Aiming to delineate all of the functional elements encoded in the human genome sequence
    6. ENCODE Consortium (The ENCODE Project Consortium, 2011)
    7. Pilot Phase •2003-2007 Technology development phase •2007-2012 •30 papers Production phase
    8. ENCODE Major methods Data production and initial analysis Accessing ENCODE data Working with ENCODE data Data analysis Limitations Threads – Nature explorer
    9. Major Methods (The ENCODE Project Consortium, 2004)
    10. Overall data flow (The ENCODE Project Consortium, 2011)
    11. (The ENCODE Project Consortium, 2011)
    12. RNA-seq – Isolation of RNA sequences followed by high-throughput sequencing CAGE – Capture of the methylated cap at the 5’end of RNA, followed by high-throughput sequencing RNA-PET – Simultaneous capture of RNAs with both a 5’methyl cap and a poly(A) tail ChIP-seq - Chromatin immunoprecipitation followed by sequencing FAIRE-seq - Formaldehyde assisted isolation of regulatory elements. Crosslinking, phenol extraction, and sequencing the DNA fragments in the aqueous phase
    13. (The ENCODE Project Consortium, 2011)
    14. ENCODE cell types (The ENCODE Project Consortium, 2011)
    15. ENCODE data production and initial analyses • Since 2007, ENCODE has developed methods and performed a large number of sequence-based studies to map functional elements across the human genome. • The elements mapped (and approaches used) include  RNA transcribed regions (RNA-seq, CAGE, RNA-PET and manual annotation),  Protein-coding regions (mass spectrometry),  Transcription-factor-binding sites (ChIP-seq and DNase-seq),  Chromatin structure (DNase-seq, FAIRE-seq, histone ChIP-seq),  DNA methylation sites (RRBS assay) (The ENCODE Project Consortium, 2012)
    16. Transcribed and protein-coding regions • In total, GENCODE-annotated exons of protein-coding genes cover 2.94% of the genome or 1.22% for protein-coding exons. • Protein-coding genes span 33.45% from the outermost start to stop codons, or 39.54% from promoter to poly(A) site. • Additional protein-coding genes remain to be found. • In addition, they annotated 8,801 automatically derived small RNAs and 9,640 manually curated long non-coding RNA (lncRNA) loci • The GENCODE annotated 11,224 pseudogenes (The ENCODE Project Consortium, 2012)
    17. Process flow of experimental evaluation of pseudogene transcription Experimental validation results showing the transcription of pseudogenes in different tissues (Pei et al., 2012)
    18. ENCODE gene and transcript annotations. (The ENCODE Project Consortium, 2011)
    19. RNA • They sequenced RNA from different cell lines and multiple subcellular fractions to develop an extensive RNA expression catalogue. • They used CAGE-seq (5’cap-targeted RNA isolation and sequencing) to identify 62,403 (TSSs) in tier 1 and2 cell types (The ENCODE Project Consortium, 2012)
    20. A large majority of GENCODE elements are detected by RNA-seq data (Djebali et al., 2012)
    21. Protein bound regions • 119 different DNA-binding proteins and a number of RNA polymerase components in 72 cell types using ChIP-seq • Overall, 636,336 binding regions covering 231 mega bases (8.1%) of the genome are enriched for regions bound by DNA- binding proteins across all cell types. (The ENCODE Project Consortium, 2012)
    22. Occupancy of transcription factors and RNA polymerase 2 on human chromosome 6p as determined by ChIP-seq
    23. (The ENCODE Project Consortium, 2011)
    24. DNase I hypersensitive sites and footprinting • Chromatin accessibility characterized by DNase I hypersensitivity is the hallmark of regulatory DNA regions. • 2.89 million unique, non-overlapping (DHSs) by DNase-seq in 125 cell types – lie distal to TSSs • In tier 1 and tier 2 cell types - 205,109 DHSs per cell type, encompassing an average of 1.0% of the genomic sequence in each cell type, and 3.9% in aggregate. (The ENCODE Project Consortium, 2012)
    25. Density of DNase I cleavage sites for selected cell types (Thurman et al., 2012)
    26. • On average, 98.5% of the occupancy sites of transcription factors mapped by ENCODE ChIP-seq • Using genomic DNase I footprinting on 41 cell types they identified 8.4million distinct DNase I footprints (The ENCODE Project Consortium, 2012)
    27. Regions of histone modification • They assayed chromosomal locations for up to 12 histone modifications and variants in 46 cell types, across tier 1 and 2. (The ENCODE Project Consortium, 2012)(http://www.factorbook.org)
    28. DNA methylation • They used reduced representation bisulphite sequencing (RRBS) to profile DNA methylation quantitatively for an 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. (The ENCODE Project Consortium, 2012)
    29. Proteomics  To assess putative protein products generated from novel RNA transcripts and isoforms, proteins are sequenced and quantified by mass spectrometry and mapped back to their encoding transcripts.  K562 and GM12878 – protein study begun (The ENCODE Project Consortium, 2011)
    30. ENCODE chromatin annotations in the HLA locus (The ENCODE Project Consortium, 2011)
    31. Accessing ENCODE Data ENCODE Data Release and Use Policy • The ENCODE Data Release and Use Policy is described at http://www.encodeproject.org/ENCODE/terms.html. • ENCODE data are released for viewing in a publicly accessible browser (initially at http://genome-preview.ucsc.edu/ENCODE and, after additional quality checks, at http://encodeproject.org) Public Repositories • UCSC Genome Browser database (http://genome.ucsc.edu). (The ENCODE Project Consortium, 2011)
    32. UCSC Portal
    33. Working with ENCODE Data Using ENCODE Data in the UCSC Browser • Many users will want to view and interpret the ENCODE data for particular genes of interest. At the online ENCODE portal (http://encodeproject.org), users should follow a ‘‘Genome Browser’’ link to visualize the data in the context of other genome annotations. (The ENCODE Project Consortium, 2011)
    34. ENCODE Data Analysis • Development and implementation of algorithms and pipelines for processing and analyzing data - major activity of the ENCODE Project. •Short sequences are aligned to the reference genome 1st Phase •Identifying the enriched regions 2nd Phase •Integrating the identified regions of enriched signal with each other and with other data types 3rd Phase (The ENCODE Project Consortium, 2011)
    35. Analysis tools applied by the ENCODE consortium (The ENCODE Project Consortium, 2011)
    36. Integrating ENCODE with other projects and the Scientific Community 1. defining promoter and enhancer regions by combining transcript mapping and biochemical marks, 2. delineating distinct classes of regions within the genomic landscape by their specific combinations of biochemical and functional characteristics, and 3. defining transcription factor co-associations and regulatory networks. (The ENCODE Project Consortium, 2011)
    37. • ENCODE Project - interpretation of human genome variation that is associated with disease or quantitative phenotypes • Integrate with 1,000 Genomes Project - how SNPs and structural variation may affect transcript, regulatory and DNA methylation data • ENCODE - GWAS and other sequence variation driven studies of human phenotypes Major contributor not only of data but also novel technologies for deciphering the human genome (The ENCODE Project Consortium, 2011)
    38. Limitations of ENCODE Annotations • Cell types - physiologically and genetically inhomogeneous. • Local micro-environments in culture may also vary • Use of DNA sequencing to annotate functional genomic features is also constrained. • Considerable quantitative variation in the signal strength along the genome (The ENCODE Project Consortium, 2011)
    39. Challenges • Adult human body contains several hundred distinct cell types • Each of which expresses a unique subset of the 1,800 TFs encoded in the human genome • Brain alone contains thousands of types of neurons that are likely to express not only different sets of TFs but also a larger variety of non-coding RNAs • A truly comprehensive atlas of human functional elements is not practical with current technologies (The ENCODE Project Consortium, 2011)
    40. Outcome • Understanding of the human genome • The broad coverage of ENCODE annotations enhances our understanding of common diseases with a genetic component, rare genetic diseases • 119 of 1,800 known transcription factors and 13 of more than 60 currently known histone or DNA modifications across 147 cell types • Overall these data reflect a minor fraction of the potential functional information encoded in the human genome (The ENCODE Project Consortium, 2012)
    41. http://www.nature.com/encode/#/threads
    42. 13 Threads 1. Transcription factor motifs 2. Chromatin patterns at transcription factor binding sites 3. Characterization of intergenic regions and gene definition 4. RNA and chromatin modification patterns around promoters 5. Epigenetic regulation of RNA processing 6. Non-coding RNA characterization 7. DNA methylation 8. Enhancer discovery and characterization 9. Three-dimensional connections across the genome 10. Characterization of network topology 11. Machine learning approaches to genomics 12. Impact of functional information on understanding variation 13. Impact of evolutionary selection on functional regions
    43. Schematic overview of the functional SNP approach (Schaub et al., 2012)
    44. Comparison of GWAS identified loci with ENCODE data
    45. (Boyle et al., 2012)
    46. Future goal • Mechanistic processes that generate these elements and how and where they function • Enlarge the data set to additional factors, modifications and cell types, complementing the other related projects • Constitute foundational resources for human genomics, allowing a deeper interpretation of the organization of gene and regulatory information and the mechanisms of regulation, and thereby provide important insights into human health and disease (The ENCODE Project Consortium, 2012)
    47. Project is still far from complete Conclusion For update: https://www.facebook.com/ENCODEProject
    48. Encode – assign word to letter
    49. Thank you:) Presented by: R. Veera Ranjani

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