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Of the Human Genome

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Of the Human Genome GGTGCCAGGGAAAGGGCAGGAGGTGAGTGCTGGGAGGCAGCTGAGGTCAACTTCTTTTGAACTTCCACGTGGTATTTACTCAGAGCAATTGGTGCCAGAG GCTCAGGGCCCTGGAGTATAAAGCAGAATGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCGAAAGACCTGTTGGAGGCTATGAATGC AATCAAGGTGACAGACAACTGGTGCAATGATGGTAGTGGAAATGGAGGAGAGGGGATTGATTCAAGATGCATTTAGGACCAAGAATCGGGAGCTTGTGAA CGTGTGTATGAGTACTGTAGACGGAGTGGGTGTGTCATCAGAGAAGATCTGAGCATTTGGGCTTGCTCTCCTCAGAGGCCCTGCGAGTGGAGTTCAGCTT TTCCTCATGGGGCAAATCTCACTTTCGCTCCAGTTCCTGGGGCTCAGAGTCCCTGGCCCAGATGCCTCTTGCCATCTCATCTTCACCCTGCCTGGCTTCC CTTGCTTGTTCCUltraconservationUltraconservationAGGATTGTTTCATAAAGAGGGATGTGGTTGGTCTTTAACCCTATGAATGC&&TGGC FunctionFunctionTGAGGATGCCTGCGGAACCTGTAGTGAAGCTTTC AGGGGCTGCTCGGGTTCTGGCTGGTAGGTGAACACTGTCCATCTTGCCGGCTGGGACACAGTGACTCTGGGTAGTTGTGTAAGAGAGGGGCCCTTGGCAG ACAAACAGGTTCTTCTCTGTTGGTGGGCCAGCCAGCAGGTCAGTGGGAAGGTTAAAGGTCATGGGGTTTGGGAGAACTGGGTGAGGAGTTCAGCCCCATC CCCCGTAAAGCTCCTGGGAAGinCACTT CTCTtheACTGGG GCAHumanGCCCCTGATACCAGGGCA GenomeCTCATTAACCCTCTGGGTGCCAGGGAAAGGGCAGGAGGTGAGT GCTGGGAGGCAGCTGAGGTCAACTTCTTTTGAACTTCCACGTGGTATTTACTCAGAGCAATTGGTGCCAGAGGCTCAGGGCCCTGGAGTATAAAGCAGAA TGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCT CTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAG GAAAGACCTGTTGGAGGCTATGAATGCAATCAAGGTGACAGACAACTGGTGCAATGATGGTAGTGGAAATGGAGGAGAGGGGATTGATTCAAGATGCATT TAGGACCAAGAATCGGGAGCTTGTGAACGTGTGTATGAGTACTGTAGACGGAGTGGGTGTGTCATCAGAGAAGATCTGAGCATTTGGGCTTGCTCTCCTC AGAGGCCCTGCGAGTGGAGTTCAGCTTTTCCTCATGGGGCAAATCTCACTTTCGCTCCAGTTCCTGGGGCTCAGAGTCCCTGGCCCAGATGCCTCTTGCC ATCTCATCTTCACCCTGCCTGGCTTCCCTTGCTTGTTCCAGGATTGTTTCATAAAGAGGGATGTGGTTGGTCTTTAACCCTATGAATGCTGGCTGAGGAT GCCTGCGGAACCTGTAGTGAAGCTTTCAGGGGCTGCTCGGGTTCTGGCTGGTAGGTGAACACTGTCCATCTTGCCGGCTGGGACACAGTGACTCTGGGTA GTTGTGTAAGAGAGGGGCCCTTGGCAGACAAACAGGTTCTTCTCTGTTGGTGGGCCAGCCAGCAGGTCAGTGGGAAGGTTAAAGGTCATGGGGTTTGGGA GAAACTGGGTGAGGAGTTCAGCCCCATCCCCCGTAAAGCTCCTGGGAAGCACTTCTCTACTGGGGCAGCCCCTGATACCAGGGCACTCATTAACCCTCTG GGTGCCAGGGAAAGGGCAGGAGGTGAGTGCTGGGAGGCAGCTGAGGTCAACTTCTTTTGAACTTCCACGTGGTATTTACTCAGAGCAATTGGTGCCAGAG GCTCAGGGCCCTGGAGTATAAAGCAGAATGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCGAAAGACCTGTTGGAGGCTATGAATGC AATCAAGGTGACAGACAACTGGTGCAATGATGGTAGTGGAAATGGAGGAGAGGGGATTGATTCAAGATGCATTTAGGACCAAGAATCGGGAGCTTGTGAA CGTGTGTATGAGTACTGTAGACGGAGTGGGTGTGTCATCAGAGAAGATCTGAGCATTTGGGCTTGCTCTCCTCAGAGGCCCTGCGAGTGGAGTTCAGCTT TTCCTCATGGGGCAAATCTCACTTTCGCTCCAGTTCCTGGGGCTCAGAGTCCCTGGCCCAGATGCCTCTTGCCATCTCATCTTCACCCTGCCTGGCTTCC CTTGCTTGTTCCAGGATTGTTTCATAAAGAGGGATGTGGGillTTGGT CBejeranoTTTAACCCTATGAATGCTGGCTGAGGATGCCTGCGGAACCTGTAGTGAAGCTTTC AGGGGCTGCTCGGGTTCTGGCTGGTAGGTGAACACTGTCCATCTTGCCGGCTGGGACACAGTGACTCTGGGTAGTTGTGTAAGAGAGGGGCCCTTGGCAG ACAAACAGGTTCTTCTCSchoolTGTTGGTGGGCCA ofGCCAG Engineering,CAGGTCAGTGGGAAGGTTAAAG GTUCCATG GGGSantaTTTGGGAGA CruzACTGGGTGAGGAGTTCAGCCCCATC CCCCGTAAAGCTCCTGGGAAGCACTTCTCTACTGGGGCAGCCCCTGATACCAGGGCACTCATTAACCCTCTGGGTGCCAGGGAAAGGGCAGGAGGTGAGT GCTGGGAGGCAGCTGAGGTCAACTTCTTTTGAACTTCCACGTGGTATTTACTCAGAGCAATTGGTGCCAGAGGCTCAGGGCCCTGGAGTATAAAGCAGAA TGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCT CTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAG Gill Bejerano, UCSC w: www.soe.ucsc.edu/~jill 1 Functional Genomics Objective: Understanding the function and evolution of every base in the human genome. Gill Bejerano, UCSC 2 Talk Outline • The Annotated Human Genome • The Comparative Genomics Paradigm • Families and Functions of Conserved ncDNA • Ultraconservation in Mammals, Vertebrates, and Beyond • Summary Gill Bejerano, UCSC 3 Dude, Where’s My Complexity? Organism complexity does not correlate with neither • Genome size • Number of genes E.g., genome genes Human 3Gb 25,000 1 mm 959 cells C.elegans 100Mb 20,000 What should explain it then? • Alternative splicing • Gene expression control 1 2 3 • Chromatin remodeling • RNA regulatory networks • Transposable elements Gill Bejerano, UCSC 4 The Annotated Human Genome coding known exons function ~2.9Gb 1.5% 0.5% other repeats 48% 50% functional prediction tools good protein coding genes, repeats* improving alt-splicing, TF binding sites , ncRNA, RNA editing infancy enhancers, silencers, insulators, S/MARs Gill Bejerano, UCSC 5 Comparative Genomics Functional DNA often evolves slower than neutral DNA. To detect functional elements: align genomes of related species, and find regions of high conservation. E.g., http://genome.ucsc.edu Gill Bejerano, UCSC 6 ≥5% of H.G. Appears Under Selection! compare to mouse - 40% DNA alignable 95% coding genes shared all human-mouse DNA coding known neutral human-mouse DNA H.G. exons function 2.9Gb 1.5% 0.5% other repeats 48% 50% frequency Difference: 5% of Human better get to work... conservation level [Mouse Consortium 2002] Gill Bejerano, UCSC 7 Functional Annotation by Paralogy Removed from H.G. top 5% all annotated regions, and then some: GeRUF = Genomic Region of Unknown Function 700,000 3.5% H.G. Group them into families of human paralogs. • Annotated members induce function on all. • Examine functional core of family. •Test for “guilt by association” [Dermitzakis et al., Nat. Rev. Gen. 2005] Gill Bejerano, UCSC 8 Functional Annotation by Paralogy Removed from H.G. top 5% all annotated regions, and then some: GeRUF = Genomic Region of Unknown Function 700,000 3.5% H.G. Group them into families of human paralogs. • Annotated members induce function on all. • Examine functional core of family. •Test for “guilt by association” Bad News: Good News: 96% of 700,000 GeRUFs We still find have no paralogs. 12,027 families! [Bejerano et al., ISMB 2004] Gill Bejerano, UCSC 9 GeRUF Clustering 1) Define the set of intervals of interest. 2) Define a similarity measure between any pair of intervals. 3) Build a weighted similarity graph, where Vertices = GeRUFs, and Edge weights = pairwise similarity. 4) Find biologically motivated dense subgraphs. 5) Analyze and annotate clusters. (Refine and reiterate…) Human.chr1 GCACC...AGCGC w Human.chrX ATAGA...GCACA Gill Bejerano, UCSC 10 Novel ncRNA Using Sean Eddy’s track of known human RNA genes we find: • 47 clusters containing only known RNA genes • 30 clusters containing some known RNA genes • 50 clusters with (mfold) conserved secondary structure in many/all members, but no annotation, e.g.: Gill Bejerano, UCSC 11 Jumping Alt Spliced Exon uc.338 Element overlaps exon in: • PCBP2: poly C binding protein 2 • SMARCA4: member,SWI/SNF family • ATF2: activating T.F. 2 • Other proteins of diverse functions It also occurs in an intron or upstream of other unrelated genes. Gill Bejerano, UCSC 12 Iroquois (IRX) Gene Clusters early IRX-1 IRX-2 IRX-3 IRX-4 IRX-5 IRX-6 body patterning Ancient duplications – Drosophila has one such clusters Chicken has both Hs.chr16 IRX-3 650 kb IRX-5 390 kb IRX-6 35 GeRUFs 70-80%id IRX genes bracket 4 gene deserts* IRX-1 IRX-2 IRX-4 840 kb 890 kb 45 GeRUFs Hs.chr5 Gill Bejerano, UCSC 13 IRX Co-regulation / Insulation? early IRX-1 IRX-2 IRX-3 IRX-4 IRX-5 IRX-6 body patterning Hs.chr16 IRX-3 IRX-5 IRX-6 70-80%id IRX-1 IRX-2 IRX-4 Hs.chr5 Gill Bejerano, UCSC 14 No known function requires this much conservation! CDS ncRNA TFBS contamination?! Gill Bejerano, UCSC 15 Contamination An issue in initial genome drafts (sample switching, vector, ...) We employed ample defenses: • Human and Mouse are high quality drafts. • The addition of the Rat genome. • Syntenic nets. Gill Bejerano, UCSC 16 Ultra Conserved (UC) Elements Any* contiguous block of human-mouse-rat alignment that is identical in all three species, syntenic and ≥200 bp. * annotated or not (p=10-22 of finding one such element in slowest rate 2.9G neutral DNA) Turns out there are 481(!) such blocks of sizes 200-779bp (total of 126Kb) in all chromosomes but 21, Y. Possibly Intronic Transcribed 100 114 Non Exonic 256 Intergenic Exonic* 156 111 *68 (61%) associated with alt-spliced exons Gill Bejerano, UCSC 17 Ultra Clusters •exonic •non •possibly By joining two ultras into a cluster when separated <675Kb we obtained 89 clusters (each named after prominent gene/s) Non exonic elements tend to congregate in clusters. Exonic elements are distributed more randomly (tend to overlap an alt-spliced exon). Gill Bejerano, UCSC 18 Genomic Distribution •exonic •non •possibly Gill Bejerano, UCSC 19 Functional Annotation of Related Genes ultra related genes RNA binding p = 1.3 x 10-19 observed RNA splicing p = 4.8 x 10-10 expected RNA rec. motif p = 5.6 x 10-18 86 Transcription reg. p = 1.2 x 10-4 DNA binding -6 Exonic p = 9.1 x 10 Homeobox p = 4.4 x 10-6 7 RNA binding p = 0.39 RNA splicing p = 0.44 218 RNA rec. motif p = 0.77 -20 Transcription reg. p = 2.5 x 10 -15 DNA binding p = 1.8 x 10 -15 Homeobox p = 7.8 x 10 Non Exonic 0 10203040506070 No. of Genes Exonic – RNA processing @ transcription regulation Non Exonic – regulation of transcription at DNA level Gill Bejerano, UCSC 20 Subs. rate 20-fold reduced for >300Mya Independent species comparison species matching identical (%Hs covered) elements bases Chicken (4%) 467 (97%) 95.7% (29 perfectly) Fugu (1.8%) 324 (67%) 76.8% Single base differences (excl. 2x20bp) observed expected under rep. Validated SNPs 6 / 106,767 119 20x (p=10-42) Hi Qual chimp diffs 38 / 106,767 716 19x (p=10-200) Gill Bejerano, UCSC 21 Non Exonic Enhancers The non exonic ultras are often found in “gene deserts” (140 / 256 >10Kb from a known gene; 88 > 100Kb away). The genes flanking these ultras are GO enriched for development (p = 10-6), particularly early developmental tasks (p
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