The Genom of Homo sapiens.pdf

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Ensembl: A Genome InfrastructureE. BIRNEY AND THE ENSEMBL TEAMEBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD; and Sanger Institute,Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, United KingdomThe genome sequence of any organism is an invaluableresource for molecular biologists. Experiments are eithertrivial to design or greatly enhanced by the knowledge ofthe genome sequence; linkage analysis leads directly to aset of genes in the critical region, and association studiescan be designed to any region (including, in theory, theentire genome). At least as important as the ease of experimentaldesign is the fact that the genome is essentiallycomplete, and therefore, researchers can be confidentthat the aspects of biology they are studying must bepresent in the genome sequence in some manner. The factthat all biology is somehow associated with some aspectof the genome, that the genome is complete and essentiallyunchanging, means that it is a foundational resourcefor biology. The generation of the human (Landfer et al.2001) and mouse (Waterston et al. 2002) genome sequencesprovided landmarks for the understanding of humanbiology.There is a catch. The genome sequence of large organismsis itself a large, unwieldy data set and is opaque toanalysis: For all of the arguments of completeness of thegenome, if we can’t ascribe at least some function to partsof the sequence, it becomes effectively unusable. In additionto this scientific problem of knowing which sequencesare functional or not, there is the somewhat mundaneproblem of simply handling the data size. Thisengineering problem is compounded due to the number ofgenomes now sequenced and the churn rate of genomeand cDNA sequence.Ensembl is designed to overcome these problems andtherefore to make genomes far more useful to a broadrange of audiences (Clamp et al. 2003). Ensembl focuseson providing information to three classes of users: (1)Bench biologists, who generally are focused on one ortwo genes and want a user-friendly, graphical web-basedsystem to access the genome. The ensembl web site,www.ensembl.org, is focused on this user. (2) Mid-scalefunctional genomics users, who are working with sets ofgenes, either due to positional cloning or expression analysis.These users often need their own “slice and dice”data-extraction routines. The EnsMart data-mining system(described below) is focused on this user. (3) Largescalegenomics groups and other bioinformatics groups.We have found that by simply being open in terms of bothsoftware and data, we are able to satisfy most of thisgroup’s needs.Ensembl is not the only group analyzing and displayingthese genomes. The UCSC group under David Hausslerand the NCBI group are both very active in this area.We enjoy healthy competition with these groups and collaborateon the underlying data resources, ensuring, forexample, that the assembly is common between all threesites.RESULTSTable 1 outlines the genomes which Ensembl displays.We make a distinction between genomes where we predictgenes and genomes where the gene structures areprovided by another group. Notice that for all genomesthere have been multiple gene builds, in each case marshalinga set of data resources (e.g., cDNA and EST datasets) and tuning the gene prediction process for eachgenome. A gene build takes information from threesources. cDNAs generated from the target genome arereconciled back onto the genome sequence by a specific“best in genome” process. Then, remaining pieces of thegenome which show strong protein similarity to genes inother organsisms are used to generate “novel” genes viathe program genewise. Finally, EST sequences aremapped back to the genome, clustered, and then a minimumset of transcripts which represent the clusteredESTs are generated. Depending on the species, the ESTdata are sometimes merged with the main cDNA and proteinbuild (e.g., in Anopheles gambiae) and sometimes arekept separate (e.g., in Homo sapiens). This is due to thedifference in EST quality in different genomes, with thelarge, error-prone human EST set proving the hardest touse.The Ensembl web site (www.ensembl.org) is designedfor biological researchers to quickly orient themselves onthe genome and design experiments on the basis of thegenome information. Our two main displays are focusedon genomic sequence and gene products. Increasingly,we have discovered that more and more people want towork with subsets of gene products encoded by a particulargenome. This “set” working behavior is catered to bythe EnsMart data-mining tool available both through theWeb and as a downloadable command-line tool.Assessing the quality of our gene prediction is hard becausewe use all available data at any point in our buildprocess, and new experimental cDNA approaches tend tofocus on currently undiscovered cases. Assessments viaoverlap to dual-genome predictors, which use only thehuman and mouse genome as input (and therefore shouldbe unbiased toward other cDNA or EST evidence), suggestthat there are around another 1,000 protein-codinggenes outside of Ensembl to predict (Guigó et al. 2003).Cold Spring Harbor Symposia on Quantitative Biology, Volume LXVIII. © 2003 Cold Spring Harbor Laboratory Press 0-87969-709-1/04. 213
214 BIRNEY ET AL.Table 1. General Information on Species ActivitiesGenome Gene Exons/ Builds to Assembly %WebSpecies size number gene date authority hitsIn-house genebuildsHomo sapiens 3.23 Gb 24037 8.7 11 NCBI 57Mus musculus 2.50 Gb 24948 8.7 4 NCBI 20Rattus norvegicus 2.56 Gb 23751 7.9 3 RGSC a 9Danio rerio 1.57 Gb 20062 7.9 5 Sanger Institute 7Anopheles gambiae 278 Mb 14707 4.0 4 IAGP b 1Caenorhabditis briggsae 106 Mb 11884 7.2 1 Sanger Institute 1Genebuilds performed outside EnsemblFugu rubripes 390 Mb 35180 4.7 2 IFGC c 1Drosophila melanogaster 128 Mb 13525 4.6 N/A FlyBase 1Caenorhabditis elegans 103 Mb 19988 6.2 N/A WormBase 3a RGSC = Rat Genome Sequencing Consortium: Baylor College of Medicine, Celera Genomics, Genome Therapeutics, The Institutefor Genome Research, The University of British Columbia.b IAGP = International Anopheles Genome Project: EBI/Sanger Institute, Celera Genomics, Genoscope, University of NotreDame, EMBL, Institut Pasteur, IMBB, TIGR.c IFGC = International Fugu Genome Consortium: Institute of Molecular and Cell Biology (Singapore), Joint Genome Institute,Human Genome Mapping Project Resource Centre, Institute for Systems Biology.Other estimates have run closer to 4,000 new genes.There has been a healthy debate on the number of finalgenes in the human genome, which I was foolish enoughto open a book on in 2000, taking bets for the number ofgenes in the human genome. The rules of the bet (writtenin 2000) were that we would declare a winner in 2003.Sadly, our estimates of the gene number do not have aclose margin of error to come to any firm number; however,I was saved by the fact that the distribution of betsis centered around 50,000 (median number in the bettingpool was 52,689). This large overbetting has meant that,although we are not certain of the precise number, therewere only three individuals who placed even close to estimatedbets below 30,000 protein-coding genes. We dulysplit the betting pool between these three participantswithout having to fix on a particular number.Ensembl also calculates orthology relationships betweenthe protein-coding gene sets from the differentgenomes. Each genome pair has a number of unmatchedgenes that have no obvious orthologs in the other species(complex lineage-specific duplications are allowed). Wecall these unmatched genes “orphans.” Table 2 shows thenumber of orphans found between different comparisons,and the total number of orphans across all comparisons ineach species. The presence of orphans is a combination offast-evolving genes that have lost the clear protein similaritysignatures to assign orthology solely on protein sequence;the fact that none of the genomes is finished; errorsin the gene prediction process on genomes, inparticular, the presence of pseudogenes; and finally, erronouslysubmitted cDNA information (for example, genomiccontamination that has a significant open readingframe). By sampling random sets of orphan sequences,we believe that nearly all orphans are due to either misappropiatepseudogene classification (classifying a pseudogeneas a real gene) or cloning artifacts from library sequencing(for example, cloning projects that useddifferential display to clone cancer-specific genes). TheseTable 2. Comparative Analysis of Ensembl Gene Structure PredictionsNumber of orphans per speciesHs Mm Rn Dr Fr a Dm b Ag Ce c CbHs:Mm 2723 2237Hs:Rn 2845 1261Hs:Dr 5465 1333Hs:Fr 4835 13016Mm:Rn 1929 717Mm:Dr 5544 1510Mm:Fr 4696 13271Rn:Dr 4340 1613Rn:Fr 3147 13376Dm:Ag 3043 3747Ce:Cb 4035 22Orphans across 2416 1423 375 719 10397 3043 3747 4035 22all comparisonsNumbers represent the number of orphans both in pairwise comparisons and in terms of comparison to allclosely related species.a Fugu rubripes, b Drosophila melanogaster, and c Caenorhabditis elegans gene structures are imported from externalsources.Pairwise comparison
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ForewordIn 2001, as we considered t
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The Finished Genome Sequence of Hom
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4 ROGERSFigure 2. Accumulation of h
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6 ROGERSFigure 4. Sequencing center
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8 ROGERSabcFigure 5. Ensembl view o
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10 ROGERS2000. Analysis of vertebra
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The Human Genome: Genes, Pseudogene
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VARIATION ON CHROMOSOME 7 15rived f
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VARIATION ON CHROMOSOME 7 17DNAs an
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VARIATION ON CHROMOSOME 7 19expecte
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VARIATION ON CHROMOSOME 7 21Drosoph
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Mutational Profiling in the Human G
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HUMAN MUTATIONAL PROFILING 25Anothe
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HUMAN MUTATIONAL PROFILING 27Figure
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HUMAN MUTATIONAL PROFILING 29Rieder
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32 SCHMUTZ ET AL.algorithm itself,
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34 SCHMUTZ ET AL.Figure 2. Genomic
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36 SCHMUTZ ET AL.compared. Some of
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Human Subtelomeric DNAH. RIETHMAN,
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HUMAN SUBTELOMERIC SEQUENCES 41The
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HUMAN SUBTELOMERIC SEQUENCES 43cate
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HUMAN SUBTELOMERIC SEQUENCES 45Figu
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HUMAN SUBTELOMERIC SEQUENCES 47pres
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50 COLLINSand expand the genomics r
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52 COLLINSFigure 2. A public-sector
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54 COLLINSdefine all the parts of t
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56 BENTLEYmon over many generations
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58 BENTLEYTable 1. Genetic Disease
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60 BENTLEY(Clark et al. 1998; Reich
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62 BENTLEYACKNOWLEDGMENTSThe author
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SNP Genotyping and Molecular Haplot
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GENETIC ANALYSIS OF DNA POOLS 67gen
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70 FAN ET AL.matrix is then mated t
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72 FAN ET AL.Figure 3. Views of gen
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74 FAN ET AL.including 32 duplicate
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76 FAN ET AL.Figure 7. Allele-speci
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78 FAN ET AL.microsphere-based assa
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80 BERTRANPETIT ET AL.function, may
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82 BERTRANPETIT ET AL.diversity in
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84 BERTRANPETIT ET AL.gree of block
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86 BERTRANPETIT ET AL.Figure 1. Dec
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88 BERTRANPETIT ET AL.1999. Populat
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90 WINDEMUTH ET AL.Expression data.
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92 WINDEMUTH ET AL.Table 1. A Summa
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94 WINDEMUTH ET AL.Table 2. Signifi
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96 WINDEMUTH ET AL.Table 3. Summary
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98 WINDEMUTH ET AL.Table 6. List of
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100 WINDEMUTH ET AL.Table 6. (Conti
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102 WINDEMUTH ET AL.Table 6. (Conti
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104 WINDEMUTH ET AL.much of a surpr
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106 WINDEMUTH ET AL.Given our resul
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Genetic Variation and the Control o
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GENETIC CONTROL OF TRANSCRIPTION 11
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GENETIC CONTROL OF TRANSCRIPTION 11
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Genome-wide Detection and Analysis
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RECENT SEGMENTAL DUPLICATIONS 117Fi
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RECENT SEGMENTAL DUPLICATIONS 119St
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RECENT SEGMENTAL DUPLICATIONS 121Co
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RECENT SEGMENTAL DUPLICATIONS 123Ho
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The Effects of Evolutionary Distanc
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EVOLUTIONARY DISTANCE AND GENE PRED
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EVOLUTIONARY DISTANCE AND GENE PRED
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Lineage-specific Expansion of KRAB
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EVOLUTION OF ZNF GENES 133Figure 2.
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EVOLUTION OF ZNF GENES 135Figure 4.
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EVOLUTION OF ZNF GENES 137get gene,
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EVOLUTION OF ZNF GENES 139Y., Goodw
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Sequence Organization and Functiona
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CENTROMERE ANNOTATION 143THE CENTRO
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CENTROMERE ANNOTATION 145Figure 4.
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CENTROMERE ANNOTATION 147CONCLUSION
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CENTROMERE ANNOTATION 149Schueler M
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152 PARKHILL AND THOMSONFigure 1. T
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154 PARKHILL AND THOMSONshow very h
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156 PARKHILL AND THOMSONGene Loss a
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158 PARKHILL AND THOMSONYersinia ad
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160 MCKAY ET AL.Choosing Candidate
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Page 231 and 232: 224 ZHANGWe are waiting for experim
Page 234 and 235: Ontologies for Biologists: A Commun
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DETECTING MULTISPECIES CONSERVED SE
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266 ROE ET AL.noncoding regions. On
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268 ROE ET AL.a48 hpf embryos in Mi
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270 ROE ET AL.aNovel gene KIAA0819[
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272 ROE ET AL.aMouseRatAP00354.2 Hu
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274 ROE ET AL.Tautz D. and Pfeifle
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276 JAILLON ET AL.Detection of Evol
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278 JAILLON ET AL.Table 1. Distribu
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280 JAILLON ET AL.Table 3. Distribu
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282 JAILLON ET AL.ecotig is a resul
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284 OVCHARENKO AND LOOTSdivergent r
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286 OVCHARENKO AND LOOTSmodulation
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288 OVCHARENKO AND LOOTSsequencing
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290 OVCHARENKO AND LOOTSments of cl
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Evolution of Eukaryotic Gene Repert
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EVOLUTION OF EUKARYOTIC GENES AND I
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304 PENNACCHIO, BAROUKH, AND RUBINA
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306 PENNACCHIO, BAROUKH, AND RUBINh
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308 PENNACCHIO, BAROUKH, AND RUBINA
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High-Throughput Mouse Knockouts Pro
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314 FRIDDLE ET AL.screen to lines o
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Identification of Novel Functional
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100 bp ladder68G1168G1168H1168H6100
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FUNCTIONAL ELEMENTS IN HUMAN DNA 32
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High-resolution Human Genome Scanni
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HUMAN GENOME SCANNING 325false-posi
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HUMAN GENOME SCANNING 327affecting
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HUMAN GENOME SCANNING 329methods fo
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332 MALEK ET AL.Figure 1. The bacte
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334 MALEK ET AL.J., Vincent S., and
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336 HARDISON ET AL.reflect blocks o
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338 HARDISON ET AL.plain the region
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340 HARDISON ET AL.CALIBRATION OF T
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342 HARDISON ET AL.PositionRP2.3noE
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344 HARDISON ET AL.cific chromosoma
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346 WESTON ET AL.these differences
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348 WESTON ET AL.els controlled by
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350 WESTON ET AL.ures prominently i
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352 WESTON ET AL.nal and Bop, which
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354 WESTON ET AL.ablp 1466 bopbcrtB
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356 WESTON ET AL.like fold (Fig. 6)
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Implications of Genomics for Public
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GENETIC EPIDEMIOLOGY 361lytic epide
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GENETIC EPIDEMIOLOGY 363curate risk
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A Model System for Identifying Gene
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PTC TASTE GENETICS 367Figure 2. Hap
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PTC TASTE GENETICS 369Table 2. Hapl
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PTC TASTE GENETICS 371the emergence
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374 MCCALLION ET AL.Figure 1. Schem
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376 MCCALLION ET AL.lier (Carrasqui
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378 MCCALLION ET AL.Table 3. HSCR A
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380 MCCALLION ET AL.Figure 3. Trans
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Genetics of Schizophrenia and Bipol
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SCHIZOPHRENIA AND BIPOLAR AFFECTIVE
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The Genetics of Common Diseases: 10
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GENETICS OF COMMON DISEASES 397with
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GENETICS OF COMMON DISEASES 399SELE
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GENETICS OF COMMON DISEASES 401F.,
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404 CHEUNG ET AL.netic analysis. Ex
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406 CHEUNG ET AL.Figure 3. The expr
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Regulation of α-Synuclein Expressi
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α-SYNUCLEIN EXPRESSION AND PD 411T
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1. The levels of α-synuclein prote
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α-SYNUCLEIN EXPRESSION AND PD 415g
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418 BOTSTEINFigure 1. (A) Blectron
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420 BOTSTEINFigure 3. Cluster diagr
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422 BOTSTEINFigure 6. Kaplan-Meier
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424 BOTSTEINGarber M.E., Troyanskay
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426 ANTONARAKIS ET AL.1316192225283
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428 ANTONARAKIS ET AL.Figure 5. Sam
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430 ANTONARAKIS ET AL.POPULATION VA
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432 JORGENSEN ET AL.tive small mole
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434 JORGENSEN ET AL.FLAG-tagged pro
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436 JORGENSEN ET AL.visualization t
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438 JORGENSEN ET AL.AArp2/3 Complex
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Pathway40S440 JORGENSEN ET AL.ANutr
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442 JORGENSEN ET AL.Giaever G., Chu
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Genomic Disorders: Genome Architect
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GENOME ARCHITECTURE AND GENOMIC DIS
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Human Versus Chimpanzee Chromosome-
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HUMAN VS. CHIMP CHROMOSOME COMPARIS
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HUMAN VS. CHIMP CHROMOSOME COMPARIS
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Novel Transcriptional Units and Unc
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TRANSCRIPTIONAL UNITS AND GENE PAIR
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mtDNA Variation, Climatic Adaptatio
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mtDNA VARIATION 473Figure 3. Region
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ANALYSIS OF ADAPTIVE SELECTION FORR
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mtDNA VARIATION 477Figure 8. Temper
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Positive Selection in the Human Gen
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HUMAN-SPECIFIC EVOLUTIONARY CHANGES
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488 UNDERHILLorigin episodes, each
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490 UNDERHILLhaplogroups C through
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492 UNDERHILLO (Fig. 2e) that share
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The New Quantitative BiologyM.V. OL
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NEW QUANTITATIVE BIOLOGY 497alone.
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NEW QUANTITATIVE BIOLOGY 499There w
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NEW QUANTITATIVE BIOLOGY 501ceded,
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