The Genom of Homo sapiens.pdf

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THE GENOME KNOWLEDGEBASE 239Figure 2. A detail page summarizing an early step in the G 1 phase of the mitotic cell cycle.Figure 3. Asking GK to find the shortest path that connects D-fructose (upper left) to pyruvate (bottom right). Boxes represent entities:encoded entities (proteins and nucleic acids, dark blue) or non-encoded entities (small molecules, light blue). Entities are connected tothe events in which they participate by arrows whose colors denote the entities’ roles: (red) catalyst; (green) input; (blue) output.
240 JOSHI-TOPE ET AL.state of knowledge. In terms of currency, GK tries tobe more of a textbook than a review article. This policyreduces the rate at which modules go out of date,and also allows authors to defer controversial or uncertainassertions. This is partially offset by an updatepolicy which calls for each module to be reviewed andupdated once every three years.• As discussed below, our data model makes deliberatesimplifications. For example, we do not attempt to accountfor rate constants, concentration gradients, orother numerical values that would be key for truephysiological modeling.THE GK DATA MODELThe core of GK is its data model, a simplified versionof which is shown in Figure 4. There are three fundamentaldata types: Physical Entity, Reaction, and Pathway.A reaction is a biological event that consumes oneor more physical entities as input, and produces one ormore physical entities as output. A physical entity, as itsname implies, is any molecule that participates in a biologicalprocess and can be as simple as a molecule of wateror as complex as a multi-subunit receptor complex. APathway is group of reactions that together drive forwarda biological process.This paradigm turns out to be quite general as demonstratedby examples from two very different fields—classicbiochemistry and the more modern study of signaltransduction. The phosphorylation of glucose to formglucose-6-phosphate is a classic reaction in intermediatemetabolism, and it is easy to see how it is represented inGK: The inputs are D-glucose and ATP, and the outputsare glucose-6-phosphate and ADP. Downstream reactions,such as those of glycolysis, are dependent on theavailability of glucose-6-phosphate as input.Now let us look at the conformational change that occurswhen calcium binds to calmodulin, thereby puttingcalmodulin into its “active” form, and relaying the signalto downstream effector molecules. GK represents the inputsas calmodulin and inorganic calcium, and the outputsas the calmodulin–calcium complex. Downstreamreactions, such as those mediated by protein kinase A, aredependent on the calmodulin–calcium complex. We havethus neatly disposed of the need to represent elusive ideaslike “activation” and “transduction,” and have enabledFigure 4. The GK data model is built around the Reaction andPhysical Entity. Pathways are used to organize reactions in a traditionalgoal-oriented manner.pathways to be built by reasoning, based on the presenceof required inputs. Reactions are also used in GK to representthe stepwise assembly of macromolecular complexes.Reactions include a number of attributes in addition totheir inputs and outputs. These include such things as catalystand catalyst activities associated with the reaction,the species in which the reaction is known to occur (human,or, when the existence of a human reaction must beinferred from another species, the name of that species),the subcellular compartment in which the reaction occurs,GO terms associated with the reaction, the phase of thecell cycle to which the reaction may be limited, and literaturereferences that document this reaction.The GK data model includes many subclasses andvariants of the basic reaction and physical entity datatypes. For example, there is a generic physical entitytype used to represent the concept of “all mRNAs” as opposedto a particular instance of an mRNA. The Pathwaydata type allows us to group reactions into a hierarchicalseries of steps and sub-steps. This is crucial for GK’s didacticfunction and needed to accommodate the frequentcases in which the exact reactions that occur are speculative.THE GK BUSINESS MODELGK is organized along the lines of an electronic publication.The GK editor-in-chief, in consultation with theprincipal investigators and an editorial advisory board,annually maps out a calendar of modules and a rough releaseschedule. We then solicit experts in the field to authormodules. We have had most success when we haveidentified a single module “lead author” who then helpsdevelop the structure of the module, recruits additionalauthors, and supervises the completion of the annotation.The mechanics of authoring a GK module are simplebut effective. We give each author a Microsoft Power-Point presentation, which contains a series of blank diagramsin a standardized format, and ask the author to fillin the blanks. For example, one diagram shows a genericbinding reaction in which two molecules interact to forma complex. The author copies this diagram and changesthe labels as appropriate, to indicate a specific reaction.The author then fills in fields that indicate the species,phase of the cell cycle, subcellular location, and the citationsthat describe this reaction.When the PowerPoint representation is complete, theauthor sends it to a GK curator, who extracts the informationand enters it into the GK database using a Knowledgebaseeditor tool called Protégé (Noy et al. 2001). Thedraft module is reviewed internally by the GK staff and issubsequently made available on a prepublication Website for the author to review and approve. After the authorapproves the draft, it is made available to one or morepeer reviewers, who are asked to verify the completenessand correctness of the module. We supplement the humanpeer review with a series of software checks to verify theintegrity of the described pathways, the validity of proteinand citation accession numbers, and concordance betweenthe molecular functions assigned to gene products
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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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162 MCKAY ET AL.new comparative too
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164 MCKAY ET AL.rich. Based on a th
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166 MCKAY ET AL.Embryonic Muscle an
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168 MCKAY ET AL.native polyadenylat
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Building Comparative Maps Using 1.5
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HUMAN CHROMOSOME 1p IN THE DOG 1731
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HUMAN CHROMOSOME 1p IN THE DOG 175(
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HUMAN CHROMOSOME 1p IN THE DOG 177l
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180 GEORGES AND ANDERSSON5. There i
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182 GEORGES AND ANDERSSONplied to r
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184 GEORGES AND ANDERSSONbe common
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186 GEORGES AND ANDERSSONin humans
Page 196 and 197: Evolving Methods for the Assembly o
Page 198 and 199: ASSEMBLING LARGE GENOMES 191Figure
Page 200 and 201: ASSEMBLING LARGE GENOMES 193tant ad
Page 202 and 203: Mouse Genome Encyclopedia ProjectY.
Page 204 and 205: MOUSE GENOME ENCYCLOPEDIA PROJECT 1
Page 212 and 213: DNA Sequence Assembly and Multiple
Page 214 and 215: EULERIAN ASSEMBLY AND MULTIPLE ALIG
Page 220 and 221: Ensembl: A Genome InfrastructureE.
Page 222: ENSEMBL 215projects often submit th
Page 225 and 226: 218 ZHANGthe majority of these are
Page 227 and 228: 220 ZHANGFigure 2. Demonstration of
Page 229 and 230: 222 ZHANG(G.X. Chen et al., in prep
Page 231 and 232: 224 ZHANGWe are waiting for experim
Page 234 and 235: Ontologies for Biologists: A Commun
Page 236 and 237: ONTOLOGIES FOR BIOLOGISTS 229al. 20
Page 238 and 239: ONTOLOGIES FOR BIOLOGISTS 231TOPIC
Page 240 and 241: ONTOLOGIES FOR BIOLOGISTS 233a.b.Fi
Page 242: ONTOLOGIES FOR BIOLOGISTS 2352003.
Page 245: 238 JOSHI-TOPE ET AL.Figure 1. The
Page 249 and 250: 242 JOSHI-TOPE ET AL.and co-immunop
Page 252 and 253: The Share of Human Genomic DNA unde
Page 254 and 255: DNA UNDER SELECTION FROM HUMAN-MOUS
Page 262 and 263: Detecting Highly Conserved Regions
Page 264 and 265: DETECTING MULTISPECIES CONSERVED SE
Page 270: DETECTING MULTISPECIES CONSERVED SE
Page 273 and 274: 266 ROE ET AL.noncoding regions. On
Page 275 and 276: 268 ROE ET AL.a48 hpf embryos in Mi
Page 277 and 278: 270 ROE ET AL.aNovel gene KIAA0819[
Page 279 and 280: 272 ROE ET AL.aMouseRatAP00354.2 Hu
Page 281 and 282: 274 ROE ET AL.Tautz D. and Pfeifle
Page 283 and 284: 276 JAILLON ET AL.Detection of Evol
Page 285 and 286: 278 JAILLON ET AL.Table 1. Distribu
Page 287 and 288: 280 JAILLON ET AL.Table 3. Distribu
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Page 291 and 292: 284 OVCHARENKO AND LOOTSdivergent r
Page 293 and 294: 286 OVCHARENKO AND LOOTSmodulation
Page 295 and 296: 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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