The Genom of Homo sapiens.pdf

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NEW QUANTITATIVE BIOLOGY 497alone. There was much discussion at the Symposiumabout which set of organisms would provide the ideal inputsfor comparative analysis. However, given the diversityof questions that can be addressed through evolutionarycomparisons, consensus on this point seems unlikely.As Eddy Rubin put it, “There is no perfect distance forevolutionary comparisons.” At one extreme, Weissenbach(Jaillon et al.) emphasized the utility of distantcomparisons for tracking down the stray exons that continueto elude standard annotation methods. For this purpose,long divergence times decrease the “noise” associatedwith sequences that evolve more rapidly than typicalexons. On the basis of a sophisticated statistical analysisof exon-detection methods, Brent (Wang et al.) concludedthat the mouse–chicken divergence was nearlyideal: At this evolutionary distance, intron alignmentshave just been eliminated, and exon alignments are stillquite good. For detection of functional elements thatevolve rapidly, Rubin (Pennacchio et al.) advocated comparativeanalysis of many primates, or even many individualsof a single species. This approach has the advantageof constraining comparisons to biologically similarorganisms but requires far more data than a reliance ondistant comparisons. In a similar vein, Birney (Birney etal.) advocated analysis of single-nucleotide differences(SNDs) between closely related species to detect the effectsof selection on wild populations such as Anophelesmosquitoes. Brent (Wang et al.) predicted that it will ultimatelybe possible to use multiple, simultaneous comparisonsof genome sequences to infer the rate of evolutionat every nucleotide position. However, most would agreewith his assessment that “We are not there yet.”The two main analyses of closely related metazoangenomes both involved human–chimpanzee comparisons.Clark (Clark et al.) compared rates of nonsynonymousversus synonymous substitutions in three-waycomparisons between human, chimpanzee, and mouse.Nearly 10% of mammalian genes show statistically significantevidence of positive selection that has acceleratedon the human lineage. However, this figure is reminiscentof Haussler’s (Chiaromonte et al.) estimate that4–7% of mammalian genomes are under purifying selection.Both estimates are based on decomposing two heavilyoverlapping distributions; hence, neither producesanything like a clean list of which elements do or do notmeet the specified criteria. S. Pääbo presented evidencefor positive selection at the FOXP2 gene, which has beenimplicated in language development through studies ofrare families with FOXP2 mutations. He also reported onloss of function in many olfactory-receptor genes in boththe human and chimpanzee lineages, with the most pronouncedloss having occurred on the human lineage.In contrast to the many talks that attempted to infer thefunctional importance of genomic elements through sequenceconservation, Phil Green presented data showingthat transcribed sequences are often discernable on thebasis of an entirely different phenomenon. Promiscuousgene transcription in the germ line appears to affect nucleotide-substitutionpatterns during evolution, presumablyvia operation of a transcription-coupled repair sys-tem. This mechanism leads to a statistically significantexcess of G’s and T’s in the coding strand of ~70% of humangenes. These data are perhaps the most convincingevidence available that most genes are transcribed, atleast at minimal levels, in the germ line. Comparative-sequenceanalysis is often treated as a signal-to-noise problemin which evolutionarily conserved sequences are the“signal” and the much larger number of neutrally evolvingsequences are the “noise.” Green pointed out that hisfindings indicate that the noise actually carries a signalabout the locations of functional units in genomes.The basic data structure for all comparative genomicsis the sequence alignment. Indeed, behind every sequence-basedphylogenetic tree is a multi-sequencealignment, often constructed by less rigorous methodsthan those used to build the actual tree. Waterman reportedon a new approach to sequence alignment based onEulerian paths. This method appears to be less easily confusedby the ubiquitous repeated sequences that pose themajor challenge to sequence-alignment algorithms. Thenew method also has better computational performancethan competing approaches and, therefore, may offer apractical path toward multiple alignment of thousands ofsequences. Waterman jokingly disparaged talks that include“shameless used-car selling” and then proceeded topromote his method as outperforming the widely usedClustalW algorithm. Kent (Chiaromonte et al.) discussedanother aspect of the alignment problem, long-rangealignment of relatively diverged sequences whosealignable segments are interrupted by long insertions anddeletions. A rich source of examples is provided by human–mousealignments, which fail altogether in “synteny-breakpointhot spots” that can span hundreds ofthousands of base pairs.The impressive focus on comparative genomics at thismeeting, by experimentalists and bioinformaticians alike,foreshadows an era in which analyses based on selectedregions will be displaced by simultaneous, comparativeanalyses of multiple whole genomes. The potential powerof such methods for higher organisms was foreshadowedat the meeting by reports on their application to bacteria.C. Fraser gave a broad overview of the many ways inwhich whole-genome comparisons are transforming microbiology.With 105 complete genomes available—includingan increasing number of clusters of sequencesfrom closely related bacteria—it is becoming possible toaddress the interrelated questions “How did thesegenomes evolve?” and “How do they work?” Parkhill(Parkhill and Thomson) gave several examples of ways inwhich a better understanding of genome evolution illuminatesgenome function and vice versa. A dramaticfinding is that such feared human pathogens asSalmonella typhi and Bordetella pertussis show strongsignatures of sudden, extreme specialization. By acquiringsome new genes and losing function in many oldones, these pathogens have lost the ability of their ancestralforms to survive in general environments, to interactwith diverse hosts, and to maintain relatively benign interactionswith their hosts. Much of this evolutionary activityappears to be recent, possibly indicating that these
498 OLSONbacterial lineages prospered by piggybacking on the hugeexpansion of the human population that followed the developmentof agriculture.Although available data are much sparser for metazoans,analogous evolutionary processes appear to haveoccurred throughout the tree of life. Evolution appears toinvolve repeated cycles of specialization in which individualspecies and whole evolutionary lineages periodicallyshed genes that were present in more generalist ancestors.Koonin reported on large-scale comparisons ofthe gene content of available metazoan genomes. Thesecomparisons reveal a pattern of many, independent genelossevents on different lineages. This finding, which applieson a timescale of hundreds of millions of years, isreminiscent of S. Pääbo’s report of recent large-scale lossof olfactory receptor genes on the chimpanzee and humanlineages. In all likelihood, the recent loss of these genes issimply an instance of the general process described byKoonin (Rogozin et al.).Given the desirability of basing the comparative genomicsof all evolutionary groups on comparisons ofmultiple whole-genome sequences, the meeting wasoddly lacking in serious assessments of the rate at whichsuch data will become available and the extent to whichthey will be of adequate quality to meet future needs. Thisvoid appears to have reflected lack of activity rather thanthe tastes of the organizers. E. Myers did report on incrementalprogress with his whole-genome assembler. Healso expressed the view that reasonable whole-genomeassemblies were only attainable when the sequence coveragewas at least 8x, a sampling depth higher than thatreached in many current whole-genome-shotgun projects.Gibbs (Gibbs and Weinstock) was a lone voice who actuallyaddressed the technical challenges associated withadding value to the raw output of whole-genome-shotgunprojects. His main message was that, although it may beimpractical to keep the army of finishers who producedthe current human-genome sequence in the field, thereare numerous lower-cost options for improving on rawwhole-genome assemblies.GENOME ANNOTATION AND RESOURCEDEVELOPMENTThis Symposium was dominated by the consumers, notthe producers, of genomic sequence. These consumerswant, first and foremost, better annotation. Gerald Rubinemphasized challenges and successes in annotating theDrosophila genome. Challenges include the presence ofhundreds of genes in heterochromatic regions that remaindifficult to sequence reliably, much less to annotate.There are also many instances of genes embedded withingenes, transcribed either from the same or the oppositestrand, a theme also developed by Lipovich (Lipovichand King) for the human. Rubin’s emphasis on thesecomplex gene structures stimulated a lively exchangewith Brent about the prospects of detecting and disentanglingoverlapping and embedded genes computationally.Whereas Brent expressed optimism about the pace atwhich gene-detection programs are improving, Rubincountered with the observation that “sequencing is gettingcheaper.” The implication of cheaper sequencing isthat the comparative genomics of Drosophila speciesmay provide the best path toward comprehensive geneidentification for D. melanogaster. Rubin forcefully advocatedthe view that annotation and resource creation(e.g., full-length cDNA clones, comprehensive collectionsof transposon mutations) should be taken to thesame level of completion as the sequence itself.Many talks emphasized the breadth of commitment tothis goal, at least for intensively studied organisms.Hayashizaki described impressive progress on developmentof a full-length-cDNA resource for the mouse. Indeed,he indicated that the resource is now sufficientlyrich that more attention is needed to distribution mechanisms.Hayashizaki (Hayashizaki) illustrated the increasingdisparity between the ease of distributing data and thedifficulty of distributing biological resources by describinghis experiences sending out the FANTOM2 set of60,770 mouse full-length-cDNA clones packaged with100 kg of dry ice. To solve this problem, the Riken grouphas developed DNA-printing technology that may allowroutine distribution of DNA samples, designed for easyPCR amplification, on printed pages. Perhaps this applicationwill provide a long-term future for hard-copy editionsof biological journals!Other examples of large-scale biological resources includedFriddle’s (Friddle et al.) report on high-throughputmouse-knockout technology. His talk described an insertion-mutagenesisprotocol based on retroviral constructsthat disrupt mouse transcription units. This method hasyielded over 200,000 ES cell lines containing unique insertionevents, which disrupt the function of ~60% ofmouse genes. The positions of the insertions are determinedby sequencing the exon downstream from the insertionsite following PCR amplification of reverse transcriptsof mRNAs transcribed from a promoter within theinserted element. RNAi offers a potential alternative toknockout technology for functional annotation ofgenomes. Hannon reported on major progress in understandingthe mechanism of RNAi, a step that should facilitateincreased reliance on this relatively cheap alternativeto traditional gene-inactivation methods.High-throughput in situ hybridization to developingembryos provides still another experimental means offunctional annotation. Roe (Roe et al.) described a projectto gather such data for the zebrafish, with an initial focuson the orthologs of genes present on human Chromosome22. This talk was of technical interest in that Roe, agenome-sequencing veteran, retooled his sequencingpipeline for this purpose. Malek (Malek et al.) describeda way of coupling sequencing technology even more directlyto functional annotation. In his system, which hasbeen pioneered on the bacterium Rickettsia sibirica, thesubclone libraries that provide sequencing templates forwhole-genome-shotgun sequencing are constructed in avector that supports their use as “bait” and “prey” sequencesin a bacterial implementation of the two-hybridmethod for detecting protein–protein interactions. Hence,simply as a by-product of genome sequencing, one obtainsa saturating collection of sequenced clones for usein protein–protein-interaction mapping.
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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
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Evolving Methods for the Assembly o
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ASSEMBLING LARGE GENOMES 191Figure
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ASSEMBLING LARGE GENOMES 193tant ad
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Mouse Genome Encyclopedia ProjectY.
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MOUSE GENOME ENCYCLOPEDIA PROJECT 1
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DNA Sequence Assembly and Multiple
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EULERIAN ASSEMBLY AND MULTIPLE ALIG
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Ensembl: A Genome InfrastructureE.
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ENSEMBL 215projects often submit th
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218 ZHANGthe majority of these are
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220 ZHANGFigure 2. Demonstration of
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222 ZHANG(G.X. Chen et al., in prep
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224 ZHANGWe are waiting for experim
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Ontologies for Biologists: A Commun
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ONTOLOGIES FOR BIOLOGISTS 229al. 20
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ONTOLOGIES FOR BIOLOGISTS 231TOPIC
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ONTOLOGIES FOR BIOLOGISTS 233a.b.Fi
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ONTOLOGIES FOR BIOLOGISTS 2352003.
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238 JOSHI-TOPE ET AL.Figure 1. The
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240 JOSHI-TOPE ET AL.state of knowl
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242 JOSHI-TOPE ET AL.and co-immunop
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The Share of Human Genomic DNA unde
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DNA UNDER SELECTION FROM HUMAN-MOUS
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Detecting Highly Conserved Regions
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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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Page 462 and 463: Human Versus Chimpanzee Chromosome-
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Page 468 and 469: Novel Transcriptional Units and Unc
Page 470 and 471: TRANSCRIPTIONAL UNITS AND GENE PAIR
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Page 486 and 487: Positive Selection in the Human Gen
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Page 495 and 496: 488 UNDERHILLorigin episodes, each
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