The Genom of Homo sapiens.pdf

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ANALYSIS OF HUMAN PROMOTERS 223nacchio and Rubin 2001), because different promotersevolve at different rates, multiple species would beneeded for narrowing down to short TFBSs. Initial successin yeast (Cliften et al. 2003; Kellis et al. 2003) maynot directly translate to human; novel integrated approacheswould be required to find functional cis elementseven if the number of mammalian genomes weredoubled.Integration, Combinatorial Analysis, andNetwork ReconstructionGenomic data is noisy; the best weapon for combatingnoise is signal correlation analysis. Combinatorial interactionamong TFs introduces correlation among theirbinding sites. Recently, there have been new motif-findingalgorithms, such as CO-Bind (GuhaThakurta andStormo 2001), that are designed specifically for detectingcorrelated motifs. Integration of evolutionary conservationwith word-pair analysis can yield a better regressionto expression data (Chiang et al. 2003).Integrating ChIP-chip and expression data at the singlemotif level has recently been attempted (Conlon et al.2003). We have developed two methods for studying cooperativityby integrating ChIP-chip data and microarrayexpression data. For a given pair of TFs, A and B, the firstmethod compares expression patterns of the targets ofboth TFs to that of A or B alone. If the former is more coherent(correlated), it is more likely that the two TFs areinteracting in the transcription regulation of their commontargets (Banerjee and Zhang 2003). The secondmethod further integrates with promoter sequence analysisin order not only to infer the interacting TFs, but alsoto assign their corresponding binding sites by iterativelyand exhaustively searching for significant TF combinationsand motif combinations up to the triplet level (M.Kato et al., in prep.). After analyzing over one hundredTF ChIP-chip data (Lee et al. 2002), we were able to reconstructthe yeast cell cycle transcriptional regulationnetwork so that (1) it extends the previous chain of singleregulators to an expanded chain of regulatory modules;(2) modules at adjacent phases often share a commoncomponent that can bridge the continuity of the cycle; (3)there are modules at specific checkpoints (branchpoints)that allow cell entry or exit of the cycle according to externalsignals (Fig. 4). Experimental verification is necessaryto confirm any network predictions (Segal et al.2003).Figure 4. Reconstructed yeast cell cycle transcriptional regulation network. (Adapted from M. Kato et al., in prep.)
224 ZHANGWe are waiting for experimentalists to generate goodqualitydata of ChIP-chip and expression from the samesample preparations for mammalian systems, as well asto sequence multiple vertebrate genomes. Mammalsalone are not enough for cis-element studies about human;one needs distant organisms (such as chicken, forphylogenetic footprinting) as well as close ones (such aschimpanzee, for phylogenetic shadowing).CONCLUSIONSIt is clear now that having a “periodic table” of genes isnot enough; we also need a network diagram telling ushow the genes are connected, and for this, we are going toneed another “periodic table” of gene regulatory elementsand a “wiring diagram” that connects each regulator to itstargets. A combination of computational and functionalgenomics will help us to fill up these tables quickly. Infrastructuresuch as promoter databases and cis-element/trans-factordatabases is urgently needed. Newtechnologies that can provide a different genome-wideview of the regulatory networks and new algorithms thatintegrate various large-scale data will be the keys for attackinghuman gene regulation problems (Banerjee andZhang 2002). Conservation is important for revealingfunction; non-conservation can be even more importantfor understanding evolution (Wray et al. 2003). The recentdiscovery of a promoter that acquired p53 responsivenessduring primate evolution through microsatelliteexpansion of weak binding sites (Contente et al. 2003) isan amazing testimony, and for this, one would have tolook beyond just rodents.ACKNOWLEDGMENTSI thank all present and previous members of the Zhanglab and my collaborators for contributing most of the dataand the figures, many before publication. The Zhang labis supported by grants (HG-01696, GM-60513, CA-81152, CA-88351) from the National Institutes of Health.REFERENCESAntequera F. and Bird A. 1993. Number of CpG islands andgenes in human and mouse. Proc. Natl. Acad. Sci. 90: 11995.Ashburner M., Ball C.A., Blake J.A., Botstein D., Butler H.,Cherry J.M., Davis A.P., Dolinski K., Dwight S.S., EppigJ.T., Harris M.A., Hill D.P., Issel-Tarver L., Kasarskis A.,Lewis S., Matese J.C., Richardson J.E., Ringwald M., RubinG.M., and Sherlock G. 2000. Gene ontology: Tool for the unificationof biology. The Gene Ontology Consortium. Nat.Genet. 25: 25.Bailey T.L. and Elkan C.P. 1994. Fitting a mixture model by expectationmaximization to discover motifs in biopolymers.Proc. Int. Conf. Intell. Syst. Mol. Biol. 2: 28.Bajic V.B. and Seah S.H. 2003. Dragon Gene Start Finder identifiesapproximate locations of the 5´ ends of genes. NucleicAcids Res. 31: 3560.Bajic V.B., Seah S.H., Chong A., Zhang G., Koh J.L.Y., andBrusic V. 2002. Dragon Promoter Finder: Recognition of vertebrateRNA polymerase II promoter. Bioinformatics 18: 198.Banerjee N. and Zhang M.Q. 2002. Functional genomics as appliedto mapping transcription regulatory networks. Curr.Opin. Microbiol. 5: 313._______ . 2003. Identification of cooperativity among transcriptionfactors controlling cell cycle in yeast. Nucleic Acids Res. 31:7024.Burge C. and Karlin S. 1997. Prediction of complete gene structuresin human genomic DNA. J. Mol. Biol. 268: 78.Bussemaker H.J., Li H., and Siggia E.D. 2001. Regulatory elementdetection using correlation with expression. Nat. Genet.27: 167.Chen J.J., Lee S., Zhou G., Rowley J.D., and Wang S.M. 2003.Generation of longer cDNA fragments from SAGE tags forgene identification. Methods Mol. Biol. 221: 207.Chiang D.Y., Moses A.M., Kellis M., Lander E.S., and Eisen M.2003. Phylogenetically and spatially conserved word pairs associatedwith gene-expression changes in yeasts. GenomeBiol. 4: R43.Cliften P., Sudarsanam P., Desikan A., Fulton L., Fulton B., MajorsJ., Waterson R., Cohen B.A., and Johnston M. 2003.Finding functional features in Saccharomyces genomes byphylogenetic footprinting. Science 301: 71.Conlon E.M., Liu X.S., Lieb J.D., and Liu J.S. 2003. Integratingregulatory motif discovery and genome-wide expression analysis.Proc. Natl. Acad. Sci. 100: 3339.Contente A., Zischler H., Einspanier A., and Dobbelstein M.2003. A promoter that acquired p53 responsiveness duringprimate evolution. Cancer Res. 63: 1756.Das M., Burge C.B., Park E., Colinas J., and Pelletier J. 2001.Assessment of the total number of human transcription units.Genomics 77: 71Davuluri R.V., Grosse I., and Zhang M.Q. 2001. Computationalidentification of promoters and first exons in the humangenome. Nat. Genet. 29: 412.Davuluri R.V., Suzuki Y., Sugano S., and Zhang M.Q. 2000.CART classification of 5´UTR sequences. Genome Res. 10:1807.Down T.A. and Hubbard T.J. 2002. Computational detectionand location of transcription start sites in mammalian genomicDNA. Genome Res. 12: 458.Fickett J.W. and Hatzigeorgiou A.G. 1997. Eukaryotic promoterrecognition. Genome Res. 7: 861.Gardiner-Garden M. and Frommer M. 1987. CpG islands in vertebrategenomes. J. Mol. Biol. 196: 261.GuhaThakurta D. and Stormo G.D. 2001. Identifying target sitesfor cooperatively binding factors. Bioinformatics 17: 608.Guigo R., Dermitzakis E.T., Agarwal P., Ponting C.P., Parra G.,Reymond A., Abril J.F., Keibler E., Lyle R., Ucla C., AntonarakisS.E., and Brent M.R. 2003. Comparison of mouseand human genomes followed by experimental verificationyields an estimated 1,019 additional genes. Proc. Natl. Acad.Sci. 100: 1140.Hannenhalli S. and Levy S. 2001. Promoter prediction in the humangenome. Bioinformatics (suppl. 1) 17: S90.Hertz G.Z., Hartzell G.W., III, and Stormo G.D. 1990. Identificationof consensus patterns in unaligned DNA sequencesknown to be functionally related. Comput. Appl. Biosci. 6: 81.Hubbard T., Barker D., Birney E., Cameron G., Chen Y., ClarkL., Cox T., Cuff J., Curwen V., Down T., Durbin R., Eyras E.,Gilbert J., Hammond M., Huminiecki L., Kasprzyk A.,Lehvaslaiho H., Lijnzaad P., Melsopp C., Mongin E., PettettR., Pocock M., Potter S., Rust A., Schmidt E., Searle S., SlaterG., Smith J., Spooner W., Stabenau A., Stalker J., Stupka E.,Ureta-Vidal A., Vastrik I., and Clamp M. 2002. The Ensemblgenome database project. Nucleic Acids Res. 30: 38.Ince T.A. and Scotto K.W. 1995. A conserved downstream elementdefines a new class of RNA polymerase II promoters. J.Biol. Chem. 270: 30249.Ioshikhes I.P. and Zhang M.Q. 2000. Large-scale human promotermapping using CpG islands. Nat. Genet. 26: 61.Kapranov P., Cawley S.E., Drenkow J., Bekiranov S. StrausbergR.L., Fodor S.P., and Gingeras T.R. 2003. Large-scale transcriptionalactivity in chromosomes 21 and 22. Science 296:916.Kel A.E., Kel-Margoulis O.V., Farnham P.J., Stephanie M.B.,Wingender. E., and Zhang M.Q. 2001. Computer-assistedidentification of cell cycle-related genes—New targets for
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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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Page 191 and 192: 184 GEORGES AND ANDERSSONbe common
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Page 196 and 197: Evolving Methods for the Assembly o
Page 198 and 199: ASSEMBLING LARGE GENOMES 191Figure
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Page 202 and 203: Mouse Genome Encyclopedia ProjectY.
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Page 212 and 213: DNA Sequence Assembly and Multiple
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Page 225 and 226: 218 ZHANGthe majority of these are
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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
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Page 245 and 246: 238 JOSHI-TOPE ET AL.Figure 1. The
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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
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Page 273 and 274: 266 ROE ET AL.noncoding regions. On
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Page 279 and 280: 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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