The Genom of Homo sapiens.pdf

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COMPUTATIONAL TOOLS FOR ANALYZING GENOMES 285ABCDEFigure 1. HoxB cluster viewed in the ECR Browser. To start using the ECR Browser, it is possible to begin by searching for a genename or a specific location within the human genome. For example, to locate the HoxB cluster, the user should input “hoxb” in thesearch box and click on the submit button (A). The database will be searched and it will generate a list with all the regions that includeHoxB-related genes (B). Since all the HoxB genes are adjacent to each other, any gene can be selected and it will direct to alignmentson human chromosome 17. The window size can be adjusted to include all the HoxB genes present in this cluster (C), and the human/mouseand human/Fugu fish pair-wise alignments can be visualized as conservation plots (D). Annotated exons are colored inblue, untranslated regions in yellow, repetitive elements in green, intronic noncoding elements in pink, and intergenic noncoding elementsin red (D,E). By selecting and tabs, users can cross-reference the information displayed inthe ECR Browser with the information available in the UCSC database, and can download sequence and annotation files, or customizealignment plots using the zPicture option (data not shown). Any sequence can be compared to the human, mouse, or Fugu genomes(upload sequences as FASTA files; or NCBI accession numbers). When a cow HoxB contig (AC129959.6) is compared and alignedto the human genome, the ECR Browser allows the visualization of the human/cow pair-wise alignment as an independent track (labeledby a question mark) (E).
286 OVCHARENKO AND LOOTSmodulation of gene expression is achieved through thecomplex interaction of regulatory proteins (trans-factors)with specific DNA regions (cis-acting regulatory sequences)(Krivan and Wasserman 2001). Intensive experimentalefforts over several decades have identifiednumerous regulatory proteins, transcription factors (TF),and their DNA-binding specificities. The sequence-specificDNA-binding activity of TFs is central to transcriptionalregulation and regulatory networks. For most of theknown TFs, the DNA-binding sequence motifs werefound to be short (6–12 bp) and highly degenerate, andsuch specificities are cataloged in the TRANSFACdatabases (http://www.biobase.de/) (Wingender et al.2001; Matys et al. 2003). Pattern-recognition programs(MATCH or MatInspector) (Quandt et al. 1995) use thisdatabase to carry out the reverse-engineering task of predictingsignificant motif matches in DNA sequences,which could serve as an in silico strategy for detectingtranscription factor binding sites (TFBSs). Due to theirhighly degenerate nature, TF motifs occur very frequentlyin short genomic intervals, and only a very smallfraction of the predicted sites are biologically significant.This fact limits the use of TFBS databases for sequencebaseddiscovery of transcriptional regulatory elements(Fickett and Wasserman 2000).Multispecies comparative sequence analysis or phylogeneticfootprinting has been suggested as a strategy tocounter the large numbers of TFBS false positives derivedfrom the analysis of a single sequence (Gumucio et al.1996; Duret and Bucher 1997; Levy et al. 2001). In general,it is believed that TFBSs are the building blocks ofgene regulation. Several studies have carefully shown thattranscriptional regulatory elements are evolutionarily conserved,supporting the use of genomic comparisons for thede novo discovery of gene regulatory elements (Hardisonet al. 1997; Oeltjen et al. 1997; Loots et al. 2000). In addition,in complex organisms, gene expression results fromthe cooperative action of many different proteins simultaneouslyrequired to cooperatively activate and modulategene expression (Berman et al. 2002). Therefore, a potentialavenue for improving the discovery of functional regulatoryelements is to identify multiple TFBSs that arespecifically clustered together (Kel et al. 1999; Zhu et al.2002). This strategy has been implemented successfully inthe analysis of regulatory regions involved in muscle(Wasserman and Fickett 1998) and liver-specific gene expression(Krivan and Wasserman 2001).To facilitate the efficient and accurate prediction of biologicallyfunctional regulatory sequences present in largegenomic intervals, we have developed a computationaltool, Regulatory VISTA or rVISTA (http://rvista.dcode.org/) that enriches for functional TFBSs using evolutionaryconservation (Loots et al. 2002). The rVISTAtool combines TFBS motif recognition, orthologous sequencealignments, and TFBS cluster analysis to overcomesome of the limitations associated with TFBS predictionsof sequences derived from a single organism. Theanalysis proceeds in four steps: (1) identification of TFBSmatches in the individual sequences, (2) identification oflocally aligned noncoding TFBSs, (3) calculation of localconservation extending upstream and downstream fromeach orthologous TFBS, and (4) visualization of individualor clustered noncoding TFBSs. Alignments generatedby the zPicture (http:zpicture.dcode. org/) program can beprocessed by rVISTA by following the rVISTA link providedin the results page. The user can also submit alignmentfiles generated by PipMaker along with the correspondingsequence annotations (optional) to identifyconserved TFBS matches present only in noncoding genomicintervals. Pre-computed matrices imported fromthe TRANSFAC database or user-defined consensus sequencescan be used to identify TFBS motifs in the inputsequences. The alignment and annotation files are usednext to identify all the aligned TFBSs present in noncodingDNA and to calculate the degree of DNA conservationencompassing each TFBS. rVISTA calculates the maximumDNA conservation surrounding each aligned bindingsite in a dynamically shifting window, 20 bp in length,and filters out the sites present in regions that are less than80% conserved (Fig. 2) (Loots et al. 2002).The data generated by rVISTA are compiled into twotypes of outputs: (1) static data files and (2) a dynamicWeb-interactive graphical user interface that maps TFBSon top of the conservation plot. The static text files includedata tables with detailed statistics for all alignedand conserved TFBSs, alignment files depicting the textfor each TFBS match in a different color, and files withthe numerical position of each TFBS within the referencesequence. The visualization module allows the user tocustomize the data and graphically visualize TFBS togetherwith the alignment conservation plot. Since regulatoryregions in higher eukaryotes are represented byconglomerates of multiple TFBSs that act in concordanceto directly modulate the expression patterns of the linkedgenes (Pilpel et al. 2001), rVISTA calculates the distancebetween all neighboring TFBSs and allows the user toperform customized clustering of individual or multipleunique transcription factors. One clustering module allowsthe user to selectively cluster two or more sites ofthe same TF present in regions of user-defined lengths,and a second clustering module allows the user to identifygroups of multiple sites specific for different TFs, to predictDNA regions with unique regulatory signatures (Fig.2) (Loots et al. 2002).Recently, the ECR Browser has included an rVISTAportal that takes advantage of the available precomputedwhole-genome pair-wise alignments, eliminating theneed to create an alignment file prior to TFBS analysiswhile using the rVISTA tool. Users now have the optionto browse the human genome and to perform rVISTAanalysis on individual highly conserved noncoding elementsby using the button, or on long genomicintervals containing several blocks of conservationby using the link. rVISTA analysis canthen be conducted on any available pair-wise alignmentsby pushing the button. From this point on,TFBS analysis proceeds as described in Figure 2B–E, andis processed by the rVISTA software.Annotating the noncoding portion of the humangenome still remains one of the greatest challenges post-
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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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Page 252 and 253: The Share of Human Genomic DNA unde
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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 277 and 278: 270 ROE ET AL.aNovel gene KIAA0819[
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Page 283 and 284: 276 JAILLON ET AL.Detection of Evol
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Page 291: 284 OVCHARENKO AND LOOTSdivergent r
Page 295 and 296: 288 OVCHARENKO AND LOOTSsequencing
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Page 302 and 303: EVOLUTION OF EUKARYOTIC GENES AND I
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Page 318: High-Throughput Mouse Knockouts Pro
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Page 324 and 325: Identification of Novel Functional
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Page 330 and 331: High-resolution Human Genome Scanni
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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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