- Page 6 and 7: ForewordIn 2001, as we considered t
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- Page 13 and 14: 6 ROGERSFigure 4. Sequencing center
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- Page 21 and 22: 14 WATERSTON ET AL.have been diffic
- Page 23 and 24: 16 WATERSTON ET AL.there are unlike
- Page 25 and 26: 18 WATERSTON ET AL.Table 3. Genes w
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- Page 29 and 30: 22 WATERSTON ET AL.Pruitt K.D. and
- Page 31 and 32: 24 WILSON ET AL.Figure 3. Our proto
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- Page 49 and 50: 42 RIETHMAN ET AL.Figure 2. Models
- Page 51 and 52: 44 RIETHMAN ET AL.gions were unavai
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- Page 62 and 63: DNA Sequence Variation of Homo sapi
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HUMAN SEQUENCE VARIATION 61Figure 4
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HUMAN SEQUENCE VARIATION 63Li W.H.
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66 KWOK AND XIAOvader assay (Lyamic
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Highly Parallel SNP GenotypingJ.-B.
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SNP GENOTYPING 71tem, consisting of
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SNP GENOTYPING 73ASSAY CONVERSION R
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SNP GENOTYPING 75double-labeled con
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SNP GENOTYPING 77date the genetic b
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Structure of Linkage Disequilibrium
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STRATIFICATION OF LINKAGE DISEQUILI
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STRATIFICATION OF LINKAGE DISEQUILI
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STRATIFICATION OF LINKAGE DISEQUILI
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STRATIFICATION OF LINKAGE DISEQUILI
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Genome-wide Association of Haplotyp
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HAPLOTYPE EXPRESSION ASSOCIATIONS 9
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HAPLOTYPE EXPRESSION ASSOCIATIONS 9
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HAPLOTYPE EXPRESSION ASSOCIATIONS 9
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HAPLOTYPE EXPRESSION ASSOCIATIONS 9
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HAPLOTYPE EXPRESSION ASSOCIATIONS 9
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HAPLOTYPE EXPRESSION ASSOCIATIONS 1
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HAPLOTYPE EXPRESSION ASSOCIATIONS 1
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HAPLOTYPE EXPRESSION ASSOCIATIONS 1
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HAPLOTYPE EXPRESSION ASSOCIATIONS 1
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110 COTSAPAS ET AL.that this might
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112 COTSAPAS ET AL.Figure 5. The de
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114 COTSAPAS ET AL.peat variation a
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116 BAILEY AND EICHLERthe extremes,
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118 BAILEY AND EICHLERFigure 2. Ove
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120 BAILEY AND EICHLERand other inv
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122 BAILEY AND EICHLERknown, but it
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124 BAILEY AND EICHLERRuault M., Tr
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126 WANG, BUHLER, AND BRENTTWINSCAN
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128 WANG, BUHLER, AND BRENTMETHODSS
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130 WANG, BUHLER, AND BRENTThomas J
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132 HAMILTON ET AL.family affected
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134 HAMILTON ET AL.Figure 3. Three-
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136 HAMILTON ET AL.Figure 6. Diagra
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138 HAMILTON ET AL.upon vision (Gil
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140 HAMILTON ET AL.Wilson M.J. 2001
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142 RUDD, SCHUELER, AND WILLARDDNA
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144 RUDD, SCHUELER, AND WILLARDFigu
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146 RUDD, SCHUELER, AND WILLARDdeta
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148 RUDD, SCHUELER, AND WILLARDray
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Evolutionary Strategies of Human Pa
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EVOLUTION OF HUMAN PATHOGENS 153Fig
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EVOLUTION OF HUMAN PATHOGENS 155rol
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EVOLUTION OF HUMAN PATHOGENS 157in
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Gene Expression Profiling of Cells,
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GENE EXPRESSION PROFILING IN C. ELE
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GENE EXPRESSION PROFILING IN C. ELE
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GENE EXPRESSION PROFILING IN C. ELE
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GENE EXPRESSION PROFILING IN C. ELE
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GENE EXPRESSION PROFILING IN C. ELE
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172 GUYON ET AL.Table 1. Map Statis
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174 GUYON ET AL.only in one of the
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176 GUYON ET AL.million reads (mean
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Positional Identification of Struct
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IDENTIFYING LIVESTOCK QTN 181initia
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IDENTIFYING LIVESTOCK QTN 183tation
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IDENTIFYING LIVESTOCK QTN 185The fa
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IDENTIFYING LIVESTOCK QTN 187Farnir
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190 GIBBS AND WEINSTOCKBACATLASWGSF
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192 GIBBS AND WEINSTOCKsentation of
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194 GIBBS AND WEINSTOCKCONCLUSIONSE
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196 HAYASHIZAKIFigure 1. Strategy f
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198 HAYASHIZAKIpasses per day. Ther
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200 HAYASHIZAKIto known genes. As s
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202 HAYASHIZAKIaged small amounts o
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204 HAYASHIZAKIanalysis of more tha
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206 ZHANG AND WATERMAN(a)Repeat Rep
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208 ZHANG AND WATERMAN501234301245(
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210 ZHANG AND WATERMANing and Green
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212 ZHANG AND WATERMANACKNOWLEDGMEN
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214 BIRNEY ET AL.Table 1. General I
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Prediction, Annotation, and Analysi
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ANALYSIS OF HUMAN PROMOTERS 219Inte
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ANALYSIS OF HUMAN PROMOTERS 221Figu
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ANALYSIS OF HUMAN PROMOTERS 223nacc
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ANALYSIS OF HUMAN PROMOTERS 225E2F
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228 ASHBURNER ET AL.cally meaningfu
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230 ASHBURNER ET AL.man protein ann
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232 ASHBURNER ET AL.associations be
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234 ASHBURNER ET AL.cabulary for th
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The Genome Knowledgebase: A Resourc
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THE GENOME KNOWLEDGEBASE 239Figure
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THE GENOME KNOWLEDGEBASE 241by the
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THE GENOME KNOWLEDGEBASE 243Karp P.
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246 CHIAROMONTE ET AL.We make no pa
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248 CHIAROMONTE ET AL.Figure 1. Smo
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250 CHIAROMONTE ET AL.can use this
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252 CHIAROMONTE ET AL.However, we c
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254 CHIAROMONTE ET AL.Ihaka R. and
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256 MARGULIES ET AL.HumanMouseFuguR
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258 MARGULIES ET AL.Base Position 1
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260 MARGULIES ET AL.Figure 4. Gener
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262 MARGULIES ET AL.CONCLUSIONSWith
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Comparative Analysis of Human Chrom
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COMPARATIVE GENOMIC ANALYSIS 267aBa
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COMPARATIVE GENOMIC ANALYSIS 269µg
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COMPARATIVE GENOMIC ANALYSIS 271aHu
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COMPARATIVE GENOMIC ANALYSIS 273___
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Genome-wide Analyses Based on Compa
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WHOLE-GENOME COMPARISONS 277Figure
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WHOLE-GENOME COMPARISONS 279Table 2
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WHOLE-GENOME COMPARISONS 281models
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Comparative Genomic Tools for Explo
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COMPUTATIONAL TOOLS FOR ANALYZING G
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COMPUTATIONAL TOOLS FOR ANALYZING G
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COMPUTATIONAL TOOLS FOR ANALYZING G
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294 ROGOZIN ET AL.BeTs are combined
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296 ROGOZIN ET AL.Dm Hs Ce Sc Sp Ec
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298 ROGOZIN ET AL.Dm Ag Hs Ce Sc Sp
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300 ROGOZIN ET AL.gene lossgene gai
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Human-Mouse Comparative Genomics: S
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HUMAN-MOUSE COMPARATIVE GENOMICS 30
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HUMAN-MOUSE COMPARATIVE GENOMICS 30
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HUMAN-MOUSE COMPARATIVE GENOMICS 30
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MOUSE KNOCKOUTS ILLUSTRATE FUNCTION
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MOUSE KNOCKOUTS ILLUSTRATE FUNCTION
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318 LIAN ET AL.0 12 34 5 6 78 9 101
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320 LIAN ET AL.90 minutes and the c
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322 LIAN ET AL.Iyer V.I., Horak C.A
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324 LI ET AL.Figure 1. Array-based
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326 LI ET AL.Figure 4. Test of quan
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328 LI ET AL.Figure 5. Comparison o
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Annotation of Novel Proteins Utiliz
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FUNCTIONAL SHOTGUN SEQUENCING 333Fi
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Global Predictions and Tests of Ery
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PREDICTING GENE REGULATORY REGIONS
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PREDICTING GENE REGULATORY REGIONS
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PREDICTING GENE REGULATORY REGIONS
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PREDICTING GENE REGULATORY REGIONS
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Systems Approaches Applied to the S
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SYSTEMS BIOLOGY IN S. CEREVISIAE AN
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SYSTEMS BIOLOGY IN S. CEREVISIAE AN
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SYSTEMS BIOLOGY IN S. CEREVISIAE AN
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SYSTEMS BIOLOGY IN S. CEREVISIAE AN
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SYSTEMS BIOLOGY IN S. CEREVISIAE AN
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SYSTEMS BIOLOGY IN S. CEREVISIAE AN
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360 MERIKANGASidentification of gen
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362 MERIKANGASDay 1984; Ottman 1990
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364 MERIKANGASPrevention Research G
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366 DRAYNA ET AL.Figure 1. Distribu
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368 DRAYNA ET AL.Table 1. Summary o
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370 DRAYNA ET AL.Figure 5. Predicte
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Genomic Variation in Multigenic Tra
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GENOMIC VARIATION IN MULTIGENIC TRA
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GENOMIC VARIATION IN MULTIGENIC TRA
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GENOMIC VARIATION IN MULTIGENIC TRA
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GENOMIC VARIATION IN MULTIGENIC TRA
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384 PORTEOUS ET AL.of these devasta
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386 PORTEOUS ET AL.Figure 2. The t(
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388 PORTEOUS ET AL.Figure 3. DISC1
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390 PORTEOUS ET AL.cgi-bin/hgGatewa
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392 PORTEOUS ET AL.REFERENCESAshers
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394 PORTEOUS ET AL.Valles V., Van O
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396 GOLDSTEIN, CAVALLERI, AND AHMAD
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398 GOLDSTEIN, CAVALLERI, AND AHMAD
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400 GOLDSTEIN, CAVALLERI, AND AHMAD
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Genetics of Quantitative Variation
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HUMAN GENE EXPRESSION VARIATION 405
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HUMAN GENE EXPRESSION VARIATION 407
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410 CHIBA-FALEK AND NUSSBAUMconcent
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412 CHIBA-FALEK AND NUSSBAUMTable 4
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414 CHIBA-FALEK AND NUSSBAUMclein i
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Genomic Perspective and CancerD. BO
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GENOMIC PERSPECTIVE AND CANCER 419F
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GENOMIC PERSPECTIVE AND CANCER 421F
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GENOMIC PERSPECTIVE AND CANCER 423f
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Chromosome 21 and Down Syndrome: Th
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DOWN SYNDROME AND HUMAN CHROMOSOME
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DOWN SYNDROME AND HUMAN CHROMOSOME
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Harvesting the Genome’s Bounty: I
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INTEGRATIVE GENOMICS 433with high-d
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INTEGRATIVE GENOMICS 435497 baits3,
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INTEGRATIVE GENOMICS 437In contrast
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INTEGRATIVE GENOMICS 439Fly19,912Ye
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INTEGRATIVE GENOMICS 441IMPLICATION
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INTEGRATIVE GENOMICS 443gene intera
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446 STANKIEWICZ ET AL.Table 1. Sele
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448 STANKIEWICZ ET AL.al. 2002; Wal
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450 STANKIEWICZ ET AL.ate DNA rearr
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452 STANKIEWICZ ET AL.Figure 5. Sch
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454 STANKIEWICZ ET AL.Legius E. 200
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456 SAKAKI ET AL.determined. We pai
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458 SAKAKI ET AL.Table 2. Base Subs
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460 SAKAKI ET AL.D.R. 2003. Genomic
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462 LIPOVICH AND KINGtions that cre
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464 LIPOVICH AND KINGto-tail antise
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466 LIPOVICH AND KINGFigure 2. Perl
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468 LIPOVICH AND KINGtions. In inte
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470 LIPOVICH AND KINGOlson M.V. and
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472 WALLACE, RUIZ-PESINI, AND MISHM
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474 WALLACE, RUIZ-PESINI, AND MISHM
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476 WALLACE, RUIZ-PESINI, AND MISHM
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478 WALLACE, RUIZ-PESINI, AND MISHM
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480 CLARK ET AL.Table 1. Derivation
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482 CLARK ET AL.Table 3. Biological
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484 CLARK ET AL.be under adaptive e
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Inferring Human History: Clues from
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Y-CHROMOSOME HAPLOTYPES AND GEOGRAP
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Y-CHROMOSOME HAPLOTYPES AND GEOGRAP
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Y-CHROMOSOME HAPLOTYPES AND GEOGRAP
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496 OLSONTable 1. Talks Grouped by
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498 OLSONbacterial lineages prosper
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500 OLSONwill respond to perturbati
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502 OLSONIn short, much remained to