The Genom of Homo sapiens.pdf

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TRANSCRIPTIONAL UNITS AND GENE PAIRS 465Table 2. Clustering of Genes and TUs Involved in Unconventional Gene Pairs at 5q31Interval [DAMS-TRP7] (TRP7-KLHL3) [KLHL3-SIL1] (SIL1-25P15.TU1) [25P15.TU1-HARSL]Size (kb) 225 1260 1580 1200 300Transcript models 6 6 32 42 16Transcript models per 100 kb 2.7 0.5 2.0 3.5 5.0Transcript models involved in UGPs 4 0 18 4 11% of transcript models in UGPs 67% 0% 56% 10% 69%The term “transcript models” refers to both genes and TUs.domly distributed along the genomic sequence in a waythat is independent from gene density. Fourth, relative togenes, TUs are enriched in expressed repetitive elements,including primate-specific Alu and Mer1 repeats.An analysis of UGP distribution in the 5q31 region exclusiveof the PCDH clusters demonstrated that UGPscluster in well-defined genomic intervals (Table 2). Theintervals differ in the proportion of expressed featuresparticipating in UGPs, which represent the majority offeatures in some intervals and a very small minority inothers. The UGP-enriched genomic intervals containmultiple types of genomic complexity. For example, theinterval containing 38I10.TU1 also contained four consecutivefeatures, each of which was oriented opposite toits neighbors and which formed three antisense pairs (Fig.1B), a rare arrangement analogous to, but even morecomplex than, that seen in the human Surfeit locus(Duhig et al. 1998).Table 3 indicates differences between known genesand novel TUs in ~4.5 Mb of 5q31. The biological realityof TUs is suggested by canonical transcript processingand the presence of multiple ESTs. Nonetheless, TUs representa radically different fraction of the transcriptome.For example, BLASTN and TBLASTX analysis of allTUs mapping to this 5q31 region, performed against theNT, EST, GSS, and HTGS databases, found nonhumanhomology for most of the genes but for less than half ofthe TUs.LARGE-SCALE VERIFICATION OF TRENDSFROM CHR. 5q31 ON CHR. 22Perl-based High-throughput TU Discoveryand UGP Analysis PipelineWe hypothesized that these four trends are global andnot region-specific. Therefore, we utilized 5q31 as atraining set for chromosome-scale analysis of TUs andUGPs. We codified criteria developed for the 5q31 annotationinto a three-stage Perl-based high-throughput automatedannotation pipeline (Fig. 2). We modified thebioperl.org open-source BLAST parsers (Stajich et al.2002) to make them aware of the transcriptional orientationof cDNAs and ESTs matching genomic sequences.The first stage utilized these parsers to analyze all cDNAand EST BLAST matches against the query genomic sequenceand determined which nongenic EST matcheswere TU-worthy (i.e., completely and precisely satisfiedour operational definition of a TU). Only primary ESTand cDNA evidence was used. We did not use third-partyannotations or any curated reference transcripts bearingNM and XM designations. In the second stage, all cDNAand TU-worthy EST matches were subjected to BLASTNagainst the entire human genomic sequence in the NR andHTGS databases. Matches with homologies to genomicregions other than the region in which they were originallyidentified, and with equal or higher BLAST scoresassociated with homologies to those other regions, wereautomatically eliminated due to their putatively segmentallyduplicated or pseudogenic nature. The third stageautomatically compiled complete exon–intron structuresfor every gene and TU, quoting exact coordinates of everyelement of the structure on the genomic sequence, accessionnumbers of cDNAs or ESTs supporting eachexon of each gene and TU, and the extent of apparent involvementof the gene or TU in UGPs in a table suitablefor manual curation. We selected human chr. 22 for chromosome-scalevalidation of 5q31 trends because chr. 22is small and thoroughly annotated, facilitating comparisonswith other algorithms (Collins et al. 2003) and representativeof other chromosomes in terms of segmentalduplications (Bailey et al. 2002), low-copy repeats (Mc-Dermid and Morrow 2002), and gene family expansions(Coggan et al. 1998; Jarmuz et al. 2002).Table 3. Differences between Known Genes and Novel TUs at 5q31P-value fordifference ofKnown genes Novel TUs genes and TUsN 54 47With homology to nonhuman DNA 52 17 0.0001Length (amino acid) of longest sense-strand ORF 496 ± 47.3 64 ± 4.4 0.0001% of reference transcript in expressed repeats 5.3 ± 1.6 21.4 ± 3.8 0.0011ESTs per gene or TU 201 ± 26.7 6 ± 1.0 0.0001Standard errors (calculated by SPSS v10, GLM parameter estimates module) are given after ±.
466 LIPOVICH AND KINGFigure 2. Perl-based, high-throughput gene verification, TUdiscovery, and UGP identification pipeline. Following stage 3(CLUSTER) output, manual curation was performed.Automated Identification of Known Genesand Novel TUs on Chr. 22Our algorithm identified 1012 nonredundant transcriptmodels on chr. 22. Of these, 495 (49%) representedknown genes and 517 (51%) represented novel TUs supportedsolely by ESTs. This result was consistent with therelative proportions of genes and TUs at 5q31, as well aswith recent findings indicating that, due to large numbersof TUs, the total number of expressed features comprisinga mammalian transcriptome is likely to be more thantwice the number of coding genes (Carninci et al. 2003).We automatically excluded transcripts homologous toimmunoglobulin λ gene segments, because existing annotationsgenerally group them into a special categoryseparate from the rest of expressed features on chr. 22(Collins et al. 2003).The sensitivity and specificity of our approach were assessedusing the most current Sanger Centre chr. 22 annotation(Collins et al. 2003). Of the 577 genes identifiedby the Sanger annotation, 469 were found by our algorithm,a sensitivity of 81%. The discrepancy was due inpart to the fact that our definition of a known gene wasbased solely on the presence of a full-length cDNA inGenbank, and did not include genes based solely onORFs or ab initio exon prediction. Of the 108 Sangergenes missed by our algorithm, the majority were eithertranscriptionally silent, segmentally duplicated paralogouscopies of genes we identified, or completely devoidof sense-strand cDNA and EST support. Of the 234 featuresidentified by the Sanger analysis as pseudogenes,206 were missing from the gene and TU sets created byour algorithm, a specificity of 88%. The remaining 28were transcribed and had full-length cDNA or EST-onlysupport, therefore fitting our definition of genes and TUs,respectively.Characterization of UGPs on Chr. 22Of the 1012 transcript models on chr. 22, 209 (21%)participated in UGPs. 77 cis-antisense pairs and 42 putativebidirectional promoters were found.Of the 77 antisense pairs, 23 were tail-to-tail and 13head-to-head, roughly consistent with the proportion at5q31 outside of the PCDH clusters (7 and 4, respectively)and with published evidence that in mammals tail-to-tailgene overlaps are more common than head-to-head overlaps(Edgar 2003). Surprisingly, the remaining 41 pairsdid not fit either category (this was the case for only 2pairs in the non-PCDH part of our 5.5-Mb 5q31 region),which argues for a substantial diversity and complexity ofgene and TU structures participating in antisense overlaps.Of the 77 pairs, 36 were gene–gene, 38 weregene–TU, and 3 were TU–TU. Hence, a gene-only approachto chr. 22 annotation would miss more than half ofthe cis-antisense pairs. The 77 pairs accounted for only145 transcript models rather than the expected 154, because8 models participated in cis-antisense overlaps withmultiple other models.We addressed whether any of the 77 pairs had potentialfor hybridization of sense and antisense transcripts invivo due to the expression of both members of the pair inthe same tissue or cell type. Complete lists of cDNAs andTU-worthy ESTs for every pair were obtained by BLASTand manual curation, and were examined for commonalitiesin expression profiles. ESTs from pooled libraries ortotal fetus were eliminated, since their precise origin wasunknown. Normal tissues were considered as differentfrom corresponding tumors; e.g., a gene–TU pair inwhich the gene was expressed only in normal brain butthe TU was expressed only in brain tumors would becharacterized as lacking any overlap in expression profilesof the two. For 35 of the 77 antisense pairs (45%),EST evidence suggested expression of both members ofthe pair in the same tissue or cell type. In 19 of these 35,genomic organization of the locus was conserved betweenhuman and mouse. However, in the other 16, oneor both members of each human pair lacked orthologs andpositional equivalents in mouse (Table 4). Examples includethe acrosin precursor gene, whose head-to-head antisenseTU in humans has no mouse equivalent, and theCHK2/BC000004 head-to-head antisense pair, the orthologsof whose members in the mouse are in a head-toheadorientation but do not overlap. Of the 16 cases of human–mousedifferences in antisense-containing loci, 5were characterized by the expression of both members ofthe antisense pairs in human brain, raising the intriguingpossibility that some cis-regulatory effects on gene expressionin human brain are lineage-specific and are notuniversally conserved in mammals.Of the 42 putative bidirectional promoters, 34 (79%)occurred at CpG islands, confirming existing reports ofdivergent transcription initiation at mammalian CpG islands(Adachi and Lieber 2002). Of the 42 bidirectionallypromoted pairs, 21 were gene–gene, 18 were gene–TU,and 3 were TU–TU. Therefore, similar to the case withcis-antisense, a gene-only annotation would miss approximatelyhalf of the putative bidirectionally promoted transcriptmodel pairs.Chromosomewide, 20 transcript models participated inboth cis-antisense and putative promoter-sharing pairs.This is significantly more than expected under the nullhypothesis that involvement in the two types of UGPs is
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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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Page 468 and 469: Novel Transcriptional Units and Unc
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Page 484 and 485: mtDNA VARIATION 477Figure 8. Temper
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Page 495 and 496: 488 UNDERHILLorigin episodes, each
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Page 504 and 505: NEW QUANTITATIVE BIOLOGY 497alone.
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