The Genom of Homo sapiens.pdf

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EVOLUTIONARY DISTANCE AND GENE PREDICTION 129formance. Finally, our results on CFTR agree qualitativelywith those that Thomas et al. obtained withBLASTZ (Schwartz et al. 2003), an algorithm that producesa very different type of alignment than BLASTN.The accuracy curve shown in Figure 4A reflects comparisonsat only four significantly different evolutionarydistances. The alignment curves in Figure 4B suggest thatfilling in the intermediate distances will yield an accuracycurve with a single peak at the evolutionary distance thatis optimal for informing gene modeling. If so, the peak isat a distance farther than that between mouse and rat butcloser than that between mouse and chicken. The peak atthe mouse–human comparison in Figure 4A is consistentwith a highly idealized theoretical analysis suggesting apeak somewhere in the vicinity of mouse–human, or possiblysomewhat farther out (Zhang et al. 2003). To determinethe peak location more precisely, we will need thesequences of more genomes. The most immediate possibilityfor a comparison at a distance intermediate betweenmouse–human and mouse–chicken will come from theopossum, Monodelphis domestica, a marsupial that hasbeen designated as a high-priority sequencing targetby the National Human Genome Research Institute(http://www.genome.gov/page.cfm?pageID=10002154).Although the mouse–rat divergence is much too closefor optimal annotation of mammalian genomes using asingle genome pair, preliminary data suggest that the situationmay be quite different when the target is a genomewith very short introns. For example, gene prediction inCryptococcus neoformans serotype D benefits fromalignments to serotype A. These alignments cover 100%of CDS and 87% of intron bases, excluding splice sites(A. Tenney, unpubl.). Visual inspection suggests thatwhen TWINSCAN is trained on these alignments, it predictsintrons or intergenic regions that include most unalignedregions; conversely, it rarely predicts an intronthat does not overlap an unaligned region. Because the intronsare very short (68 bp on average), the locations ofthe intron boundaries are quite constrained relative tothose of mammalian introns. Apparently, TWINSCANuses these alignments to find the general locations of introns,rather than their precise boundaries.The research presented here is a significant step towarddetermining the optimal distance for gene modeling usingpair-wise genome alignments. Looking ahead to the nextstep, we and other investigators are developing methodsthat use information from multiple genome alignmentsrather than choosing a single best alignment (Boffelli etal. 2003; Siepel and Haussler 2003). When it has beenfully developed, the multi-genome approach is expectedto yield real breakthroughs in the accuracy of genemodeling.ACKNOWLEDGMENTSWe are grateful to the centers that produced thegenome sequences used in this study. Special mention isdue to the Washington University Genome SequencingCenter for producing the chicken whole-genome shotgunsequence, the National Institutes of Health Intramural SequencingCenter for producing BAC-based sequences ofthe greater CFTR regions, and the Whitehead InstituteGenome Research Center for producing the dog wholegenomeshotgun sequence. The authors were supported inpart by grant HG-02278 from the National HumanGenome Research Institute.REFERENCESAlexandersson M., Cawley S., and Pachter L. 2003. SLAM:Cross-species gene finding and alignment with a generalizedpair hidden Markov model. Genome Res. 13: 496.Aparicio S., Chapman J., Stupka E., Putnam N., Chia J.M., DehalP., Christoffels A., Rash S., Hoon S., Smit A., GelpkeM.D., Roach J., Oh T., Ho I.Y., Wong M., Detter C., VerhoefF., Predki P., Tay A., Lucas S., Richardson P., Smith S.F.,Clark M.S., Edwards Y.J., and Doggett N., et al. 2002.Whole-genome shotgun assembly and analysis of the genomeof Fugu rubripes. Science 297: 1301.Bafna V. and Huson D.H. 2000. The conserved exon method forgene finding. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8: 3.Boffelli D., McAuliffe J., Ovcharenko D., Lewis K.D.,Ovcharenko I., Pachter L., and Rubin E.M. 2003. Phylogeneticshadowing of primate sequences to find functional regionsof the human genome. Science 299: 1391.Burge C. and Karlin S. 1997. Prediction of complete gene structuresin human genomic DNA. J. Mol. Biol. 268: 78.Flicek P., Keibler E., Hu P., Korf I., and Brent M.R. 2003.Leveraging the mouse genome for gene prediction in human:From whole-genome shotgun reads to a global synteny map.Genome Res. 13: 46.Guigó R., Dermitzakis E.T., Agarwal P., Ponting C., 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.Keibler E. and Brent M.R. 2003. Eval: A software package foranalysis of genome annotations. BMC Bioinformatics 4: 50.Kirkness E.F., Bafna V., Halpern A.L., Levy S., Remington K.,Rusch D.B., Delcher A.L., Pop M., Wang W., Fraser C.M.,and Venter J.C. 2003. The dog genome: Survey sequencingand comparative analysis. Science 301: 1898.Korf I., Flicek P., Duan D., and Brent M.R. 2001. Integrating genomichomology into gene structure prediction. Bioinformatics(suppl. 1) 17: S140.McPherson J.D., Dodson J., Krumlauf R., and Olivier P. 2002.Proposal to sequence the genome of the chicken. (http://www.wattnet.com/library/DownLoad/PD12 genome.pdf)Parra G., Agarwal P., Abril J.F., Wiehe T., Fickett J.W., andGuigó R. 2003. Comparative gene prediction in human andmouse. Genome Res. 13: 108.Pruitt K.D. and Maglott D.R. 2001. RefSeq and LocusLink:NCBI gene-centered resources. Nucleic Acids Res. 29: 137.Roest Crollius H., Jaillon O., Bernot A., Dasilva C., Bouneau L.,Fischer C., Fizames C., Wincker P., Brottier P., Quetier F.,Saurin W., and Weissenbach J. 2000. Estimate of human genenumber provided by genome-wide analysis using Tetraodonnigroviridis DNA sequence. Nat. Genet. 25: 235.Schwartz S., Kent W.J., Smit A., Zhang Z., Baertsch R., HardisonR.C., Haussler D., and Miller W. 2003. Human-mousealignments with BLASTZ. Genome Res. 13: 103.Siepel A.C. and Haussler D. 2003. Combining phylogenetic andhidden Markov models in biosequence analysis. In RECOMB2003 (ed. W. Miller et al.), p. 277. ACM Press (ACM DigitalLibrary), New York.Strausberg R.L., Feingold E.A., Grouse L.H., Derge J.G., KlausnerR.D., Collins F.S., Wagner L., Shenmen C.M., SchulerG.D., Altschul S.F., Zeeberg B., Buetow K.H., Schaefer C.F.,Bhat N.K., Hopkins R.F., Jordan H., Moore T., Max S.I.,Wang J., Hsieh F., Diatchenko L., Marusina K., Farmer A.A.,Rubin G.M., and Hong L., et al. 2002. Generation and initialanalysis of more than 15,000 full-length human and mousecDNA sequences. Proc. Natl. Acad. Sci. 99: 16899.
130 WANG, BUHLER, AND BRENTThomas J.W., Touchman J.W., Blakesley R.W., Bouffard G.G.,Beckstrom-Sternberg S.M., Margulies E.H., Blanchette M.,Siepel A.C., Thomas P.J., McDowell J.C., Maskeri B.,Hansen N.F., Schwartz M.S., Weber R.J., Kent W.J.,Karolchik D., Bruen T.C., Bevan R., Cutler D.J., SchwartzS., Elnitski L., Idol J.R., Prasad A.B., Lee-Lin S.Q., andMaduro V.V., et al. 2003. Comparative analyses of multispeciessequences from targeted genomic regions. Nature424: 788.Waterston R.H., Lindblad-Toh K., Birney E., Rogers J., AbrilJ.F., Agarwal P., Agarwala R., Ainscough R., AlexanderssonM., An P., Antonarakis S.E., Attwood J., Baertsch R., BaileyJ., Barlow K., Beck S., Berry E., Birren B., Bloom T., BorkP., Botcherby M., Bray N., Brent M.R., Brown D.G., andBrown S.D., et al. (Mouse Genome Sequencing Consortium).2002. Initial sequencing and comparative analysis of themouse genome. Nature 420: 520.Wootton J.C. and Federhen S. 1996. Analysis of compositionallybiased regions in sequence databases. Methods Enzymol.266: 554.Wu J.Q., Shteynberg D., Arumugam M., Gibbs R.A., and BrentM.R. 2004. Identification of rat genes by TWINSCAN geneprediction, RT-PCR, and direct sequencing. Genome Res. 14:665.Zhang L., Pavlovic V., Cantor C.R., and Kasif S. 2003. Humanmousegene identification by comparative evidence integrationand evolutionary analysis. Genome Res. 13: 1190.WEBSITE REFERENCESMammalian Gene Collection (MGC)http://mgc.nci.nih.gov/NCBI LocusLinkhttp://www.ncbi.nlm.nih.gov/LocusLink/RSstatistics.htmlNCBI trace archiveftp://ftp.ncbi.nih.gov/pub/TraceDB/NHGRI’s sequencing priority listhttp://www.genome.gov/page.cfm?pageID=10002154NISC’s annotation of the greater CFTR region of mousehttp://www.nisc.nih.gov/data/20020612_Target1_0051/mouse_T1.fastaAnnotation of the CFTR regionhttp://www.nisc.gov/data/20020612_Target1_0051/mouse_T1.annot.ffNISC’s map of orthologous vertebrate BAC cloneshttp://www.nisc.nih.gov/projects/zooseq/pubmap/PubM.cgiUCSC’s repeat masked mouse genome sequencehttp://genome.ucsc.edu/goldenPath/mmFeb2003/chromosomes/chr*.fa.zipRepeatMasker software and documentationhttp://ftp.genome.Washington.edu/RM/RepeatMasker.htmlEval software and documentationhttp://genes.cse.wustl.edu/eval/TWINSCAN software, documentation, and web serverhttp://genes.cse.wustl.edu
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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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Page 116 and 117: Genetic Variation and the Control o
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Page 122 and 123: Genome-wide Detection and Analysis
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Page 140 and 141: EVOLUTION OF ZNF GENES 133Figure 2.
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Page 144 and 145: EVOLUTION OF ZNF GENES 137get gene,
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Page 150 and 151: CENTROMERE ANNOTATION 143THE CENTRO
Page 152 and 153: CENTROMERE ANNOTATION 145Figure 4.
Page 154 and 155: CENTROMERE ANNOTATION 147CONCLUSION
Page 156: CENTROMERE ANNOTATION 149Schueler M
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Page 165 and 166: 158 PARKHILL AND THOMSONYersinia ad
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Page 169 and 170: 162 MCKAY ET AL.new comparative too
Page 171 and 172: 164 MCKAY ET AL.rich. Based on a th
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180 GEORGES AND ANDERSSON5. There i
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182 GEORGES AND ANDERSSONplied to r
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184 GEORGES AND ANDERSSONbe common
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186 GEORGES AND ANDERSSONin humans
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Evolving Methods for the Assembly o
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ASSEMBLING LARGE GENOMES 191Figure
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ASSEMBLING LARGE GENOMES 193tant ad
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Mouse Genome Encyclopedia ProjectY.
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MOUSE GENOME ENCYCLOPEDIA PROJECT 1
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DNA Sequence Assembly and Multiple
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EULERIAN ASSEMBLY AND MULTIPLE ALIG
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Ensembl: A Genome InfrastructureE.
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ENSEMBL 215projects often submit th
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218 ZHANGthe majority of these are
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220 ZHANGFigure 2. Demonstration of
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222 ZHANG(G.X. Chen et al., in prep
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224 ZHANGWe are waiting for experim
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Ontologies for Biologists: A Commun
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ONTOLOGIES FOR BIOLOGISTS 229al. 20
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ONTOLOGIES FOR BIOLOGISTS 231TOPIC
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ONTOLOGIES FOR BIOLOGISTS 233a.b.Fi
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ONTOLOGIES FOR BIOLOGISTS 2352003.
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238 JOSHI-TOPE ET AL.Figure 1. The
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240 JOSHI-TOPE ET AL.state of knowl
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242 JOSHI-TOPE ET AL.and co-immunop
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The Share of Human Genomic DNA unde
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DNA UNDER SELECTION FROM HUMAN-MOUS
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Detecting Highly Conserved Regions
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DETECTING MULTISPECIES CONSERVED SE
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266 ROE ET AL.noncoding regions. On
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268 ROE ET AL.a48 hpf embryos in Mi
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270 ROE ET AL.aNovel gene KIAA0819[
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272 ROE ET AL.aMouseRatAP00354.2 Hu
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274 ROE ET AL.Tautz D. and Pfeifle
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276 JAILLON ET AL.Detection of Evol
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278 JAILLON ET AL.Table 1. Distribu
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280 JAILLON ET AL.Table 3. Distribu
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282 JAILLON ET AL.ecotig is a resul
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284 OVCHARENKO AND LOOTSdivergent r
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286 OVCHARENKO AND LOOTSmodulation
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288 OVCHARENKO AND LOOTSsequencing
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290 OVCHARENKO AND LOOTSments of cl
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Evolution of Eukaryotic Gene Repert
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EVOLUTION OF EUKARYOTIC GENES AND I
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304 PENNACCHIO, BAROUKH, AND RUBINA
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306 PENNACCHIO, BAROUKH, AND RUBINh
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308 PENNACCHIO, BAROUKH, AND RUBINA
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High-Throughput Mouse Knockouts Pro
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314 FRIDDLE ET AL.screen to lines o
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Identification of Novel Functional
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100 bp ladder68G1168G1168H1168H6100
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FUNCTIONAL ELEMENTS IN HUMAN DNA 32
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High-resolution Human Genome Scanni
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HUMAN GENOME SCANNING 325false-posi
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HUMAN GENOME SCANNING 327affecting
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HUMAN GENOME SCANNING 329methods fo
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332 MALEK ET AL.Figure 1. The bacte
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334 MALEK ET AL.J., Vincent S., and
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336 HARDISON ET AL.reflect blocks o
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338 HARDISON ET AL.plain the region
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340 HARDISON ET AL.CALIBRATION OF T
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342 HARDISON ET AL.PositionRP2.3noE
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344 HARDISON ET AL.cific chromosoma
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346 WESTON ET AL.these differences
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348 WESTON ET AL.els controlled by
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350 WESTON ET AL.ures prominently i
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352 WESTON ET AL.nal and Bop, which
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354 WESTON ET AL.ablp 1466 bopbcrtB
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356 WESTON ET AL.like fold (Fig. 6)
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Implications of Genomics for Public
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GENETIC EPIDEMIOLOGY 361lytic epide
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GENETIC EPIDEMIOLOGY 363curate risk
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A Model System for Identifying Gene
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PTC TASTE GENETICS 367Figure 2. Hap
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PTC TASTE GENETICS 369Table 2. Hapl
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PTC TASTE GENETICS 371the emergence
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374 MCCALLION ET AL.Figure 1. Schem
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376 MCCALLION ET AL.lier (Carrasqui
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378 MCCALLION ET AL.Table 3. HSCR A
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380 MCCALLION ET AL.Figure 3. Trans
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Genetics of Schizophrenia and Bipol
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SCHIZOPHRENIA AND BIPOLAR AFFECTIVE
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The Genetics of Common Diseases: 10
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GENETICS OF COMMON DISEASES 397with
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GENETICS OF COMMON DISEASES 399SELE
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GENETICS OF COMMON DISEASES 401F.,
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404 CHEUNG ET AL.netic analysis. Ex
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406 CHEUNG ET AL.Figure 3. The expr
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Regulation of α-Synuclein Expressi
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α-SYNUCLEIN EXPRESSION AND PD 411T
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1. The levels of α-synuclein prote
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α-SYNUCLEIN EXPRESSION AND PD 415g
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418 BOTSTEINFigure 1. (A) Blectron
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420 BOTSTEINFigure 3. Cluster diagr
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422 BOTSTEINFigure 6. Kaplan-Meier
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424 BOTSTEINGarber M.E., Troyanskay
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426 ANTONARAKIS ET AL.1316192225283
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428 ANTONARAKIS ET AL.Figure 5. Sam
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430 ANTONARAKIS ET AL.POPULATION VA
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432 JORGENSEN ET AL.tive small mole
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434 JORGENSEN ET AL.FLAG-tagged pro
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436 JORGENSEN ET AL.visualization t
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438 JORGENSEN ET AL.AArp2/3 Complex
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Pathway40S440 JORGENSEN ET AL.ANutr
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442 JORGENSEN ET AL.Giaever G., Chu
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Genomic Disorders: Genome Architect
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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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