The Genom of Homo sapiens.pdf

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DETECTING MULTISPECIES CONSERVED SEQUENCES 259ments with the human sequence than that seen with theclassic placental mammals, but notably more than thatseen with the fish species.The findings highlighted in Figure 3 illustrate how simplepair-wise alignments of vertebrate sequences, whichreveal the chaotic-appearing patterns of sequence conservation,do not readily uncover the small fraction of themammalian genome that is likely to serve a functionalrole. We have thus sought to use our multispecies sequencedata to develop approaches for identifying thoseregions that are highly conserved across multiple species;we call such sequences MCSs—for Multispecies ConservedSequences. In general, our methods involve analyzingthe multispecies sequences as a group, in a fashionthat allows the “signal” of actively conserved sequence tobe discriminated from the “noise” inherent to pair-wisealignments.One strategy we have developed for detecting MCSs(Margulies et al. 2003) is illustrated in Figure 4. In brief,this involves calculating a “conservation score” for eachbase across a sequenced region. Starting with human-referencedmultisequence alignments (Fig. 4A) (e.g., derivedfrom MultiPipMaker; Schwartz et al. 2003b), overlapping25-base windows are used to calculate thecumulative binomial probability of encountering the observednumber of base identities, given the neutral substitutionrate calculated from fourfold degenerate sites(Margulies et al. 2003). The latter are the third positionsof codons for which any base codes for the same aminoacid; this is calculated separately for each targeted genomicregion (e.g., shown in Fig. 4B for the 7q31.3 region).Thus, conserved sequences from more divergedspecies (e.g., platypus) make a larger relative contributionto the conservation score than do those from less divergedspecies (e.g., cat) (Fig. 4B). In calculating a finalconservation score, and to normalize for any phylogeneticbiases (e.g., the use of two carnivores but only onemarsupial species), the data are then “phylogeneticallyaveraged” by first averaging the conservation scoreswithin each represented clade and then averaging acrossall the clades.In light of the estimate that ~5% of the mammaliangenome is actively conserved (Waterston et al. 2002;Roskin et al. 2003), we typically use a conservation scorethreshold such that the top 5% most conserved bases fallwithin MCSs (shaded in red in Fig. 4C). To monitor theeffectiveness of the established thresholds at identifyingthe most conserved sequence, the distributions of codingbases (a “marker” for actively conserved sequence) versusnoncoding bases (most of which are presumably notactively conserved) are examined. Representative distributionsare schematically illustrated in Figure 4C. Notethat the noncoding (purple) and coding (yellow) bases reflect~99% and ~1% of the total sequence, respectively.Using a threshold that identifies the top 5% most conservedsequence typically results in the inclusion of>98% of coding bases within MCSs, along with a verysmall fraction of the noncoding bases.Using the above methods, analyses of the multispeciessequences identified 1572 and 4520 MCSs in the 7q31.3and 7q11.23 regions highlighted in Figure 2, respectively.Overall, the MCSs average 60–70 bp in size; however,those overlapping coding sequence tend to be larger thanthose in noncoding sequence (Margulies et al. 2003). Bydefinition, the MCSs together comprise 5% of the totalsequence, but there are notable differences in the compositionof the MCSs relative to known annotated featuresin each region. For example, the percent of MCS bases residingwithin apparently noncoding sequence is higherfor the 7q11.23 region than for the 7q31.3 region (Fig. 2).Remarkably, for both regions, this constitutes ≥70% ofthe MCS bases, providing a sizable amount of highly conservedsequence that might reflect functional noncodingelements. Finally, only a small fraction of MCS bases(3.6% for both regions) reside within ancestral repeats(ARs; relics of transposons inserted prior to the eutherianradiation and presumed not to be actively conserved);thus, our MCS-detection scheme detects very little sequencethat is thought to be neutrally evolving.More careful inspection reveals important insightsabout the locations of MCSs relative to known genomicfeatures. This is nicely illustrated in the adapted view ofthe UCSC Genome Browser in Figure 3. Note the tracks(near the top) displaying the calculated conservationscores and corresponding positions of MCSs (which arefurther highlighted by vertical yellow lines) across the depictedregion. As expected, an MCS overlaps an exonpresent in the RefSeq record for the CFTR gene; interestingly,another MCS overlaps a rarely expressed, alternatelyspliced CFTR exon (represented by GenBankrecords X73053 and S64699) (Melo et al. 1993; Will etal. 1993). Other MCSs overlap two previously detectedDNase I hypersensitive sites (Smith et al. 2000), whichare presumed to be associated with regions of transcriptionfactor binding. The remaining MCSs overlap apparentlynoncoding sequences, for which no functional significancehas yet been established; however, the highlyconserved nature of these sequences makes them attractivecandidates for further study. Finally, it is striking thatmany of the MCSs reside at the few genomic locationswhere there are alignments between the human sequenceand marsupial (opossum and dunnart) or monotreme(platypus) sequences (Fig. 3); these findings illustratewhy sequences from these species, which occupy uniquepositions on the evolutionary tree (Graves and Westerman2002; Wakefield and Graves 2003), will likely be avaluable resource for identifying the most conserved sequencesin the human genome.PERFORMANCE OF DIFFERENT SPECIESThe relative contribution of different species’ sequencestoward the identification of MCSs is of interest,in part because such information might help guide decisionsabout future genome-wide sequencing projects. Wethus investigated the effectiveness of various species’ sequences,both individually and in groups, at detectingMCSs.For this analysis, we systematically recomputed theconservation scores using all possible subsets of species,and then assessed the ability of each subset to detect a referenceset of MCSs (those detected using all species’ se-
260 MARGULIES ET AL.Figure 4. General strategy for detecting MCSs. (A) The amount of sequence conservation (with the human reference sequence) is calculatedfor each overlapping 25-base window of a multispecies sequence alignment. (B) The contribution of each species’ sequenceto the conservation score is weighted based on the neutral substitution rate between that species and human (indicated are the calculatedsimilarities at neutral positions, with the neutral substitution rates being 1 minus these values); in this fashion, the ultimate contributionof an alignment to the resulting conservation score depends on the difference between the percent identity of that alignmentand the corresponding neutral rate for that species (as illustrated for cat and platypus). (C) In general, coding regions (yellow) havehigher conservation scores than noncoding regions (purple). A conservation score threshold (vertical arrow) is selected such that 5%of the total sequence resides within the detected MCSs (red); importantly, this results in virtually all coding sequence and a very smallamount of noncoding sequence falling within MCSs. For additional details, see Margulies et al. (2003).
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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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Page 231 and 232: 224 ZHANGWe are waiting for experim
Page 234 and 235: Ontologies for Biologists: A Commun
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Page 245 and 246: 238 JOSHI-TOPE ET AL.Figure 1. The
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Page 252 and 253: The Share of Human Genomic DNA unde
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Page 273 and 274: 266 ROE ET AL.noncoding regions. On
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Page 283 and 284: 276 JAILLON ET AL.Detection of Evol
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Page 295 and 296: 288 OVCHARENKO AND LOOTSsequencing
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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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