The Genom of Homo sapiens.pdf

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DNA Sequence Assembly and Multiple Sequence Alignmentby an Eulerian Path ApproachY. ZHANG* AND M.S.WATERMAN†Departments of *Mathematics and †Biological Sciences, University of Southern California,Los Angeles, California 90089DNA SEQUENCE ASSEMBLYAssembly of short DNA fragments (500–1000 bp) generatedby shotgun sequencing is a widely used techniquefor sequencing large genomes, including the humangenome. The most popular framework of DNA fragmentassembly algorithms in the past 25 years is the “overlaplayout-consensus”approach. All high-quality DNA fragmentsare first compared to each other for possible overlaps,then a layout is made by arranging all DNAfragments into relative positions and orientations accordingto the overlap information, and finally, a multiplealignment is computed to obtain a consensus sequence thatwill be used as the genomic sequence. The main difficultywith this framework, in addition to the computation required,comes from the fact that genomic sequences alwayscontain a large amount of repeat regions accumulatedalong their evolutionary history. In particular, repeatsthat are longer than the fragment length and have >98%identity are hard to distinguish from true overlaps, andhence finding a correct path in the layout step is difficult.Surprisingly, a 15-year-old computational model fromDNA arrays provides the basis for a novel approach to assembly.Sequencing by hybridization (SBH) is conceptuallyanalogous to DNA fragment assembly by regardingeach DNA fragment as a k-tuple. Idury and Waterman(1995) mimicked SBH procedure by breaking each DNAfragment of length n into n–k+1 overlapping k-tuples andhybridized all k-tuples in silicon such that a DNA fragmentassembly problem is mapped into a SBH problem.A de Bruijn graph is constructed in their SBH approachwhere each edge represents a k-tuple from fragments; twoedges share a common vertex if they share a common(k–1)-tuple; identical k-tuples share the same edge. Repeatsand sequencing errors make the simple de Bruijngraph extremely tangled, and the solution of DNA fragmentassembly from such a complex graph remains challenging.Pevzner et al. (2001b) took the SBH idea andprovided some additional features. Instead of building anoverlap graph in the traditional “overlap-layout-consensus”framework, the Eulerian path approach builds a deBruijn graph that represents all fragments and their relationshipsin a much simpler way. In addition, the difficultyresulting from the repeats and sequencing errors oftenis much easier to conquer in the de Bruijn graphstructure.Assume a DNA sequence consists of four unique subsequencesthat are separated by one triple repeat, asshown in Figure 1a. The traditional overlap layout consensusapproach builds an overlap graph by regarding everyfragment as a vertex, and two vertices are connectedif two corresponding fragments overlap. Figure 1b showsthe overlap graph in our example. The assembly problemis thus finding a path in the overlap graph that visits everyvertex exactly once, a Hamilton path problem that iswell known NP-complete. On the contrary, the Eulerianpath approach breaks all fragments into k-tuples andbuilds a de Bruijn graph as described above (Fig. 1c). Aconceptually ideal de Bruijn graph is shown in Figure 1d,a much simpler representation of repeats than the overlapgraph. The most important advantage of this representationis that the assembly problem is now finding a paththat visits every edge exactly once, an Eulerian path problemthat has linear-time solutions (Fleischner 1990).The implementation of the Eulerian path approach toDNA fragment assembly problems is called EULER.Traditional algorithms postpone the error correction untilthe last consensus step, whereas EULER applies errorcorrection at the beginning of assembly (this is the innovationof Pevzner et al. 2001b). Without knowing the finishedgenomic sequence or even the layout of fragments,error correction is still possible by approximating the k-tuple spectrum, and the result is usually accurate enough.After error correction, EULER constructs a de Bruijngraph and stores the fragment information in correspondingedges. This fragment information is the fundamentaldifference between EULER and SBH where such informationis unavailable. EULER’s superpath idea can successfullysolve many repeats by using fragment information.Finally, EULER outputs an Eulerian path from thede Bruijn graph that represents the finished genomic sequence.Error CorrectionSequencing errors make the de Bruijn graph a tangle oferroneous edges, thus very difficult to solve. EULER’serror correction procedure reduces 97% of errors from theoriginal DNA fragments and makes the data almost errorfree.The idea is based on an approximation to the spectrumof real genomic sequence, i.e., to find a collection ofk-tuples all of which are from the genomic sequence insteadof sequencing errors. With sequencing errors, manyk-tuples are erroneous and many “true” k-tuples are missing.EULER calls a k-tuple solid if it appears in more thanCold Spring Harbor Symposia on Quantitative Biology, Volume LXVIII. © 2003 Cold Spring Harbor Laboratory Press 0-87969-709-1/04. 205
206 ZHANG AND WATERMAN(a)Repeat Repeat Repeat(b)(c)(d)Figure 1. (a) DNA sequence with a triple repeat R; (b) the overlap graph; (c) construction of the de Bruijn graph by gluing repeats;(d) de Bruijn graph. (Reprinted, with permission, from Pevzner et al. 2001b.)M fragments, otherwise it is a weak k-tuple. The errorcorrection problem is thus to transform the spectrum ofthe original DNA fragments into the spectrum of a genomicsequence by changing weak k-tuples to solid k-tuples.Without knowing the real genomic sequence, onenatural criterion of error correction is to minimize the totalnumber of distinct k-tuples in the spectrum. One errorin a fragment will create at most 2k (including the reversecomplement part) erroneous k-tuples in the spectrum, or2d (d 1. The de Bruijn graph correspondingto the original fragments of the NM genomeATGCATGTGCATGTATGTGT( a )ATATATGATGTGTGTGTGTGCTGTFigure 2. (a) Two DNA fragments and their 3-tuples (for simplicity,their reverse complements are not included); (b) edgeand vertex presentation of those 3-tuples; (c) a de Bruijn graphmade by “gluing” identical edges and vertices.GCGTATATG( b ) ( c )TGTGCTGTGCGT
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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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Page 163 and 164: 156 PARKHILL AND THOMSONGene Loss a
Page 165 and 166: 158 PARKHILL AND THOMSONYersinia ad
Page 167 and 168: 160 MCKAY ET AL.Choosing Candidate
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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Page 178 and 179: Building Comparative Maps Using 1.5
Page 180 and 181: HUMAN CHROMOSOME 1p IN THE DOG 1731
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Page 187 and 188: 180 GEORGES AND ANDERSSON5. There i
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Page 191 and 192: 184 GEORGES AND ANDERSSONbe common
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Page 196 and 197: Evolving Methods for the Assembly o
Page 198 and 199: ASSEMBLING LARGE GENOMES 191Figure
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Page 202 and 203: Mouse Genome Encyclopedia ProjectY.
Page 204 and 205: MOUSE GENOME ENCYCLOPEDIA PROJECT 1
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Page 225 and 226: 218 ZHANGthe majority of these are
Page 227 and 228: 220 ZHANGFigure 2. Demonstration of
Page 229 and 230: 222 ZHANG(G.X. Chen et al., in prep
Page 231 and 232: 224 ZHANGWe are waiting for experim
Page 234 and 235: Ontologies for Biologists: A Commun
Page 236 and 237: ONTOLOGIES FOR BIOLOGISTS 229al. 20
Page 238 and 239: ONTOLOGIES FOR BIOLOGISTS 231TOPIC
Page 240 and 241: ONTOLOGIES FOR BIOLOGISTS 233a.b.Fi
Page 242: ONTOLOGIES FOR BIOLOGISTS 2352003.
Page 245 and 246: 238 JOSHI-TOPE ET AL.Figure 1. The
Page 247 and 248: 240 JOSHI-TOPE ET AL.state of knowl
Page 249 and 250: 242 JOSHI-TOPE ET AL.and co-immunop
Page 252 and 253: The Share of Human Genomic DNA unde
Page 254 and 255: 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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