Bioinformatics, Volume I Data, Sequence Analysis and Evolution

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348 Jermiin et al. 3.4. Testing Testing the Assumption of Independent and Identical Identical Processes model to the first codon sites, another such model to the second codon sites, and a more general Markov model to the third codon sites. In conclusion, visual and statistical assessment of the nucleotide content in alignments of nucleotides provides considerable insight into the evolutionary processes that may have led to contemporary sequences—failure to acknowledge this, or to incorporate the information that the methods described in the preceding can provide, may lead to undetected errors in phylogenetic estimates. It is commonly assumed that the sites in an alignment of nucleotides are independent and identically distributed, or at least independently distributed. The latter case includes the commonly occurring scenario where rate-heterogeneity across sites is modeled using a Γ distribution and a proportion of sites are assumed to be permanently invariant. However, the order and number of nucleotides usually determine the function of the gene product, implying that it would be unrealistic, and maybe even unwise, to assume that the sites in the alignment of nucleotides are independent and identically distributed. To test whether sites in an alignment of nucleotides are independent and identically distributed, it is necessary to compare this simple model to the more complex models that describe the interrelationship among sites in the alignment of nucleotides. The description of the more complex models depends on prior knowledge of the genes and gene products, and the comparisons require the use of likelihood-ratio tests, sometimes combined with permutation tests or the parametric bootstrap. The likelihood-ratio test provides a statistically sound framework for testing alternative hypotheses within an evolutionary context (96). In statistical terms, the likelihood-ratio test statistic, ∆, is defined as: ( ( ) ) ( ( 1 ) ) max L H0| data ∆= max L H | data [10] where the likelihood, L, of the null hypothesis (H 0 ), given the data, and the likelihood of the alternative hypothesis (H 1 ), given the data, both are maximized with respect to the parameters. When ∆ > 1, the data are more likely to have evolved under H 0 — when ∆ < 1, the data favor the alternative hypothesis. When the two hypotheses are nested, that is H 0 is a special case of H 1 , ∆ is < 1, and −2 log ∆ is usually asymptotically distributed under H 0 as a c 2 variate with n degrees of freedom (where n is the extra number of parameters in H 1 )—for a detailed discussion of the likelihood-ratio test, see Whelan and Goldman (97) and Goldman and
A C Phylogenetic Model Evaluation 349 Whelan (98). When the hypotheses are not nested, it is necessary to use the parametric bootstrap (96, 99), in which case pseudodata are generated under H 0 . In some instances, permutation tests may be used instead (100). The evolution of protein-coding genes and RNA-coding genes is likely to differ due to the structural and functional constraints of their gene products, so to determine whether sites in an alignment of such genes evolved under independent and identical conditions, it is necessary to draw on knowledge of the structure and function of these genes and their gene products. For example, although a protein-coding gene could be regarded as a sequence of independently evolving sites (Fig. 16.4A), it would be more appropriate to consider it as a sequence of independently evolving codons (Fig. 16.4B) or a sequence of independently evolving codon positions, with sites in the same codon position evolving under identical and independent conditions Gene ATGAACGAAAATCTGTTCGCTTCATTCATTGCCCCCACAATCCTAGGCCTACCCGCCGCA Unit B Gene ATGAACGAAAATCTGTTCGCTTCATTCATTGCCCCCACAATCCTAGGCCTACCCGCCGCA Unit Gene ATGAACGAAAATCTGTTCGCTTCATTCATTGCCCCCACAATCCTAGGCCTACCCGCCGCA Unit Category 123123123123123123123123123123123123123123123123123123123123 D Gene ATGAACGAAAATCTGTTCGCTTCATTCATTGCCCCCACAATCCTAGGCCTACCCGCCGCA Unit Category 123123123123456456456456456456456456456456456789789789789789 E Gene ATGAACGAAAATCTGTTCGCTTCATTCATTGCCCCCACAATCCTAGGCCTACCCGCCGCA Unit 1 Unit 2 Fig. 16.4. Models used to describe the relationship among sites in protein-coding genes. A protein-coding gene may be regarded as a sequence of independently evolving units, where each unit is (A) a site, (B) a codon, or (C) a site assigned its own evolutionary model, depending on its position within a codon. The more complex models include those that consider (D) information about the gene product’s structure and function. (Here, categories 1, 2, and 3 correspond to models assigned to sites within the codons that encode amino acids in one structural domain, categories 4, 5, and 6 correspond to models assigned to sites in codons that encode amino acids in another structural domain, and so forth.) (E) Overlapping reading frames. (Here, unit 1 corresponds to one reading frame of one gene whereas unit 2 corresponds to that of the other gene.)
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Bioinformatics
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M ETHODS IN MOLECULAR BIOLOGY Bioi
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Preface Bioinformatics is the manag
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Contents Preface . . . . . . . . .
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Contributors FAISAL ABABNEH • Dep
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Contents of Volume II SECTION I: ST
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Managing Sequence Data Ilene Karsch
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2.2. The NCBI RefSeq Project Managi
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3.2. EST/STS/GSS 3.3. Mammalian Gen
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3.7. Third-Party Annotation Managin
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4. 4. Submission of of Sequence Dat
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6. The GenBank Sequence Record 6.1.
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7.1. Features and Sequence Managing
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Managing Sequence Data 17 first lev
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9. Pitfalls Pitfalls of an Archival
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10. Accessing Sequence Data Managin
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11. Conclusion 12. Notes Managing S
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Managing Sequence Data 25 by the se
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Acknowledgment References 1. Benson
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30 Scott and Hennig Large quantitie
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32 Scott and Hennig 2. Materials 2.
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34 Scott and Hennig 2.3. Anion-Exch
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36 Scott and Hennig 3.1.1. Large-Sc
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38 Scott and Hennig 3.2. NMR Resona
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40 Scott and Hennig Potentially, th
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42 Scott and Hennig qualitatively w
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44 Scott and Hennig 3.2.5. Residual
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46 Scott and Hennig 3.2.7. Base-to-
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48 Scott and Hennig Fig. 2.12. Sche
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50 Scott and Hennig 3.3.2. Molecula
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52 Scott and Hennig 5. In addition
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54 Scott and Hennig rapidly with th
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56 Scott and Hennig Acknowledgments
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58 Scott and Hennig structure eluci
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60 Scott and Hennig for correlating
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Chapter 3 Protein Structure Determi
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1.2.2. X-Ray Diffraction 1.2.3. X-R
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1.3. Structure Determination 1.3.1.
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1.3.2. Rotation Function 1.3.3. Tra
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1.4.1. Model Building Protein X-Ray
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3.2. Crystal Cryoprotection Protein
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Protein X-Ray Structure Determinati
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3.5. Molecular Replacement Protein
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3.6. Structure Refinement Protein X
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3.7. Model Building Protein X-Ray S
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4. Notes Protein X-Ray Structure De
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Protein X-Ray Structure Determinati
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References 1. Ducruix, A., Giegè,
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90 Durinck 1.2. Quality Assessment
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92 Durinck 1.5. Data Filtering 1.6.
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94 Durinck 2.1.2. Matrices 2.1.3. L
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96 Durinck 2.4.1. Affymetrix Experi
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98 Durinck >norm=gcrma(data) Comput
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100 Durinck 3.2. cDNA Microarray Da
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102 Durinck Fig. 4.5. Image of the
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104 Durinck 3.3. Detection of Diffe
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106 Durinck Fig. 4.8. Volcano plot,
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108 Durinck 4. Notes hg_u95av2”,v
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110 Durinck 5. Irizarry, R. A., Hob
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112 Harris 1.2. What Is an Ontology
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114 Harris 2.2.1. Groundwork 2.2.1.
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116 Harris 2.2.2. Ontology Construc
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118 Harris 2.2.3. Early Deployment
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120 Harris 3.1.4. Relationship Type
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122 Harris 5. Notes there is no sin
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124 Harris Genetics, Genomics, Prot
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126 Kawaji and Hayashizaki 2. Mater
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128 Kawaji and Hayashizaki 2.1.3. E
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130 Kawaji and Hayashizaki 2.2. Dat
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132 Kawaji and Hayashizaki 4. A gra
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134 Kawaji and Hayashizaki Export C
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136 Kawaji and Hayashizaki chr7: Us
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138 Kawaji and Hayashizaki Referenc
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Multiple Sequence Alignment Walter
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1.4. The Progressive Alignment Prot
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2.2. Unequal Sequence Lengths: Leng
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Multiple Sequence Alignment 149 Tab
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Multiple Sequence Alignment 151 par
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3.4. MAFFT Multiple Sequence Alignm
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3.6. SPEM 4. Notes Multiple Sequenc
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Multiple Sequence Alignment 157 mul
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References 1. Gribskov, M., McLachl
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34. Shi, J., Blundell, T. L., Mizug
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164 McHardy 2. Methods 2.1. Gene Fi
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166 McHardy Table 8.1. Publicly ava
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168 McHardy Fig. 8.2. Output of the
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170 McHardy 2.3. Gene Finding in Eu
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172 McHardy Known proteins Homology
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174 McHardy 3. Notes 1. The periodi
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176 McHardy specialization, and eff
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Bioinformatics Detection of Alterna
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1.2. How to Detect Alternative Alte
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Non-standard splice sites? (+) in m
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2.2. Completeness and Updating of P
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3. Methods 3.1. Pre-Processing Dete
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3.3. Alignment Filtering 3.4. Detec
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Detection of Alternative Splicing 1
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Detection of Alternative Splicing 1
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20. Mironov, A. A., Fickett, J. W.,
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124. Grasso, C., Quist, M., Ke, K.,
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200 Xing and Lee 2. The Isoform Pro
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202 Xing and Lee Fig. 10.2. Splice
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204 Xing and Lee 4. Web Resources a
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Sequence Segmentation Jonathan M. K
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1.2. Change-Point Analysis Sequence
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2. Systems, Data, and Databases 3.
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3.2. Running changept Sequence Segm
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Sequence Segmentation 215 -hp heati
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3.3. Assessing Convergence Sequence
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3.4. Determining Degree of Pooling
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3.6. Generating and Viewing Profile
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Sequence Segmentation 223 segmentat
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3.8. Generating Segments Sequence S
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Sequence Segmentation 227 to 10 lab
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elements in vertebrate, insect, wor
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232 Bailey 2. Representing Sequence
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234 Bailey assumption implies that
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236 Bailey 3. General General Techn
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238 Bailey 4.1. Assemble: Select th
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240 Bailey 4.3. Discover: Run a Mot
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242 Bailey 4.4. Evaluate: Evaluate:
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244 Bailey generate) the random seq
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246 Bailey 5. Limitations of Motif
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248 Bailey References 1. Blais, A.,
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250 Bailey 40. La, D., Silver, M.,
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Modeling Sequence Evolution Pietro
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Fig. 13.1. Schematic description of
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3. DNA Substitution Models where di
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3.1. Modeling Rate Heterogeneity Al
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5. Amino Acid Mutation Models Model
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5.1. Generating Mutation Matrices M
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5.2. Amino Acid Models Incorporatin
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5.3. Amino Acid Models Incorporatin
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6. RNA Model of Evolution 7. Models
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8. Sub-Functionalization Model Mode
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11. Tests for Functional Divergence
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Modeling Sequence Evolution 277 of
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12.1. The Evolution of Introns Mode
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Fig. 13.6. Microsatellite length di
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References 1. Hein, J. (1994) TreeA
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59. von Mering, C., Krause, R., Sne
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288 Whelan 2. Underlying Principles
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290 Whelan 2.2. Why Estimating Tree
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292 Whelan 3.2. Refining the Tree E
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294 Whelan 3.3. Stopping Criteria t
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Page 306 and 307: Detecting the Presence and Location
Page 308 and 309: Presence and Location of Selection
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Page 354 and 355: 360 Jermiin et al. 3. Hardy, M. P.,
Page 356 and 357: 362 Jermiin et al. 59. Azad, R. K.,
Page 358 and 359: 364 Jermiin et al. 114. Shapiro, B.
Page 360 and 361: 366 Catchen, Conery, and Postlethwa
Page 378 and 379: Genome Rearrangement by the Double
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Page 382 and 383: Genome Rearrangement 389 Fig. 18.4.
Page 384 and 385: Genome Rearrangement 391 Fig. 18.6.
Page 386 and 387: 1.2.5. Distance Genome Rearrangemen
Page 388 and 389: 1.3. DCJ on Circular and Linear Chr
Page 390 and 391: (A) (B) 1.3.4. Paths and Cycles 1.3
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1.4. DCJ on Circular and Linear Chr
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1.4.3. Paths and Cycles, Odd and Ev
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1.5. Linear Chromosomes with Restri
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3. Identify synteny blocks (LCBs) i
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3.2. Construction of a White Genome
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3.4. Construction of Adjacency Grap
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3.9. Sorting Without Linear Chromos
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Genome Rearrangement 413 Fig. 18.20
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Acknowledgments References 1. Nadea
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Inferring Ancestral Protein Interac
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2. Materials 2.1. Equipment 2.2. Ge
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3.7. Visualization of Protein Inter
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Acknowledgments References 1. Uetz,
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Chapter 20 Computational Tools for
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2. Materials 2.1. Computer Requirem
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2.3.2. Data for Input to GRIMM and
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3. Methods 3.1. GRIMM-Synteny: Iden
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3.1.3. Forming Synteny Synteny Bloc
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3.1.4. GRIMM-Synteny: Usage and Opt
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3.1.5. Sample Run 1: Human-Mouse- R
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3.2. 3.2. GRIMM: Identifying Identi
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Analysis of Mammalian Genomes 447 G
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3.3.1. Input Format 3.3.2. Output:
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4. Notes Analysis of Mammalian Geno
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(A) File data/unsigned1.txt >genome
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11. Bourque, G., Zdobnov, E., Bork,
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458 Beiko and Ragan 2. Systems, Sys
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460 Beiko and Ragan 3. Methods 3.1.
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462 Beiko and Ragan 3.3. Phylogenet
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464 Beiko and Ragan 4. Notes A) B)
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466 Beiko and Ragan variability to
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468 Beiko and Ragan 8. Nelson, K. E
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Detecting Genetic Recombination Geo
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2. Materials 3. Methods Detecting R
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3.3. Creating the First Phylogeneti
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3.5. Refinement of the Sequence Set
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3.9. Systematic Removal of of Recom
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4. Notes Detecting Recombination 48
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References 1. Beiko, R. G., Ragan,
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486 Bunje and Wirth 2. Data and Mat
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488 Bunje and Wirth 2.3. Types of D
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490 Bunje and Wirth Table 23.1 List
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492 Bunje and Wirth and Phrap (4).
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494 Bunje and Wirth 3. Methods 3.1.
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496 Bunje and Wirth 3.2.2. Mismatch
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498 Bunje and Wirth Fig. 23.2. Exam
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500 Bunje and Wirth Fig. 23.4. The
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502 Bunje and Wirth 3.7.1. Direct E
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504 Bunje and Wirth Acknowledgments
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506 Bunje and Wirth 27. Wilson, G.
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508 Gramm, Nickelsen, and Tantau 1.
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512 Gramm, Nickelsen, and Tantau De
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518 Gramm, Nickelsen, and Tantau De
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522 Gramm, Nickelsen, and Tantau ho
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524 Gramm, Nickelsen, and Tantau Fi
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526 Gramm, Nickelsen, and Tantau ha
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530 Gramm, Nickelsen, and Tantau In
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534 Gramm, Nickelsen, and Tantau 31
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A Ababneh, F., ....................
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Dermitzakis, E. T., ...............
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events in chordate evolution and R3
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Li, W.-H., ........................
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Phosphorus-fitting of doublets from
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RSA Tools......... ................
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UPGMA, tree calculation ...........
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552 BIOINFORMATICS Index Bootstrap
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554 BIOINFORMATICS Index FFT-NS-1 a
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556 BIOINFORMATICS Index Jim Kent w
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558 BIOINFORMATICS Index O OBO-Edit
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560 BIOINFORMATICS Index Reference
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562 BIOINFORMATICS Index Variance S
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