Bioinformatics, Volume I Data, Sequence Analysis and Evolution

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Managing Sequence Data Ilene Karsch Mizrachi Abstract Chapter 1 Nucleotide and protein sequences are the foundation for all bioinformatics tools and resources. Researchers can analyze these sequences to discover genes or predict the function of their products. The INSD (International Nucleotide Sequence Database—DDBJ/EMBL/GenBank) is an international, centralized primary sequence resource that is freely available on the internet. This database contains all publicly available nucleotide and derived protein sequences. This chapter summarizes the nucleotide sequence database resources, provides information on how to submit sequences to the databases, and explains how to access the sequence data. Key words: DNA sequence database, GenBank, EMBL, DDBJ, INSD. 1. Introduction The International Nucleotide Sequence Database (INSD) is a centralized public sequence resource. As of August 2007, it contains over 101 million DNA sequences comprised of over 181 billion nucleotides, numbers that continue to increase exponentially. Scientists generate and submit their primary sequence data to INSD as part of the publication process. The database is archival and represents the results of scientists’ experiments. The annotation, represented by annotated features such as coding regions, genes, and structural RNAs, on the sequence is based on the submitter’s observations and conclusions rather than those of the database curators. Jonathan M. Keith (ed.), Bioinformatics, Volume I: Data, Sequence Analysis, and Evolution, vol. 452 © 2008 Humana Press, a part of Springer Science + Business Media, Totowa, NJ Book doi: 10.1007/978-1-60327-159-2 Springerprotocols.com 3
4 Mizrachi 2. Sequence Databases 2.1. International Collaboration The International Nucleotide Sequence Database (INSD) Collaboration is a partnership among GenBank (http://www. ncbi.nlm.nih.gov/Genbank/) NCBI, NLM, NIH, Bethesda, MD, (1) EMBL database (http://www.ebi.ac.uk/EMBL/) EBI, Hinxton, United Kingdom, (2) and DDBJ (http://www.ddbj. nig.ac.jp/) National Institute of Genetics, Mishima, Japan (3). For over 25 years, GenBank, EMBL, and DDBJ have maintained this active, successful collaboration for building and maintaining nucleotide sequence databases. Representatives from the three databases meet annually to discuss technical and biological issues affecting the databases. Ensuring that sequence data from scientists worldwide is freely available to all is the primary mission of this group. As part of the publication process, scientists are required to deposit sequence data in a public repository; the INSD encourages publishers of scientific journals to enforce this policy to ensure that sequence data associated with a paper are freely available from an international resource. One advantage to depositing sequences in INSD is that they are available electronically, which is far more usable to the scientific community than having the sequences in a printed paper. Consequently, scientists can download a sequence from INSD and analyze the sequences as part of their research. The three databases have built a shared web site, http:// insdc.org (Fig. 1.1), which contains information, policies, and procedures that are important for the collaborators, submitters, and users. This site contains: 1. The data release policy 2. Standards for submission of sequences to the nucleotide databases 3. The Feature Table Document, outlining legal features and syntax to be included in the sequence record in order to standardize annotation across the databases (see Note 1) 4. Links to the three contributing databases’ websites, where submitters can find information regarding submission and sequence retrieval that is specific to each database Each of the three INSD contributors has its own set of submission (see Section 4.1) and retrieval tools (see Note 2). Although scientists may submit data to any of the three databases for inclusion in INSD, data processed at each site are exchanged daily so that a sequence submitted to any of the three databases will be retrievable from all three sites. In order to avoid entries that are out of sync at the three sites, submitters must update their submissions only at the site where they initially submitted the data. Updates are also propagated to the other two sites through the
Page 2 and 3: Bioinformatics
Page 4 and 5: M ETHODS IN MOLECULAR BIOLOGY Bioi
Page 6 and 7: Preface Bioinformatics is the manag
Page 8 and 9: Contents Preface . . . . . . . . .
Page 10 and 11: Contributors FAISAL ABABNEH • Dep
Page 12 and 13: Contents of Volume II SECTION I: ST
Page 16 and 17: 2.2. The NCBI RefSeq Project Managi
Page 18 and 19: 3.2. EST/STS/GSS 3.3. Mammalian Gen
Page 20 and 21: 3.7. Third-Party Annotation Managin
Page 22 and 23: 4. 4. Submission of of Sequence Dat
Page 24 and 25: 6. The GenBank Sequence Record 6.1.
Page 26 and 27: 7.1. Features and Sequence Managing
Page 28 and 29: Managing Sequence Data 17 first lev
Page 30 and 31: 9. Pitfalls Pitfalls of an Archival
Page 32 and 33: 10. Accessing Sequence Data Managin
Page 34 and 35: 11. Conclusion 12. Notes Managing S
Page 36 and 37: Managing Sequence Data 25 by the se
Page 38 and 39: Acknowledgment References 1. Benson
Page 40 and 41: 30 Scott and Hennig Large quantitie
Page 42 and 43: 32 Scott and Hennig 2. Materials 2.
Page 44 and 45: 34 Scott and Hennig 2.3. Anion-Exch
Page 46 and 47: 36 Scott and Hennig 3.1.1. Large-Sc
Page 48 and 49: 38 Scott and Hennig 3.2. NMR Resona
Page 50 and 51: 40 Scott and Hennig Potentially, th
Page 52 and 53: 42 Scott and Hennig qualitatively w
Page 54 and 55: 44 Scott and Hennig 3.2.5. Residual
Page 56 and 57: 46 Scott and Hennig 3.2.7. Base-to-
Page 58 and 59: 48 Scott and Hennig Fig. 2.12. Sche
Page 60 and 61: 50 Scott and Hennig 3.3.2. Molecula
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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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296 Whelan 4. Confidence Intervals
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298 Whelan 4.2. The Parametric Boot
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300 Whelan 4.3. Limitations of Curr
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302 Whelan 5.2. Sampling the Poster
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304 Whelan 5.4. The Specification o
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306 Whelan mutation. The replacemen
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308 Whelan 13. Chang, J. T. (1996)
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Detecting the Presence and Location
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Presence and Location of Selection
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2.3. Location of Diversifying Selec
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4. Correcting for Multiple Tests Pr
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5. Notes Presence and Location of S
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References 1. McDonald, J., Kreitma
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332 Jermiin et al. thought to have
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334 Jermiin et al. 2.1. Phylogeneti
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336 Jermiin et al. 2.2. Modeling th
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338 Jermiin et al. 3. Choosing a Su
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340 Jermiin et al. 3.3.1. 3.3.1. Ma
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342 Jermiin et al. 3.3.3. Matched-P
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344 Jermiin et al. obtained by usin
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346 Jermiin et al. Fig. 16.2. The t
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348 Jermiin et al. 3.4. Testing Tes
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350 Jermiin et al. (Fig. 16.4C). Th
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352 Jermiin et al. 3.5. Choosing a
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354 Jermiin et al. catering for som
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356 Jermiin et al. two time-reversi
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358 Jermiin et al. 4. Discussion 1.
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360 Jermiin et al. 3. Hardy, M. P.,
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362 Jermiin et al. 59. Azad, R. K.,
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364 Jermiin et al. 114. Shapiro, B.
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366 Catchen, Conery, and Postlethwa
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Genome Rearrangement by the Double
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1.2.2. DCJ Operation −3 −2 −5
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Genome Rearrangement 389 Fig. 18.4.
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Genome Rearrangement 391 Fig. 18.6.
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1.2.5. Distance Genome Rearrangemen
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1.3. DCJ on Circular and Linear Chr
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(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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