Bioinformatics, Volume I Data, Sequence Analysis and Evolution

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Multiple Sequence Alignment 149 Table 7.1 Web sites of multiple alignment programs mentioned in this chapter Name Web site PRALINE http://ibi.vu.nl/programs/ pralinewww/ MUSCLE http://www.drive5.com/muscle/ T-Coffee and 3D-Coffee http://igs-server.cnrs-mrs.fr/ Tcoffee/tcoffee_cgi/index.cgi MAFFT http://align.bmr.kyushu-u.ac.jp/ mafft/online/server/ ProbCons http://probcons.stanford.edu/ SPEM & SPEM-3D http://sparks.informatics.iupui. edu/Softwares-Services_files/ spem_3d.htm Fig. 7.2. The PRALINE standard web interface. Protein sequences can be pasted in the upper box in FASTA format or directly uploaded from a file. In addition to using default settings, various alignment strategies can be selected (see Section 3.1) as well as the desired number of iterations or preprocessing cut-off scores.
150 Pirovano and Heringa 3.2. MUSCLE (18,19), (2) local pre-processing (19), and (3) PSI-Praline (20). The first two options attempt to maximize the information from each sequence. For each sequence, a preprofile is built containing information from other sequences in the query set. Under global pre-processing, other sequences can be selected according to a preset minimal pairwise alignment score with the main sequence within each pre-profile. Under local pre-processing, segments of other sequences in the query set are selected based on local alignment scores. The PSI-Praline pre-profile processing strategy employs the PSI-BLAST homology search engine (21) to enrich the information of each of the pre-profiles. Based on a user-specified E-value, PSI-BLAST selects sequence fragments from a large non-redundant sequence database, building more consistent and useful pre-profiles for the alignment. The alignment quality of the PSI-Praline strategy is among the highest in the field (20), but the technique is relatively slow as a PSI-BLAST run needs to be conducted for every sequence in the input set. 2. DSSP or predicted secondary structure information. PRALINE currently allows the incorporation of DSSP-defined secondary structure information (22) to guide the alignment. If no DSSP is available, a choice of seven secondary structure prediction methods is provided to determine the putative secondary structure of those sequences that do not have a PDB structure. In addition, two different consensus strategies are also included, both relying on the prediction methods PSIPRED (23), PROFsec (24), and YASPIN (25). 3. Iteration. For the above global and local pre-processing strategies, iterative optimization is possible. Iteration is based on the consistency of a preceding multiple alignment, in which consistency is defined as the agreement between matched amino acids in the multiple alignment and those in corresponding pairwise alignments. These consistency scores are then fed as weights to a next round of dynamic programming. During iteration, therefore, consistent multiple alignment positions tend to be maintained, whereas inconsistent segments are more likely to become re-aligned. Iterations are terminated upon reaching convergence or limit cycle (i.e., a number of cyclically recurring multiple alignments), whereas the user can also specify a maximum number of iterations. MUSCLE (26, 27) is multiple alignment software for both nucleotide and protein sequences. It includes an online server, but the user can also choose to download the program and run it locally. The web server performs calculations using pre-defined default parameters, albeit the program provides a large number of options. MUSCLE is a very fast algorithm, which should be
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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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Page 108 and 109: 100 Durinck 3.2. cDNA Microarray Da
Page 110 and 111: 102 Durinck Fig. 4.5. Image of the
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Page 116 and 117: 108 Durinck 4. Notes hg_u95av2”,v
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Page 120 and 121: 112 Harris 1.2. What Is an Ontology
Page 122 and 123: 114 Harris 2.2.1. Groundwork 2.2.1.
Page 124 and 125: 116 Harris 2.2.2. Ontology Construc
Page 126 and 127: 118 Harris 2.2.3. Early Deployment
Page 128 and 129: 120 Harris 3.1.4. Relationship Type
Page 130 and 131: 122 Harris 5. Notes there is no sin
Page 132 and 133: 124 Harris Genetics, Genomics, Prot
Page 134 and 135: 126 Kawaji and Hayashizaki 2. Mater
Page 136 and 137: 128 Kawaji and Hayashizaki 2.1.3. E
Page 138 and 139: 130 Kawaji and Hayashizaki 2.2. Dat
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Page 142 and 143: 134 Kawaji and Hayashizaki Export C
Page 144 and 145: 136 Kawaji and Hayashizaki chr7: Us
Page 146 and 147: 138 Kawaji and Hayashizaki Referenc
Page 148 and 149: Multiple Sequence Alignment Walter
Page 150 and 151: 1.4. The Progressive Alignment Prot
Page 152 and 153: 2.2. Unequal Sequence Lengths: Leng
Page 156 and 157: Multiple Sequence Alignment 151 par
Page 158 and 159: 3.4. MAFFT Multiple Sequence Alignm
Page 160 and 161: 3.6. SPEM 4. Notes Multiple Sequenc
Page 162 and 163: Multiple Sequence Alignment 157 mul
Page 164 and 165: References 1. Gribskov, M., McLachl
Page 166 and 167: 34. Shi, J., Blundell, T. L., Mizug
Page 168 and 169: 164 McHardy 2. Methods 2.1. Gene Fi
Page 170 and 171: 166 McHardy Table 8.1. Publicly ava
Page 172 and 173: 168 McHardy Fig. 8.2. Output of the
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Page 176 and 177: 172 McHardy Known proteins Homology
Page 178 and 179: 174 McHardy 3. Notes 1. The periodi
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Page 184 and 185: 1.2. How to Detect Alternative Alte
Page 186 and 187: Non-standard splice sites? (+) in m
Page 188 and 189: 2.2. Completeness and Updating of P
Page 190 and 191: 3. Methods 3.1. Pre-Processing Dete
Page 192 and 193: 3.3. Alignment Filtering 3.4. Detec
Page 194 and 195: Detection of Alternative Splicing 1
Page 196 and 197: Detection of Alternative Splicing 1
Page 198 and 199: 20. Mironov, A. A., Fickett, J. W.,
Page 200 and 201: 124. Grasso, C., Quist, M., Ke, K.,
Page 202 and 203: 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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