Bioinformatics, Volume I Data, Sequence Analysis and Evolution

More documents

Recommendations

Info

10. Accessing Sequence Data Managing Sequence Data 21 Fig. 1.5. Graphical display of results for VecScreen BLAST analysis that shows that the query sequence has cloning vector sequence spanning nucleotides 1–66 and 1838–1860. The sequence alignments showing the similarity are also present on this output page (not shown). 10.1. FTP database in which all of the RNA genes are present on the plus strand. Thus, the annotators can be sure that new ribosomal RNA submissions are released to INSD with the RNA on the plus strand. DDBJ, EMBL, and GenBank all have periodic releases of the sequences in the database. The sequences are available for download by ftp from each of the three sites (ftp://ftp.ddbj.nig.ac.jp/ database/ddbj/, ftp://ftp.ebi.ac.uk/pub/databases/embl/, and ftp://ftp.ncbi.nih.gov/genbank/). In addition, NCBI makes available a complete RefSeq release (ftp://ftp.ncbi.nih.gov/refseq/ release/). Users can download the complete release and search the databases at their own site.
22 Mizrachi 10.2. Entrez Retrieval System NCBI has a single search and retrieval system for all of the databases that it maintains. This system is called Entrez (4) (http:// www.ncbi.nlm.nih.gov/gquery/gquery.fcgi). Users can query an individual database or all of the databases with a single query. The number of sequences in Entrez Nucleotide and Entrez Protein continue to grow at an exponential rate. The Entrez Nucleotide database is a collection of sequences from several sources, including INSD and RefSeq. The Entrez Nucleotide database has divided into three subsets due to the rapid growth of the database. Recently, the EST and GSS divisions of GenBank have been separated from the rest of the sequences that are contained in the CoreNucleotide subset. The Entrez Protein database is a collection of sequences from a variety of sources, including SwissProt, PIR, PRF, PDB, and translations from annotated coding regions in INSD and Ref- Seq. One significant advantage to the Entrez retrieval system is the network of links that join entries from each of the databases. A nucleotide sequence record can have links (Fig. 1.6) to the Taxonomy, PubMed, PubMed Central, Protein, Popset, Unigene, Gene, and Genome databases. In addition to those links, a protein record will also have links to Related Structures, Conserved Domains, and Related Domains. All of these links can be accessed through a pull-down Links menu. In addition, the GenBank flat file also displays hyperlinks to the Taxonomy and PubMed databases. Links to external databases can be made by LinkOut or by db_xrefs within the entry. By taking advantage of these links, users Fig. 1.6. Results page from Entrez Nucleotide Database query. The Links menu shows list of links to Related Squences, Entrez PopSet, Entrez Protein, PubMed, and Entrez Taxonomy from DQ988166, the first sequence retrieved by this query. This Links assist with the navigation through the NCBI site for additional information related to sequences.
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 14 and 15: Managing Sequence Data Ilene Karsch
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 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
Page 62 and 63: 52 Scott and Hennig 5. In addition
Page 64 and 65: 54 Scott and Hennig rapidly with th
Page 66 and 67: 56 Scott and Hennig Acknowledgments
Page 68 and 69: 58 Scott and Hennig structure eluci
Page 70 and 71: 60 Scott and Hennig for correlating
Page 72 and 73: Chapter 3 Protein Structure Determi
Page 74 and 75: 1.2.2. X-Ray Diffraction 1.2.3. X-R
Page 76 and 77: 1.3. Structure Determination 1.3.1.
Page 78 and 79: 1.3.2. Rotation Function 1.3.3. Tra
Page 80 and 81: 1.4.1. Model Building Protein X-Ray
Page 82 and 83:
3.2. Crystal Cryoprotection Protein
Page 84 and 85:
Protein X-Ray Structure Determinati
Page 86 and 87:
3.5. Molecular Replacement Protein
Page 88 and 89:
3.6. Structure Refinement Protein X
Page 90 and 91:
3.7. Model Building Protein X-Ray S
Page 92 and 93:
4. Notes Protein X-Ray Structure De
Page 94 and 95:
Protein X-Ray Structure Determinati
Page 96 and 97:
References 1. Ducruix, A., Giegè,
Page 98 and 99:
90 Durinck 1.2. Quality Assessment
Page 100 and 101:
92 Durinck 1.5. Data Filtering 1.6.
Page 102 and 103:
94 Durinck 2.1.2. Matrices 2.1.3. L
Page 104 and 105:
96 Durinck 2.4.1. Affymetrix Experi
Page 106 and 107:
98 Durinck >norm=gcrma(data) Comput
Page 108 and 109:
100 Durinck 3.2. cDNA Microarray Da
Page 110 and 111:
102 Durinck Fig. 4.5. Image of the
Page 112 and 113:
104 Durinck 3.3. Detection of Diffe
Page 114 and 115:
106 Durinck Fig. 4.8. Volcano plot,
Page 116 and 117:
108 Durinck 4. Notes hg_u95av2”,v
Page 118 and 119:
110 Durinck 5. Irizarry, R. A., Hob
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
Page 140 and 141:
132 Kawaji and Hayashizaki 4. A gra
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 154 and 155:
Multiple Sequence Alignment 149 Tab
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
Page 174 and 175:
170 McHardy 2.3. Gene Finding in Eu
Page 176 and 177:
172 McHardy Known proteins Homology
Page 178 and 179:
174 McHardy 3. Notes 1. The periodi
Page 180 and 181:
176 McHardy specialization, and eff
Page 182 and 183:
Bioinformatics Detection of Alterna
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
Page 204 and 205:
202 Xing and Lee Fig. 10.2. Splice
Page 206 and 207:
204 Xing and Lee 4. Web Resources a
Page 208 and 209:
Sequence Segmentation Jonathan M. K
Page 210 and 211:
1.2. Change-Point Analysis Sequence
Page 212 and 213:
2. Systems, Data, and Databases 3.
Page 214 and 215:
3.2. Running changept Sequence Segm
Page 216 and 217:
Sequence Segmentation 215 -hp heati
Page 218 and 219:
3.3. Assessing Convergence Sequence
Page 220 and 221:
3.4. Determining Degree of Pooling
Page 222 and 223:
3.6. Generating and Viewing Profile
Page 224 and 225:
Sequence Segmentation 223 segmentat
Page 226 and 227:
3.8. Generating Segments Sequence S
Page 228 and 229:
Sequence Segmentation 227 to 10 lab
Page 230 and 231:
elements in vertebrate, insect, wor
Page 232 and 233:
232 Bailey 2. Representing Sequence
Page 234 and 235:
234 Bailey assumption implies that
Page 236 and 237:
236 Bailey 3. General General Techn
Page 238 and 239:
238 Bailey 4.1. Assemble: Select th
Page 240 and 241:
240 Bailey 4.3. Discover: Run a Mot
Page 242 and 243:
242 Bailey 4.4. Evaluate: Evaluate:
Page 244 and 245:
244 Bailey generate) the random seq
Page 246 and 247:
246 Bailey 5. Limitations of Motif
Page 248 and 249:
248 Bailey References 1. Blais, A.,
Page 250 and 251:
250 Bailey 40. La, D., Silver, M.,
Page 252 and 253:
Modeling Sequence Evolution Pietro
Page 254 and 255:
Fig. 13.1. Schematic description of
Page 256 and 257:
3. DNA Substitution Models where di
Page 258 and 259:
3.1. Modeling Rate Heterogeneity Al
Page 260 and 261:
5. Amino Acid Mutation Models Model
Page 262 and 263:
5.1. Generating Mutation Matrices M
Page 264 and 265:
5.2. Amino Acid Models Incorporatin
Page 266 and 267:
5.3. Amino Acid Models Incorporatin
Page 268 and 269:
6. RNA Model of Evolution 7. Models
Page 270 and 271:
8. Sub-Functionalization Model Mode
Page 272 and 273:
11. Tests for Functional Divergence
Page 274 and 275:
Modeling Sequence Evolution 277 of
Page 276 and 277:
12.1. The Evolution of Introns Mode
Page 278 and 279:
Fig. 13.6. Microsatellite length di
Page 280 and 281:
References 1. Hein, J. (1994) TreeA
Page 282 and 283:
59. von Mering, C., Krause, R., Sne
Page 284 and 285:
288 Whelan 2. Underlying Principles
Page 286 and 287:
290 Whelan 2.2. Why Estimating Tree
Page 288 and 289:
292 Whelan 3.2. Refining the Tree E
Page 290 and 291:
294 Whelan 3.3. Stopping Criteria t
Page 292 and 293:
296 Whelan 4. Confidence Intervals
Page 294 and 295:
298 Whelan 4.2. The Parametric Boot
Page 296 and 297:
300 Whelan 4.3. Limitations of Curr
Page 298 and 299:
302 Whelan 5.2. Sampling the Poster
Page 300 and 301:
304 Whelan 5.4. The Specification o
Page 302 and 303:
306 Whelan mutation. The replacemen
Page 304 and 305:
308 Whelan 13. Chang, J. T. (1996)
Page 306 and 307:
Detecting the Presence and Location
Page 308 and 309:
Presence and Location of Selection
Page 310 and 311:
Page 312 and 313:
Page 314 and 315:
2.3. Location of Diversifying Selec
Page 316 and 317:
Page 318 and 319:
4. Correcting for Multiple Tests Pr
Page 320 and 321:
5. Notes Presence and Location of S
Page 322 and 323:
Page 324 and 325:
References 1. McDonald, J., Kreitma
Page 326 and 327:
332 Jermiin et al. thought to have
Page 328 and 329:
334 Jermiin et al. 2.1. Phylogeneti
Page 330 and 331:
336 Jermiin et al. 2.2. Modeling th
Page 332 and 333:
338 Jermiin et al. 3. Choosing a Su
Page 334 and 335:
340 Jermiin et al. 3.3.1. 3.3.1. Ma
Page 336 and 337:
342 Jermiin et al. 3.3.3. Matched-P
Page 338 and 339:
344 Jermiin et al. obtained by usin
Page 340 and 341:
346 Jermiin et al. Fig. 16.2. The t
Page 342 and 343:
348 Jermiin et al. 3.4. Testing Tes
Page 344 and 345:
350 Jermiin et al. (Fig. 16.4C). Th
Page 346 and 347:
352 Jermiin et al. 3.5. Choosing a
Page 348 and 349:
354 Jermiin et al. catering for som
Page 350 and 351:
356 Jermiin et al. two time-reversi
Page 352 and 353:
358 Jermiin et al. 4. Discussion 1.
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 362 and 363:
Page 364 and 365:
Page 366 and 367:
Page 368 and 369:
Page 370 and 371:
Page 372 and 373:
Page 374 and 375:
Page 376 and 377:
Page 378 and 379:
Genome Rearrangement by the Double
Page 380 and 381:
1.2.2. DCJ Operation −3 −2 −5
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
Page 392 and 393:
1.4. DCJ on Circular and Linear Chr
Page 394 and 395:
1.4.3. Paths and Cycles, Odd and Ev
Page 396 and 397:
1.5. Linear Chromosomes with Restri
Page 398 and 399:
3. Identify synteny blocks (LCBs) i
Page 400 and 401:
3.2. Construction of a White Genome
Page 402 and 403:
3.4. Construction of Adjacency Grap
Page 404 and 405:
3.9. Sorting Without Linear Chromos
Page 406 and 407:
Genome Rearrangement 413 Fig. 18.20
Page 408 and 409:
Acknowledgments References 1. Nadea
Page 410 and 411:
Inferring Ancestral Protein Interac
Page 412 and 413:
2. Materials 2.1. Equipment 2.2. Ge
Page 414 and 415:
Page 416 and 417:
3.7. Visualization of Protein Inter
Page 418 and 419:
Page 420 and 421:
Page 422 and 423:
Acknowledgments References 1. Uetz,
Page 424 and 425:
Chapter 20 Computational Tools for
Page 426 and 427:
2. Materials 2.1. Computer Requirem
Page 428 and 429:
2.3.2. Data for Input to GRIMM and
Page 430 and 431:
3. Methods 3.1. GRIMM-Synteny: Iden
Page 432 and 433:
3.1.3. Forming Synteny Synteny Bloc
Page 434 and 435:
3.1.4. GRIMM-Synteny: Usage and Opt
Page 436 and 437:
3.1.5. Sample Run 1: Human-Mouse- R
Page 438 and 439:
3.2. 3.2. GRIMM: Identifying Identi
Page 440 and 441:
Analysis of Mammalian Genomes 447 G
Page 442 and 443:
3.3.1. Input Format 3.3.2. Output:
Page 444 and 445:
4. Notes Analysis of Mammalian Geno
Page 446 and 447:
(A) File data/unsigned1.txt >genome
Page 448 and 449:
11. Bourque, G., Zdobnov, E., Bork,
Page 450 and 451:
458 Beiko and Ragan 2. Systems, Sys
Page 452 and 453:
460 Beiko and Ragan 3. Methods 3.1.
Page 454 and 455:
462 Beiko and Ragan 3.3. Phylogenet
Page 456 and 457:
464 Beiko and Ragan 4. Notes A) B)
Page 458 and 459:
466 Beiko and Ragan variability to
Page 460 and 461:
468 Beiko and Ragan 8. Nelson, K. E
Page 462 and 463:
Detecting Genetic Recombination Geo
Page 464 and 465:
2. Materials 3. Methods Detecting R
Page 466 and 467:
3.3. Creating the First Phylogeneti
Page 468 and 469:
3.5. Refinement of the Sequence Set
Page 470 and 471:
3.9. Systematic Removal of of Recom
Page 472 and 473:
4. Notes Detecting Recombination 48
Page 474 and 475:
References 1. Beiko, R. G., Ragan,
Page 476 and 477:
486 Bunje and Wirth 2. Data and Mat
Page 478 and 479:
488 Bunje and Wirth 2.3. Types of D
Page 480 and 481:
490 Bunje and Wirth Table 23.1 List
Page 482 and 483:
492 Bunje and Wirth and Phrap (4).
Page 484 and 485:
494 Bunje and Wirth 3. Methods 3.1.
Page 486 and 487:
496 Bunje and Wirth 3.2.2. Mismatch
Page 488 and 489:
498 Bunje and Wirth Fig. 23.2. Exam
Page 490 and 491:
500 Bunje and Wirth Fig. 23.4. The
Page 492 and 493:
502 Bunje and Wirth 3.7.1. Direct E
Page 494 and 495:
504 Bunje and Wirth Acknowledgments
Page 496 and 497:
506 Bunje and Wirth 27. Wilson, G.
Page 498 and 499:
508 Gramm, Nickelsen, and Tantau 1.
Page 500 and 501:
Page 502 and 503:
512 Gramm, Nickelsen, and Tantau De
Page 504 and 505:
Page 506 and 507:
Page 508 and 509:
518 Gramm, Nickelsen, and Tantau De
Page 510 and 511:
Page 512 and 513:
522 Gramm, Nickelsen, and Tantau ho
Page 514 and 515:
524 Gramm, Nickelsen, and Tantau Fi
Page 516 and 517:
526 Gramm, Nickelsen, and Tantau ha
Page 518 and 519:
Page 520 and 521:
530 Gramm, Nickelsen, and Tantau In
Page 522 and 523:
Page 524 and 525:
534 Gramm, Nickelsen, and Tantau 31
Page 526 and 527:
A Ababneh, F., ....................
Page 528 and 529:
Dermitzakis, E. T., ...............
Page 530 and 531:
events in chordate evolution and R3
Page 532 and 533:
Li, W.-H., ........................
Page 534 and 535:
Phosphorus-fitting of doublets from
Page 536 and 537:
RSA Tools......... ................
Page 538 and 539:
UPGMA, tree calculation ...........
Page 540 and 541:
552 BIOINFORMATICS Index Bootstrap
Page 542 and 543:
554 BIOINFORMATICS Index FFT-NS-1 a
Page 544 and 545:
556 BIOINFORMATICS Index Jim Kent w
Page 546 and 547:
558 BIOINFORMATICS Index O OBO-Edit
Page 548 and 549:
560 BIOINFORMATICS Index Reference
Page 550:
562 BIOINFORMATICS Index Variance S
show all

Bioinformatics, Volume I Data, Sequence Analysis and Evolution

Create successful ePaper yourself

Delete template?

Save as template?