Methods for computational gene prediction:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge University Press
2007
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Publisher description Table of contents only Contributor biographical information Inhaltsverzeichnis |
| Beschreibung: | XVII, 430 Seiten Illustrationen 26 cm |
| ISBN: | 9780521706940 9780521877510 |
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| 245 | 1 | 0 | |a Methods for computational gene prediction |c William H. Majoros |
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| 650 | 4 | |a Genomics |x Data processing | |
| 650 | 4 | |a Bioinformatics | |
| 650 | 4 | |a Molecular genetics |x Data processing | |
| 650 | 4 | |a Molecular genetics |x Data processing |v Case studies | |
| 650 | 4 | |a Molecular genetics |x Mathematics | |
| 650 | 4 | |a Molecular genetics |x Mathematics |v Case studies | |
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Datensatz im Suchindex
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| adam_text | Methods for
Computational
Gene Prediction
WILLIAM H MAJOROS
Duke University
| CAMBRIDGE
UNIVERSITY PRESS
Contents
Foreword by Steven Salzberg page xi
Preface xiii
Acknowledgements xvi
Introduction 1
1 1 The central dogma of molecular biology 1
1 2 Evolution 13
1 3 Genome sequencing and assembly 15
1 4 Genomic annotation 19
1 5 The problem of computational gene prediction 25
Exercises 26
Mathematical preliminaries 28
2 1 Numbers and functions 28
2 2 Logic and boolean algebra 33
2 3 Sets 34 -
2 4 Algorithms and pseudocode 35
2 5 Optimization 38
2 6 Probability 40
2 7 Some important distributions 48
2 8 Parameter estimation 54
2 9 Statistical hypothesis testing 55
2 10 Information 58
2 11 Computational complexity 62
2 12 Dynamic programming 63
2 13 Searching and sorting 66
2 14 Graphs 68
2 15 Languages and parsing 74
Exercises 80
vi Contents
3 Overview of computational gene prediction 83
3 1 Genes, exons, and coding segments 83
3 2 Orientation 86
3 3 Phase and frame 89
3 4 Gene finding as parsing 92
3 5 Common assumptions in gene prediction 97
351 No overlapping genes 98
352 No nested genes 98
353 No partial genes 98
354 No noncanonical signal consensuses 99
355 No frameshifts or sequencing errors 99
356 Optimal parse only 100
357 Constraints on feature lengths 100
358 No split start codons 100
359 No split stop codons 101
3 5 10 No alternative splicing 101
3 5 11 No selenocysteine codons 101
3 5 12 No ambiguity codes 101
3 5 13 One haplotype only 102
Exercises 102
4 Gene finder evaluation 104
4 1 Testing protocols 104
4 2 Evaluation metrics 113
Exercises 118
5 A toy exon finder 120
5 1 The toy genome and its toy genes 120
5 2 Random exon prediction as a baseline 121
5 3 Predicting exons based on {G,C} bias 126
5 4 Predicting exons based on codon bias 127
5 5 Predicting exons based on codon bias and WMM score 130
5 6 Summary 134
Exercises 135
6 Hidden Markov models 136
6 1 Introduction to HMMs 136
611 An illustrative example 138
621 Representing HMMs 139
6 2 Decoding and similar problems 140
621 Finding the most probable path 140
622 Computing the probability of a sequence 143
6 3 Training with labeled sequences 145
Contents vii
6 4 Example: Building an HMM for gene finding 147
6 5 Case study: VEIL and UNVEIL 157
6 6 Using ambiguous models 159
661 Viterbi training 159
662 Merging submodels 161
663 Baum-Welch training 162
6631 Naive Baum-Welch algorithm 163
6632 Baum-Welch with scaling 165
6 7 Higher-order HMMs 169
671 Labeled sequence training for higher-order
HMMs 169
672 Decoding with higher-order HMMs 170
6 8 Variable-order HMMs 171
681 Back-off models 171
682 Example: Incorporating variable-order
emissions 172
683 Interpolated Markov models 173
6 9 Discriminative training of HMMs 174
6 10 Posterior decoding of HMMs 177
Exercises 179
7 Signal and content sensors 184
7 1 Overview of feature sensing 184
7 2 Content sensors 185
721 Markov chains 185
722 Markov chain implementation 188
723 Improved Markov chain implementation 188
724 Three-periodic Markov chains 190
725 Interpolated Markov chains 191
726 Nonstationary Markov chains 192
7 3 Signal sensors 193
731 Weight matrices 195
732 Weight array matrices 197
733 Windowed weight array matrices 198
734 Local optimality criterion 198
735 Coding-noncoding boundaries 200
736 Case study: GeneSplicer 201
737 Maximal dependence decomposition 201
738 Probabilistic tree models 205
739 Case study: Signal sensing in GENSCAN 207
7 4 Other methods of feature sensing 208
7 6 Case study: Bacterial gene finding 209
Exercises 211
viii Contents
Generalized hidden Markov models 214
8 1 Generalization and its advantages 214
8 2 Typical model topologies 218
821 One exon model or four? 221
822 One strand or two? 222
8 3 Decoding with a GHMM 223
831 PSA decoding 228
832 DSP decoding 237
833 Equivalence of DSP and PSA 242
834A DSP example 244
835 Shortcomings of DSP and PSA 246
8 4 Higher-fidelity modeling 247
841 Modeling isochores 247
842 Explicit modeling of noncoding lengths 249
8 5 Prediction with an ORF graph 251
851 Building the graph 252
852 Decoding with a graph 253
853 Extracting suboptimal parses 254
854 Posterior decoding for GHMMs 255
855 The ORF graph as a data interchange
format 256
8 6 Training a GHMM 259
861 Maximum likelihood training for GHMMs 260
862 Discriminative training for GHMMs 260
8 7 Case study: GHMM versus HMM 263
Exercises 264
Comparative gene finding 267
9 1 Informant techniques 269
911 Case study: TWINSCAN 269
912 -Case study: GenomeScan 271
913 Case study: SGP-2 272
914 Case study: HMMgene 273
915 Case study: GENIE 274
9 2 Combiners 275
921 Case study: JIGSAW 275
922 Case study: GAZE 277
9 3 Alignment-based prediction 277
931 Case study: ROSETTA 278
932 Case study: SGP-1 278
933 Case study: CEM 279
9 4 Pair HMMs 281
941 Case study: DoubleScan 285
9 5 Generalized pair HMMs 287
951 Case study: TWAIN 289
Contents ix
9 6 Phylogenomic gene finding 299
961 Phylogenetic HMMs 299
962 Decoding with a PhyloHMM 305
963 Evolution models 306
964 Parameterization of rate matrices 310
965 Estimation of evolutionary parameters 312
966 Modeling higher-order dependencies 316
967 Enhancing discriminative power 318
968 Selection of informants 318
9 7 Auto-annotation pipelines 319
9 8 Looking toward the future 320
Exercises 321
10 Machine-learning methods 325
10 1 Overview of automatic classification
10 2 K-nearest neighbors 328
10 3 Naive Bayes models 329
10 4 Bayesian networks 330
10 5 Neural networks 332
10 5 1 Case study: GRAIL 336
10 6 Decision trees 337
10 6 1 Case study: GlimmerM 339
10 7 Linear discriminant analysis 340
10 8 Quadratic discriminant analysis 342
10 9 Multivariate regression 343
10 10 Logistic regression 343
10 11 Regularized logistic regression 345
10 12 Genetic programming 346
10 13 Simulated annealing 349
10 14 Support vector machines 349
10 15 Hill-climbing-with the GSL 351
10 16 Feature selection and dimensionality
reduction 352
10 17 Applications 354
Exercises 355
11 Tips and tricks 358
11 1 Boosting 358
11 2 Bootstrapping 359
11 3 Modeling additional gene features 361
11 4 Masking repeats 366
Exercises 367
x Contents
12 Advanced topics 369
12 1 Alternative splicing and transcription 369
12 2 Prediction of noncoding genes 373
12 3 Promoter prediction 379
12 4 Generative versus discriminative modeling 382
12 5 Parallelization and grid computing 384
Exercises 386
Appendix 388
A 1 Official book website 388
A 2 Open-source gene finders 388
A3 Gene-finding websites 389
A 4 Gene-finding bibliographies 389
References 390
Index 408
CAMBRIDGE UNIVERSITY PRESS
Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, Sao Paulo
Cambridge University Press
The Edinburgh Building, Cambridge CB2 8RU, UK
Published in the United States of America by Cambridge University Press,
New York
www cambridge org
Information on this title: www cambridge org/9780521877510
© W H Majoros 2007
This publication is in copyright Subject to statutory exception
and to the provisions of relevant collective licensing agreements,
no reproduction of any part may take place without
the written permission of Cambridge University Press
First published 2007
Printed in the United Kingdom at the University Press, Cambridge
A catalog record for this publication is available from the British Library
ISBN 978-0-521-87751-0 hardback
ISBN 978-0-521-70694-0 paperback
Cambridge University Press has no responsibility for the persistence or
accuracy of URLs for external or thirdL-party internet websites referred
to in this publication, and does not guarantee that any content on such
websites is, or will remain, accurate or appropriate
UntversitfitsfaihQothflk
|
| adam_txt |
Methods for
Computational
Gene Prediction
WILLIAM H MAJOROS
Duke University
| CAMBRIDGE
UNIVERSITY PRESS
Contents
Foreword by Steven Salzberg page xi
Preface xiii
Acknowledgements xvi
Introduction 1
1 1 The central dogma of molecular biology 1
1 2 Evolution 13
1 3 Genome sequencing and assembly 15
1 4 Genomic annotation 19
1 5 The problem of computational gene prediction 25
Exercises 26
Mathematical preliminaries 28
2 1 Numbers and functions 28
2 2 Logic and boolean algebra 33
2 3 Sets 34 -
2 4 Algorithms and pseudocode 35
2 5 Optimization 38
2 6 Probability 40
2 7 Some important distributions 48
2 8 Parameter estimation 54
2 9 Statistical hypothesis testing 55
2 10 Information 58
2 11 Computational complexity 62
2 12 Dynamic programming 63
2 13 Searching and sorting 66
2 14 Graphs 68
2 15 Languages and parsing 74
Exercises 80
vi Contents
3 Overview of computational gene prediction 83
3 1 Genes, exons, and coding segments 83
3 2 Orientation 86
3 3 Phase and frame 89
3 4 Gene finding as parsing 92
3 5 Common assumptions in gene prediction 97
351 No overlapping genes 98
352 No nested genes 98
353 No partial genes 98
354 No noncanonical signal consensuses 99
355 No frameshifts or sequencing errors 99
356 Optimal parse only 100
357 Constraints on feature lengths 100
358 No split start codons 100
359 No split stop codons 101
3 5 10 No alternative splicing 101
3 5 11 No selenocysteine codons 101
3 5 12 No ambiguity codes 101
3 5 13 One haplotype only 102
Exercises 102
4 Gene finder evaluation 104
4 1 Testing protocols 104
4 2 Evaluation metrics 113
Exercises 118
5 A toy exon finder 120
5 1 The toy genome and its toy genes 120
5 2 Random exon prediction as a baseline 121
5 3 Predicting exons based on {G,C} bias 126
5 4 Predicting exons based on codon bias 127
5 5 Predicting exons based on codon bias and WMM score 130
5 6 Summary 134
Exercises 135
6 Hidden Markov models 136
6 1 Introduction to HMMs 136
611 An illustrative example 138
621 Representing HMMs 139
6 2 Decoding and similar problems 140
621 Finding the most probable path 140
622 Computing the probability of a sequence 143
6 3 Training with labeled sequences 145
Contents vii
6 4 Example: Building an HMM for gene finding 147
6 5 Case study: VEIL and UNVEIL 157
6 6 Using ambiguous models 159
661 Viterbi training 159
662 Merging submodels 161
663 Baum-Welch training 162
6631 Naive Baum-Welch algorithm 163
6632 Baum-Welch with scaling 165
6 7 Higher-order HMMs 169
671 Labeled sequence training for higher-order
HMMs 169
672 Decoding with higher-order HMMs 170
6 8 Variable-order HMMs 171
681 Back-off models 171
682 Example: Incorporating variable-order
emissions 172
683 Interpolated Markov models 173
6 9 Discriminative training of HMMs 174
6 10 Posterior decoding of HMMs 177
Exercises 179
7 Signal and content sensors 184
7 1 Overview of feature sensing 184
7 2 Content sensors 185
721 Markov chains 185
722 Markov chain implementation 188
723 Improved Markov chain implementation 188
724 Three-periodic Markov chains 190
725 Interpolated Markov chains 191
726 Nonstationary Markov chains 192
7 3 Signal sensors 193
731 Weight matrices 195
732 Weight array matrices 197
733 Windowed weight array matrices 198
734 Local optimality criterion 198
735 Coding-noncoding boundaries 200
736 Case study: GeneSplicer 201
737 Maximal dependence decomposition 201
738 Probabilistic tree models 205
739 Case study: Signal sensing in GENSCAN 207
7 4 Other methods of feature sensing 208
7 6 Case study: Bacterial gene finding 209
Exercises 211
viii Contents
Generalized hidden Markov models 214
8 1 Generalization and its advantages 214
8 2 Typical model topologies 218
821 One exon model or four? 221
822 One strand or two? 222
8 3 Decoding with a GHMM 223
831 PSA decoding 228
832 DSP decoding 237
833 Equivalence of DSP and PSA 242
834A DSP example 244
835 Shortcomings of DSP and PSA 246
8 4 Higher-fidelity modeling 247
841 Modeling isochores 247
842 Explicit modeling of noncoding lengths 249
8 5 Prediction with an ORF graph 251
851 Building the graph 252
852 Decoding with a graph 253
853 Extracting suboptimal parses 254
854 Posterior decoding for GHMMs 255
855 The ORF graph as a data interchange
format 256
8 6 Training a GHMM 259
861 Maximum likelihood training for GHMMs 260
862 Discriminative training for GHMMs 260
8 7 Case study: GHMM versus HMM 263
Exercises 264
Comparative gene finding 267
9 1 Informant techniques 269
911 Case study: TWINSCAN 269
912 -Case study: GenomeScan 271
913 Case study: SGP-2 272
914 Case study: HMMgene 273
915 Case study: GENIE 274
9 2 Combiners 275
921 Case study: JIGSAW 275
922 Case study: GAZE 277
9 3 Alignment-based prediction 277
931 Case study: ROSETTA 278
932 Case study: SGP-1 278
933 Case study: CEM 279
9 4 Pair HMMs 281
941 Case study: DoubleScan 285
9 5 Generalized pair HMMs 287
951 Case study: TWAIN 289
Contents ix
9 6 Phylogenomic gene finding 299
961 Phylogenetic HMMs 299
962 Decoding with a PhyloHMM 305
963 Evolution models 306
964 Parameterization of rate matrices 310
965 Estimation of evolutionary parameters 312
966 Modeling higher-order dependencies 316
967 Enhancing discriminative power 318
968 Selection of informants 318
9 7 Auto-annotation pipelines 319
9 8 Looking toward the future 320
Exercises 321
10 Machine-learning methods 325
10 1 Overview of automatic classification
10 2 K-nearest neighbors 328
10 3 Naive Bayes models 329
10 4 Bayesian networks 330
10 5 Neural networks 332
10 5 1 Case study: GRAIL 336
10 6 Decision trees 337
10 6 1 Case study: GlimmerM 339
10 7 Linear discriminant analysis 340
10 8 Quadratic discriminant analysis 342
10 9 Multivariate regression 343
10 10 Logistic regression 343
10 11 Regularized logistic regression 345
10 12 Genetic programming 346
10 13 Simulated annealing 349
10 14 Support vector machines 349
10 15 Hill-climbing-with the GSL 351
10 16 Feature selection and dimensionality
reduction 352
10 17 Applications 354
Exercises 355
11 Tips and tricks 358
11 1 Boosting 358
11 2 Bootstrapping 359
11 3 Modeling additional gene features 361
11 4 Masking repeats 366
Exercises 367
x Contents
12 Advanced topics 369
12 1 Alternative splicing and transcription 369
12 2 Prediction of noncoding genes 373
12 3 Promoter prediction 379
12 4 Generative versus discriminative modeling 382
12 5 Parallelization and grid computing 384
Exercises 386
Appendix 388
A 1 Official book website 388
A 2 Open-source gene finders 388
A3 Gene-finding websites 389
A 4 Gene-finding bibliographies 389
References 390
Index 408
CAMBRIDGE UNIVERSITY PRESS
Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, Sao Paulo'
Cambridge University Press
The Edinburgh Building, Cambridge CB2 8RU, UK
Published in the United States of America by Cambridge University Press,
New York
www cambridge org
Information on this title: www cambridge org/9780521877510
© W H Majoros 2007
This publication is in copyright Subject to statutory exception
and to the provisions of relevant collective licensing agreements,
no reproduction of any part may take place without
the written permission of Cambridge University Press
First published 2007
Printed in the United Kingdom at the University Press, Cambridge
A catalog record for this publication is available from the British Library
ISBN 978-0-521-87751-0 hardback
ISBN 978-0-521-70694-0 paperback
Cambridge University Press has no responsibility for the persistence or
accuracy of URLs for external or thirdL-party internet websites referred
to in this publication, and does not guarantee that any content on such
websites is, or will remain, accurate or appropriate
UntversitfitsfaihQothflk |
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| ctrlnum | (OCoLC)137221654 (DE-599)BVBBV023119996 |
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| genre_facet | Fallstudiensammlung |
| id | DE-604.BV023119996 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:51:08Z |
| indexdate | 2024-07-09T21:11:29Z |
| institution | BVB |
| isbn | 9780521706940 9780521877510 |
| language | English |
| lccn | 2007299649 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016322479 |
| oclc_num | 137221654 |
| open_access_boolean | |
| owner | DE-29T DE-M49 DE-BY-TUM DE-188 |
| owner_facet | DE-29T DE-M49 DE-BY-TUM DE-188 |
| physical | XVII, 430 Seiten Illustrationen 26 cm |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Majoros, William H. Verfasser aut Methods for computational gene prediction William H. Majoros 1. publ. Cambridge [u.a.] Cambridge University Press 2007 XVII, 430 Seiten Illustrationen 26 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references (p. 390-407) and index Datenverarbeitung Mathematik Genomics Data processing Bioinformatics Molecular genetics Data processing Molecular genetics Data processing Case studies Molecular genetics Mathematics Molecular genetics Mathematics Case studies Genanalyse (DE-588)4200230-8 gnd rswk-swf Bioinformatik (DE-588)4611085-9 gnd rswk-swf Molekulare Bioinformatik (DE-588)4531334-9 gnd rswk-swf (DE-588)4522595-3 Fallstudiensammlung gnd-content Molekulare Bioinformatik (DE-588)4531334-9 s DE-604 Genanalyse (DE-588)4200230-8 s Bioinformatik (DE-588)4611085-9 s http://www.loc.gov/catdir/enhancements/fy0803/2007299649-d.html Publisher description http://www.loc.gov/catdir/enhancements/fy0803/2007299649-t.html Table of contents only http://www.loc.gov/catdir/enhancements/fy0803/2007299649-b.html Contributor biographical information HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016322479&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Majoros, William H. Methods for computational gene prediction Includes bibliographical references (p. 390-407) and index Datenverarbeitung Mathematik Genomics Data processing Bioinformatics Molecular genetics Data processing Molecular genetics Data processing Case studies Molecular genetics Mathematics Molecular genetics Mathematics Case studies Genanalyse (DE-588)4200230-8 gnd Bioinformatik (DE-588)4611085-9 gnd Molekulare Bioinformatik (DE-588)4531334-9 gnd |
| subject_GND | (DE-588)4200230-8 (DE-588)4611085-9 (DE-588)4531334-9 (DE-588)4522595-3 |
| title | Methods for computational gene prediction |
| title_auth | Methods for computational gene prediction |
| title_exact_search | Methods for computational gene prediction |
| title_exact_search_txtP | Methods for computational gene prediction |
| title_full | Methods for computational gene prediction William H. Majoros |
| title_fullStr | Methods for computational gene prediction William H. Majoros |
| title_full_unstemmed | Methods for computational gene prediction William H. Majoros |
| title_short | Methods for computational gene prediction |
| title_sort | methods for computational gene prediction |
| topic | Datenverarbeitung Mathematik Genomics Data processing Bioinformatics Molecular genetics Data processing Molecular genetics Data processing Case studies Molecular genetics Mathematics Molecular genetics Mathematics Case studies Genanalyse (DE-588)4200230-8 gnd Bioinformatik (DE-588)4611085-9 gnd Molekulare Bioinformatik (DE-588)4531334-9 gnd |
| topic_facet | Datenverarbeitung Mathematik Genomics Data processing Bioinformatics Molecular genetics Data processing Molecular genetics Data processing Case studies Molecular genetics Mathematics Molecular genetics Mathematics Case studies Genanalyse Bioinformatik Molekulare Bioinformatik Fallstudiensammlung |
| url | http://www.loc.gov/catdir/enhancements/fy0803/2007299649-d.html http://www.loc.gov/catdir/enhancements/fy0803/2007299649-t.html http://www.loc.gov/catdir/enhancements/fy0803/2007299649-b.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016322479&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT majoroswilliamh methodsforcomputationalgeneprediction |