Verfügbarkeit: Introduction to computational proteomics

Introduction to computational proteomics:

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Bibliographische Detailangaben
1. Verfasser:	Yona, Golan (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Boca Raton [u.a.] CRC Press 2011
Schriftenreihe:	Chapman & Hall/CRC mathematical and computational biology series
Schlagworte:	Mathematisches Modell Proteomics > Mathematical models Proteomics > methods Proteomanalyse Methode
Online-Zugang:	Inhaltsverzeichnis
Beschreibung:	Auch angekündigt u.d.T.: Computational proteomics
Beschreibung:	XXII, 739 S. Ill., graph. Darst.
ISBN:	9781584885559

Internformat

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Datensatz im Suchindex

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adam_text	Contents I The Basics 1 1 What Is Computational Proteomics? 3 1.1 The complexity of Ľ ving organisms .............. 3 1.2 Proteomics in the modern era ................. 4 1.3 The main challenges in computational proteomics ...... 5 1.3.1 Analysis of individual molecules ............ 5 1.3.1.1 Sequence analysis .............. 5 1.3.1.2 Structure analysis ............. 6 1.3.2 From individual proteins to protein families ..... 6 1.3.3 Protein classification, clustering and embedding ... 7 1.3.4 Interactions, pathways and gene networks ...... 7 2 Basic Notions in Molecular Biology 9 2.1 The cell structure of organisms ................ 9 2.2 It all starts from the DNA................... 10 2.3 Proteins ............................. 12 2.4 Prom DNA to proteins ..................... 15 2.5 Protein folding - from sequence to structure ......... 18 2.6 Evolution and relational classes in the protein space .... 20 2.7 Problems ............................. 22 3 Sequence Comparison 23 3.1 Introduction ........................... 23 3.2 Alignment of sequences ..................... 24 3.2.1 Global sequence similarity ............... 25 3.2.1.1 Calculating the global similarity score . . 26 3.2.2 Penalties for gaps .................... 27 3.2.2.1 Linear gap functions ............ 28 3.2.3 Local alignments .................... 29 3.2.3.1 Calculating the local similarity score ... 30 3.3 Heuristic algorithms for sequence comparison ........ 31 3.4 Probability and statistics of sequence alignments ...... 32 3.4.1 Basic random model .................. 33 3.4.2 Statistics of global alignment ............. 33 3.4.2.1 Fixed alignment - global alignment without gaps ..................... 34 3.4.2.2 Optimal alignment ............. 34 3.4.2.3 The zscore approach ............ 35 3.4.3 Statistics of local alignments without gaps ...... 37 3.4.3.1 Fixed alignment .............. 37 3.4.3.2 Optimal alignment ............. 38 3.4.4 Local alignments with gaps .............. 43 3.4.5 Handling low-complexity sequences .......... 45 3.4.6 Sequence identity and statistical significance ..... 48 3.4.7 Similarity, homology and transitivity ......... 49 3.5 Scoring matrices and gap penalties .............. 50 3.5.1 Scoring matrices for nucleic acids ........... 50 3.5.2 Scoring matrices for amino acids ........... 50 3.5.2.1 The РАМ family of scoring matrices ... 52 3.5.2.2 The BLOSUM family of scoring matrices 57 3.5.3 Information content of scoring matrices ....... 58 3.5.3.1 Choosing the scoring matrix ........ 60 3.5.4 Gap penalties ...................... 61 3.6 Distance and pseudo-distance functions for proteins ..... 62 3.7 Further reading ......................... 66 3.8 Conclusions ........................... 68 3.9 Appendix - non-linear gap penalty functions ......... 69 3.10 Appendix - implementation of BLAST and FASTA ...... 72 3.10.1 FASTA .......................... 72 3.10.2 BLAST ......................... 72 3.11 Appendix - performance evaluation .............. 75 3.11-1 Accuracy, sensitivity and selectivity ......... 76 3.11.2 ROC ........................... 79 3.11.3 Setup and normalization ................ 80 3.11.4 Reference datasets, negatives and positives ..... 82 3.11.5 Training and testing algorithms ............ 83 3.12 Appendix - basic concepts in probability ........... 85 3.12.1 Probability mass and probability density ....... 85 3.12.2 Moments ........................ 86 3.12.3 Conditional probability and Bayes formula ..... 87 3.12.4 Common probability distributions .......... 88 3.12.5 The entropy function .................. 91 3.12.6 Relative entropy and mutual information ...... 92 3.12.7 Prior and posterior, ML and MAP estimators .... 93 3.12.8 Decision rules and hypothesis testing ......... 95 3.13 Appendix - metrics and real normed spaces ......... 98 3.14 Problems ............................. 100 Multiple Sequence Alignment, Profiles and Partial Order Graphs 105 4.1 Dynamic programming in N dimensions ........... 106 4.1.1 Scoring functions .................... 107 4.2 Classical heuristic methods . .................. 108 4.2.1 Star alignment ..................... 109 4.2.2 Tree alignment ..................... 110 4.3 MSA representation and scoring ................ 113 4.3.1 The consensus sequence of an MSA .......... 113 4.3.2 Regular expressions ................... 114 4.3.3 Profiles and position-dependent scores ........ 116 4.3.3.1 Generating a profile ............ 116 4.3.3.2 Pseudo-counts ............... 117 4.3.3.3 Weighting sequences ............ 122 4.3.4 Position-specific scoring matrices ........... 126 4.3.4.1 Using PSSMs with the dynamic programming algorithm .......... 128 4.3.5 Profile-profile comparison ............... 128 4.4 Iterative and progressive alignment .............. 132 4.4.1 PSI-BLAST - iterative profile search algorithm ... 132 4.4.2 Progressive star alignment ............... 136 4.4.3 Progressive profile alignment ............. 137 4.5 Transitive alignment ...................... 138 4.5.1 T-coffee ......................... 139 4.6 Partial order alignment ..................... 141 4.6.1 The partial order MSA model ............. 142 4.6.2 The partial order alignment algorithm ........ 144 4.7 Further reading ......................... 148 4.8 Conclusions ........................... 149 4.9 Problems ............................. 150 Motif Discovery 155 5.1 Introduction ........................... 155 5.2 Model-based algorithms .................... 156 5.2.1 The basic model .................... 157 5.2.2 Model quality ...................... 158 5.2.2.1 Case 1: model unknown, patterns are given 159 5.2.2.2 Case 2: model is given, patterns are unknown .................. 160 5.3 Searching for good models ................... 160 5.3.1 The Gibbs sampling algorithm ............ 161 5.3.1.1 Improvements ................ 162 5.3.2 The MEME algorithm ................. 162 5.3.2.1 E-step .................... 164 5.3.2.2 M-step .................... 165 5.3.2.3 The iterative procedure .......... 166 5.4 Combinatorial approaches ................... 167 5.4.1 Clique elimination ................... 167 5.4.2 Random projections .................. 170 5.5 Further reading ......................... 173 5.6 Conclusions ........................... 175 5.7 Appendix - the Expectation-Maximization algorithm .... 176 5.8 Problems ............................. 180 Markov Models of Protein Families 183 6.1 Introduction ........................... 183 6.2 Markov models ......................... 184 6.2.1 Gene prediction ..................... 184 6.2.2 Formal definition .................... 188 6.2.2.1 Visible symbols and hidden Markov models 190 6.2.2.2 The model s components .......... 190 6.3 Main applications of hidden Markov models ......... 191 6.3.1 The evaluation problem ................ 192 6.3.1.1 The HMM forward algorithm ....... 194 6.3.1.2 The HMM backward algorithm ...... 194 6.3.1.3 Using HMMs for classification ....... 196 6.3.2 The decoding problem ................. 196 6.3.3 The learning problem ................. 198 6.3.3.1 The forward-backward algorithm ..... 198 6.3.3.2 Learning from multiple training sequences 200 6.3.4 Handling machine precision limitations ........ 201 6.3.5 Constructing a model ................. 202 6.3.5.1 General model topology .....·..... 202 6.3.5.2 Model architecture ............. 202 6.3.5.3 Hidden Markov models for protein families 204 6.3.5.4 Handling silent states ........... 206 6.3.5.5 Building a model from an MSA ...... 206 6.3.5.6 Single model vs. mixtures of multiple models .................... 209 6.4 Higher order models, codes and compression ......... 210 6.4.1 Fixed order models ................... 211 . 6.4.2 Variable-order Markov models ............. 213 6.4.2.1 Codes and compression .......... 214 6.4.2.2 Compression and prediction ........ 217 6.4.2.3 Lempel-Ziv compression and extensions . 218 6.4.2.4 Probabilistic suffix trees .......... 219 6.4.2.5 Sparse Markov transducers ........ 227 6.4.2.6 Prediction by partial matches ....... 229 6.5 Further reading ......................... 231 6.6 Conclusions ........................... 232 6.7 Problems ............................. 233 Classifiers and Kernels 235 7.1 Generative models vs. discriminative models ......... 235 7.2 Classifiers and discriminant functions ............. 237 7.2.1 Linear classifiers .................... 238 7.2.2 Linearly separable case ................. 241 7.2.3 Maximizing the margin ................ 244 7.2.4 The non-separable case - soft margin ......... 246 7.2.5 Non-linear discriminant functions ........... 249 7.2.5.1 Mercer kernels ............... 253 7.3 Applying SVMs to protein classification ........... 255 7.3.1 String kernels ...................... 256 7.3.1.1 Simple string kernel - the spectrum kernel 256 7.3.1.2 The mismatch spectrum kernel ...... 257 7.3.2 The pairwise kernel .................... 257 7.3.3 The Fischer kernel ................... 258 7.3.4 Mutual information kernels .............. 259 7.4 Decision trees .......................... 262 7.4.1 The basic decision tree model ............. 263 7.4.2 Training decision trees ................. 264 7.4.2.1 Impurity measures for multi- valued attributes .................. 267 7.4.2.2 Missing attributes ............. 268 7.4.2.3 Tree pruning ................ 268 7.4.3 Stochastic trees and mixture models ......... 270 7.4.4 Evaluation of decision trees .............. 272 7 Λ A.I Handling skewed distributions ....... 274 7.4.5 Representation and feature extraction ........ 275 7.4.5.1 Feature processing ............. 276 7.4.5.2 Dynamic attribute filtering ........ 277 7.4.5.3 Binary splitting ............... 278 7.5 Further reading .......................... 279 7.6 Conclusions ........................... 280 7.7 Appendix - estimating the significance of a split ....... 281 7.8 Problems ............................. 288 Protein Structure Analysis 291 8.1 Introduction ........................... 291 8.2 Structure prediction - the protein folding problem ...... 293 8.2.1 Protein secondary structure prediction ........ 296 8.2.1.1 Secondary structure assignment ...... 297 8.2.1.2 Secondary structure prediction ...... 299 8.2.1.3 Accuracy of secondary structure prediction 301 8.3 Structure comparison ...................... 303 8.3.1 Algorithms based on inter-atomic distances ..... 305 8.3.1.1 The RMSd measure ............ 305 8.3.1.2 The structal algorithm ........... 309 8.3.1.3 The URMS distance ............ 311 8.3.1.4 The URMS-RMS algorithm ........ 312 8.3.2 Distance matrix based algorithms ........... 318 8.3.2.1 Dali ..................... 319 8.3.2.2 GE...................... 322 8.3.3 Geometric hashing ................... 324 8.3.4 Statistical significance of structural matches ..... 327 8.3.5 Evaluation of structure comparison .......... 330 8.4 Generalized sequence profiles - integrating secondary structure with sequence information ................... 332 8.5 Further reading ......................... 336 8.6 Conclusions ........................... 339 8.7 Appendix - minimizing RMSd ................. 340 8.8 Problems ............................. 342 Protein Domains 345 9.1 Introduction ........................... 345 9.2 Domain detection ........................ 348 9.2.1 Domain prediction from 3D structure ......... 349 9.2.2 Domain analysis based on predicted measures of structural stability ................... 351 9.2.3 Domain prediction based on sequence similarity search 355 9.2.4 Domain prediction based on multiple sequence alignments ........................ 361 9.3 Learning domain boundaries from multiple features ..... 364 9.3.1 Feature optimization .................. 365 9.3.2 Scaling features ..................... 366 9.3.3 Post-processing predictions .............. 366 9.3.4 Training and evaluation of models .......... 369 9.4 Testing domain predictions ................... 370 9.4.1 Selecting more likely partitions ............ 373 9.4.1.1 Computing tlie prior P(D) ........ 375 9.4.1.2 Computing the likelihood P(S D) .... 376 9.4.2 The distribution of domain lengths .......... 378 9.5 Multi-domain architectures .................. 380 9.5.1 Hierarchies of multi-domain proteins ......... 380 9.5.2 Relationships between domain architectures ..... 381 9.5.3 Semantically significant domain architectures .... 385 9.6 Further reading ......................... 387 9.7 Conclusions ........................... 389 9.8 Appendix - domain databases ................. 390 9.9 Problems ............................. 393 II Putting All the Pieces Together 395 10 Clustering and Classification 397 10.1 Introduction ........................... 397 10.2 Clustering methods ........................ 399 10.3 Vector-space clustering algorithms .............. 401 10.3.1 The k-means algorithm ................ 402 10.3.2 Fuzzy clustering .................... 404 10.3.3 Hierarchical algorithms ................. 408 10.3.3.1 Hierarchical k-means ............ 409 10.3.3.2 The statistical mechanics approach .... 409 10.4 Graph-based clustering algorithms .............. 410 10.4.1 Pairwise clustering algorithms ............. 411 10.4.1.1 The single linkage algorithm ........ 412 10.4.1.2 The complete linkage algorithm ...... 414 10.4.1.3 The average linkage algorithm ....... 414 10.4.2 Collaborative clustering ................ 415 10.4.3 Spectral clustering algorithms ............. 421 10.4.4 Markovian clustering algorithms ........... 425 10.4.5 Super-paramagnetic clustering ............ 427 10.5 Cluster validation and assessment ............... 428 10.5.1 External indices of validity .............. 430 10.5.1.1 The case of known classification ...... 430 10.5.1.2 The case of known relations ........ 433 10.5.2 Internal indices of validity ............... 434 10.5.2.1 The MDL principle ............. 434 10.5.2.2 Cross-validation ............... 439 10.6 Clustering proteins ....................... 440 10.6.1 Domains vs. complete proteins ............ 440 10.6.2 Graph representation .................. 441 10.6.3 Graph-based protein clustering ............ 442 1Ö.6.4 Integrating multiple similarity measures ....... 444 10.7 Further reading ......................... 448 10.8 Conclusions ........................... 450 10.9 Appendix - cross-validation tests ............... 451 10.10 Problems ............................. 457 11 Embedding Algorithms and Vectorial Representations 459 11.1 Introduction ........................... 459 11.2 Structure preserving embedding ................. 461 11.2.1 Maximal variance embeddings ............. 461 11.2.1.1 Principal component analysis ....... 462 11.2.1.2 Singular value decomposition ....... 467 11.2.2 Distance preserving embeddings ............ 467 11.2.2.1 Multidimensional scaling .......... 468 11.2.2.2 Embedding through random projections . 474 11.2.3 Manifold learning - topological embeddings ..... 478 11.2.3.1 Embedding with geodesic distances .... 479 11.2.3.2 Preserving local neighborhoods ...... 482 11.2.3.3 Distributional scaling ........... 484 11.3 Setting the dimension of the host space ............ 488 11.4 Vectorial representations .................... 490 11.4.1 Internal representations ................ 492 11.4.2 Collective and external representations ........ 493 11.4.2.1 Choosing a reference set and an association measure .................... 494 11.4.2.2 Transformations and normalizations . . . 495 11.4.2.3 Noise reduction ............... 495 11.4.2.4 Comparing distance profiles ........ 496 11.4.2.5 Distance profiles and mixture models . . . 500 11.5 Further reading ......................... 502 11.6 Conclusions ........................... 503 11.7 Problems ............................. 504 12 Analysis of Gene Expression Data 505 12.1 Introduction ........................... 505 12.2 Microarrays ........................... 509 12.2.1 Datasets ......................... 512 12.3 Analysis of individual genes .................. 513 12.4 Pairwise analysis ........................ 515 12.4.1 Measures of expression similarity ........... 517 12.4.1.1 Shifts .................... 520 12.4.2 Missing data ....................... 521 12.4.3 Correlation vs. anti-correlation ............ 523 12.4.4 Statistical significance of expression similarity .... 524 12.4.5 Evaluating similarity measures ............ 527 12.4.5.1 Estimating baseline performance ..... 528 12.5 Cluster analysis and class discovery .............. 529 12.5.1 Validating clustering results .............. 534 12.5.2 Assessing individual clusters .............. 536 12.5.3 Enrichment analysis .................. 538 12.5.3.1 The gene ontology ............. 538 12.5.3.2 Gene set enrichment ............ 541 12.5.4 Limitations of mRNA arrays ............. 544 12.6 Protein arrays .......................... 545 12.6.1 Mass-spectra data ................... 546 12.7 Further reading ......................... 548 12.8 Conclusions ........................... 550 12.9 Problems ............................. 551 13 Protein-Protein Interactions 553 13.1 Introduction ........................... 553 13.2 Experimental detection of protein interactions ........ 556 13.2.1 Traditional methods .................. 557 13.2.1.1 Affinity chromatography .......... 557 13.2.1.2 Co-immunoprecipitation .......... 558 13.2.2 High-throughput methods ............... 558 13.2.2.1 The two-hybrid system ........... 558 13.2.2.2 Tandem affinity purification . ....... 560 13.2.2.3 Protein arrays ................ 561 13.3 Prediction of protein-protein interactions ........... 561 13.3.1 Structure-based prediction of interactions ...... 562 13.3.1.1 Protein docking and prediction of interaction sites ............... 563 13.3.1.2 Extensions to sequences of unknown structures .................. 567 13.3.2 Sequence-based inference ................ 568 13.3.2.1 Gene preservation and locality ...... 568 13.3.2.2 Co-evolution analysis ............ 571 13.3.2.3 Predicting the interaction interface .... 578 13.3.2.4 Sequence signatures and domain-based prediction .................. 582 13.3.3 Gene co-expression ................... 589 13.3.4 Hybrid methods .................... 589 13.3.5 Training and testing models on interaction data . . . 591 13.4 Interaction networks ...................... 592 13.4.1 Topological properties of interaction networks .... 593 13.4.2 Applications ....................... 601 13.4.3 Network motifs and the modular organization of networks ......................... 603 13.5 Further reading ......................... 606 13.6 Conclusions ........................... 607 13.7 Appendix - DNA amplification and protein expression . . . 608 13.7.1 Plasmids ......................... 608 13.7.2 SDS-PAGE ....................... 608 13.8 Appendix - the Pearson correlation .............. 610 13.8.1 Uneven divergence rates ................ 610 13.8.2 Insensitivity to the size of the dataset ........ 610 13.8.3 The effect of outliers .................. 611 13.9 Problems ............................. 613 14 Cellular Pathways 615 14.1 Introduction ........................... 615 14.2 Metabolic pathways ....................... 618 14.3 Pathway prediction ....................... 621 14.3.1 Metabolic pathway prediction ............. 621 14.3.2 Pathway prediction from blueprints .......... 623 14.3.2.1 The problem of pathway holes ....... 623 14.3.2.2 The problem of ambiguity ......... 623 14.3.3 Expression data and pathway analysis ........ 624 14.3.3.1 Deterministic gene assignments ...... 626 14.3.3.2 Fuzzy assignments .............. 629 14.3.4 From model to practice ................ 632 14.4 Regulatory networks: modules and regulation programs . . 635 14.5 Pathway networks and the minimal cell ............ 640 14.6 Further reading ......................... 642 14.7 Conclusions ........................... 645 14.8 Problems ............................. 646 15 Learning Gene Networks with Bayesian Networks 649 15.1 Introduction ........................... 649 15.1.1 The basics of Bayesian networks ........... 650 15.2 Computing the likelihood of observations ........... 654 15.3 Probabilistic inference ..................... 655 15.3.1 Inferring the values of variables in a network .... 656 15.3.2 Inference of multiple unknown variables ....... 660 15.4 Learning the parameters of a Bayesian network ....... 661 15.4.1 Computing the probability of new instances ..... 666 15.4.2 Learning from incomplete data ............ 667 15.5 Learning the structure of a Bayesian network ........ 669 15.5.1 Alternative score functions ............... 672 15.5.2 Searching for optimal structures ........... 674 15.5.2.1 Greedy search ................ 675 15.5.2.2 Sampling techniques ............ 675 15.5.2.3 Model averaging .............. 676 15.5:3 Computing the probability of new instances ..... 678 15.6 Learning Bayesian networks from microarray data ...... 678 15.7 Further reading ......................... 682 15.8 Conclusions ........................... 683 15.9 Problems ............................. 684 References 687 Conference Abbreviations 735 Acronyms 737 Index 739
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id	DE-604.BV037157048
illustrated	Illustrated
indexdate	2024-07-09T22:52:44Z
institution	BVB
isbn	9781584885559
language	English
lccn	2010042932
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-021071712
oclc_num	706984771
open_access_boolean
owner	DE-355 DE-BY-UBR DE-20
owner_facet	DE-355 DE-BY-UBR DE-20
physical	XXII, 739 S. Ill., graph. Darst.
publishDate	2011
publishDateSearch	2011
publishDateSort	2011
publisher	CRC Press
record_format	marc
series2	Chapman & Hall/CRC mathematical and computational biology series
spelling	Yona, Golan Verfasser aut Introduction to computational proteomics Golan Yona Computational proteomics Boca Raton [u.a.] CRC Press 2011 XXII, 739 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC mathematical and computational biology series Auch angekündigt u.d.T.: Computational proteomics Mathematisches Modell Proteomics Mathematical models Proteomics methods Proteomanalyse (DE-588)4596545-6 gnd rswk-swf Methode (DE-588)4038971-6 gnd rswk-swf Proteomanalyse (DE-588)4596545-6 s Methode (DE-588)4038971-6 s DE-604 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021071712&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Yona, Golan Introduction to computational proteomics Mathematisches Modell Proteomics Mathematical models Proteomics methods Proteomanalyse (DE-588)4596545-6 gnd Methode (DE-588)4038971-6 gnd
subject_GND	(DE-588)4596545-6 (DE-588)4038971-6
title	Introduction to computational proteomics
title_alt	Computational proteomics
title_auth	Introduction to computational proteomics
title_exact_search	Introduction to computational proteomics
title_full	Introduction to computational proteomics Golan Yona
title_fullStr	Introduction to computational proteomics Golan Yona
title_full_unstemmed	Introduction to computational proteomics Golan Yona
title_short	Introduction to computational proteomics
title_sort	introduction to computational proteomics
topic	Mathematisches Modell Proteomics Mathematical models Proteomics methods Proteomanalyse (DE-588)4596545-6 gnd Methode (DE-588)4038971-6 gnd
topic_facet	Mathematisches Modell Proteomics Mathematical models Proteomics methods Proteomanalyse Methode
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021071712&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT yonagolan introductiontocomputationalproteomics AT yonagolan computationalproteomics

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