Bioinformatics: Sequence Alignment and Markov Models

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  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2008-09-04
  • Publisher: McGraw-Hill Education
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GET FULLY UP-TO-DATE ON BIOINFORMATICS-THE TECHNOLOGY OF THE 21ST CENTURYBioinformaticsshowcases the latest developments in the field along with all the foundational information you'll need. It provides in-depth coverage of a wide range of autoimmune disorders and detailed analyses of suffix trees, plus late-breaking advances regarding biochips and genomes.Featuring helpful gene-finding algorithms,Bioinformaticsoffers key information on sequence alignment, HMMs, HMM applications, protein secondary structure, microarray techniques, and drug discovery and development. Helpful diagrams accompany mathematical equations throughout, and exercises appear at the end of each chapter to facilitate self-evaluation.This thorough, up-to-date resource features: Worked-out problems illustrating concepts and models End-of-chapter exercises for self-evaluation Material based on student feedback Illustrations that clarify difficult math problems A list of bioinformatics-related websitesBioinformaticscovers: Sequence representation and alignment Hidden Markov models Applications of HMMs Gene finding Protein secondary structure prediction Microarray techniques Drug discovery and development Internet resources and public domain databases

Author Biography

Kal Renganathan Sharma, Ph.D., P.E., has written five books, 11 journal articles, and hundreds of conference papers. He has held a number of high-level positions at engineering colleges and universities. Dr. Sharma currently teaches at Prairie View AAndM University in Prairie View, Texas.

Table of Contents

Prefacep. xi
Acknowledgmentsp. xv
Preliminariesp. 1
Molecular Biologyp. 2
Amino Acids and Proteinsp. 2
Structures of Proteinsp. 3
Sequence Distribution of Insulinp. 6
Bioseparation Techniquesp. 9
Nucleic Acids and Genetic Codep. 12
Genomes-Diversity, Size, and Structurep. 20
Probability and Statisticsp. 23
Three Definitions of Probabilityp. 24
Bayes' Theorem and Conditional Probabilityp. 25
Independent Events and Bernoulli's Theoremp. 25
Discrete Probability Distributionsp. 26
Continuous Probability Distributionsp. 28
Statistical Inference and Hypothesis Testingp. 30
Which Is Larger, 2[superscript n] or n[superscript 2]?p. 31
Big O Notation and Asymptotic Order of Functionsp. 32
Summaryp. 33
References and Sourcesp. 34
Exercisesp. 35
Sequence Alignment and Representation
Alignment of a Pair of Sequencesp. 41
Objectivesp. 41
Introduction to Pairwise Sequence Alignmentp. 41
Why Study Sequence Alignmentp. 43
Alignment Grading Functionp. 47
Optimal Global Alignment of a Pair of Sequencesp. 51
Needleman and Wunsch Algorithmp. 51
Dynamic Programmingp. 55
Time Analysis and Space Efficiencyp. 56
Dynamic Arrays and O(N) Spacep. 56
Subquadratic Algorithms for Longest Common Subsequence Problemsp. 57
Optimal Local Alignment of a Pair of Sequencesp. 59
Smith and Waterman Algorithmp. 59
Affine Gap Modelp. 60
Greedy Algorithms for Pairwise Alignmentp. 63
Other Alignment Methodsp. 65
Pam and Blosum Matricesp. 66
Summaryp. 69
Referencesp. 70
Further Readingp. 71
Exercisesp. 71
Sequence Representation and String Algorithmsp. 85
Objectivesp. 85
Suffix Treesp. 85
Overview of Suffix Trees in Sequence Analysisp. 85
Algorithm for Suffix Tree Representation of a Sequencep. 88
Streaming a Sequence Against a Suffix Treep. 89
String Algorithmsp. 91
Rabin-Karp Algorithmp. 92
Knuth-Morris-Pratt (KMP) Algorithmp. 92
Boyer-Moore Algorithmp. 94
Finite Automatonp. 96
Suffix Trees in String Algorithmsp. 97
Look-up Tablesp. 99
Summaryp. 100
Referencesp. 101
Exercisesp. 102
Multiple-Sequence Alignmentp. 115
Objectivesp. 115
What Is Multiple-Sequence Alignment?p. 115
Defenitions of Multiple Global Alignment and Sum of Pairsp. 117
Multiple Global Alignmentp. 117
Sum of Pairsp. 117
Optimal MSA by Dynamic Programmingp. 117
Theorem of Wang and Jiang [2]p. 118
What Are NP Complete Problems?p. 118
Center-Star-Alignment Algorithm [4]p. 119
Time Analysisp. 119
Progressive Alignment Methodsp. 121
The Consensus Sequencep. 122
Greedy Methodp. 123
Geometry of Multiple Sequencesp. 123
Summaryp. 125
Referencesp. 125
Exercisesp. 126
Probability Models
Hidden Markov Models and Applicationsp. 133
Objectivesp. 133
Introductionp. 133
kth-order Markov Chainp. 134
DNA Sequence and Geometric Distribution [2-4]p. 135
Three Questions in the HMMp. 143
Evaluation Problem and Forward Algorithmp. 146
Decoding Problem and Viterbi Algorithmp. 146
Relative Entropyp. 147
Probabilistic Approach to Phylogenyp. 149
Sequence Alignment Using HMMsp. 152
Protein Familiesp. 153
Wheel HMMs to Model Periodicity in DNAp. 156
Generalized HMM (GHMM)p. 157
Database Miningp. 160
Multiple Alignmentsp. 160
Classification Using HMMsp. 161
Signal Peptide and Signal Anchor Prediction by HMMsp. 162
Markov Model and Chargaff's Parity Rulesp. 163
Summaryp. 164
Referencesp. 165
Exercisesp. 166
Gene Finding, Protein Secondary Structurep. 179
Objectivesp. 179
Introductionp. 179
Relative Entropy Site-Selection Problemp. 180
Greedy Approachp. 180
Gibbs Samplerp. 181
Maximum-Subsequence Problemp. 182
Bates and Constable Algorithmp. 182
Binomial Heap [4-7]p. 182
Interpolated Markov Model (IMM)p. 184
Shine Dalgarno SD Sites Findingp. 185
Gene Annotation Methodsp. 187
Secondary Structures of Proteinsp. 191
Neural Networksp. 193
PHD Architecture of Rost and Sanderp. 196
Ensemble Method of Riis and Krogh [23]p. 198
Protein Secondary Structure Using HMMsp. 199
DAG RNNs: Directed Acyclic Graphs and Recursive NN Architecture and 3D Protein Structure Predictionp. 200
Annotate Subcellular Localization for Protein Structurep. 201
Summaryp. 203
Referencesp. 204
Exercisesp. 206
Measurement Techniques
Biochipsp. 213
Objectivesp. 213
Introductionp. 213
Microarrays, Biochips, and Diseasep. 214
Five Steps and Ten Tipsp. 218
Applications of Microarraysp. 220
Microarray Detectionp. 223
Fluorescence Detection and Optical Requirementsp. 223
Confocal Scanning Microscopep. 224
Microarray Surfacesp. 227
Phosphoramadite Synthesisp. 231
Microarray Manufacturep. 233
Normalization for cDNA Microarray Datap. 236
Summaryp. 240
Referencesp. 241
Exercisesp. 242
Electrophoretic Techniques and Finite Speed of Diffusionp. 245
Objectivesp. 245
Role of Electrophoresis in the Measurement of Sequence Distributionp. 245
Fick's Laws of Molecular Diffusionp. 246
Generalized Fick's Law of Diffusionp. 249
Derivation of a Generalized Fick's Law of Diffusionp. 251
Taitel Paradox and Final Time Conditionp. 254
Relativistic Transformation of Coordinatesp. 259
Periodic Boundary Conditionp. 267
Electrophoresis Apparatusp. 269
Electrophoretic Term, Ballistic Term, and Fick Term in the Governing Equationp. 270
Summaryp. 274
Referencesp. 275
Exercisesp. 276
Internet Hotlinks to Public-Domain Databasesp. 287
PERL for Bioinformaticistsp. 299
Indexp. 303
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