Multiple Biological Sequence Alignment : Scoring Functions, Algorithms and Evaluation.

Cover -- Title Page -- Copyright -- Contents -- Preface -- Chapter 1 Introduction -- 1.1 Motivation -- 1.2 The Organization of this Book -- 1.3 Sequence Fundamentals -- 1.3.1 Protein -- 1.3.2 DNA/RNA -- 1.3.3 Sequence Formats -- 1.3.4 Motifs -- 1.3.5 Sequence Databases -- Chapter 2 Protein/DNA/RNA Pairwise Sequence Alignment -- 2.1 Sequence Alignment Fundamentals -- 2.2 Dot-Plot Matrix -- 2.3 Dynamic Programming -- 2.3.1 Needleman-Wunsch's Algorithm -- 2.3.2 Example -- 2.3.3 Smith-Waterman's Algorithm -- 2.3.4 Affine Gap Penalty -- 2.4 Word Method -- 2.4.1 Example -- 2.5 Searching Sequence Databases -- 2.5.1 FASTA -- 2.5.2 BLAST -- Chapter 3 Quantifying Sequence Alignments -- 3.1 Evolution and Measuring Evolution -- 3.1.1 Jukes and Cantor's Model -- 3.1.2 Measuring Relatedness -- 3.2 Substitution Matrices and Scoring Matrices -- 3.2.1 Identity Scores -- 3.2.2 Substitution/Mutation Scores -- 3.3 GAPS -- 3.3.1 Sequence Distances -- 3.3.2 Example -- 3.4 Scoring Multiple Sequence Alignments -- 3.4.1 Sum-of-Pair Score -- 3.5 Circular Sum Score -- 3.6 Conservation Score Schemes -- 3.6.1 Wu and Kabat's Method -- 3.6.2 Jores's Method -- 3.6.3 Lockless and Ranganathan's Method -- 3.7 Diversity Scoring Schemes -- 3.7.1 Background -- 3.7.2 Methods -- 3.8 Stereochemical Property Methods -- 3.8.1 Valdar's Method -- 3.9 Hierarchical Expected Matching Probability Scoring Metric (HEP) -- 3.9.1 Building an AACCH Scoring Tree -- Procedure Build AACCH Scoring Tree -- 3.9.2 The Scoring Metric -- 3.9.3 Proof of Scoring Metric Correctness -- 3.9.4 Examples -- 3.9.5 Scoring Metric and Sequence Weighting Factor -- 3.9.6 Evaluation Data Sets -- 3.9.7 Evaluation Results -- Chapter 4 Sequence Clustering -- 4.1 Unweighted Pair Group Method with Arithmetic Mean-UPGMA -- 4.2 Neighborhood-Joining Method-NJ -- 4.3 Overlapping Sequence Clustering.

Chapter 5 Multiple Sequences Alignment Algorithms -- 5.1 Dynamic Programming -- 5.1.1 DCA -- 5.2 Progressive Alignment -- 5.2.1 Clustal Family -- 5.2.2 PIMA: Pattern-Induced Multisequence Alignment -- 5.2.3 PRIME: Profile-Based Randomized Iteration Method -- 5.2.4 DIAlign -- 5.3 Consistency and Probabilistic MSA -- 5.3.1 POA: Partial Order Graph Alignment -- 5.3.2 PSAlign -- 5.3.3 ProbCons: Probabilistic Consistency-Based Multiple Sequence Alignment -- 5.3.4 T-Coffee: Tree-Based Consistency Objective Function for Alignment Evaluation -- 5.3.5 MAFFT: MSA Based on Fast Fourier Transform -- 5.3.6 AVID -- 5.3.7 Eulerian Path MSA -- 5.4 Genetic Algorithms -- 5.4.1 SAGA: Sequence Alignment by Genetic Algorithm -- 5.4.2 GA and Self-Organizing Neural Networks -- 5.4.3 FAlign -- 5.5 New Development in Multiple Sequence Alignment Algorithms -- 5.5.1 KB-MSA: Knowledge-Based Multiple Sequence Alignment -- 5.5.2 PADT: Progressive Multiple Sequence Alignment Based on Dynamic Weighted Tree -- 5.6 Test Data and Alignment Methods -- 5.7 Results -- 5.7.1 Measuring Alignment Quality -- 5.7.2 RT-OSM Results -- Chapter 6 Phylogeny in Multiple Sequence Alignments -- 6.1 The Tree of Life -- 6.2 Phylogeny Construction -- 6.2.1 Distance Methods -- 6.2.2 Character-Based Methods -- 6.2.3 Maximum Likelihood Methods -- 6.2.4 Bootstrapping -- 6.2.5 Subtree Pruning and Re-grafting -- 6.3 Inferring Phylogeny from Multiple Sequence Alignments -- Chapter 7 Multiple Sequence Alignment on High-Performance Computing Models -- 7.1 Parallel Systems -- 7.1.1 Multiprocessor -- 7.1.2 Vector -- 7.1.3 GPU -- 7.1.4 FPGA -- 7.1.5 Reconfigurable Mesh -- 7.2 Exiting Parallel Multiple Sequence Alignment -- 7.3 Reconfigurable-Mesh Computing Models-(R-Mesh) -- 7.4 Pairwise Dynamic Programming Algorithms -- 7.4.1 R-Mesh Max Switches -- 7.4.2 R-Mesh Adder/Subtractor.

7.4.3 Constant-Time Dynamic Programming on R-Mesh -- 7.4.4 Affine Gap Cost -- 7.4.5 R-Mesh On/Off Switches -- 7.4.6 Dynamic Programming Backtracking on R-Mesh -- 7.5 Progressive Multiple Sequence Alignment ON R-Mesh -- 7.5.1 Hierarchical Clustering on R-Mesh -- 7.5.2 Constant Run-Time Sum-of-Pair Scoring Method -- 7.5.3 Parallel Progressive MSA Algorithm and Its Complexity Analysis -- Chapter 8 Sequence Analysis Services -- 8.1 EMBL-EBI: European Bioinformatics Institute -- 8.2 NCBI: National Center for Biotechnology Information -- 8.3 GenomeNet and Data Bank of Japan -- 8.4 Other Sequence Analysis and Alignment Web Servers -- 8.5 SeqAna: Multiple Sequence Alignment with Quality Ranking -- 8.6 Pairwise Sequence Alignment and Other Analysis Tools -- 8.7 Tool Evaluation -- Chapter 9 Multiple Sequence for Next-Generation Sequences -- 9.1 Introduction -- 9.2 Overview of Next Generation Sequence Alignment Algorithms -- 9.2.1 Alignment Algorithms Based on Seeding and Hash Tables -- 9.2.2 Alignment Algorithms Based on Suffix Tries -- 9.3 Next-Generation Sequencing Tools -- Chapter 10 Multiple Sequence Alignment for Variations Detection -- 10.1 Introduction -- 10.2 Genetic Variants -- 10.3 Variation Detection Methods Based on MSA -- 10.4 Evaluation Methodology -- 10.4.1 Performance Metrics -- 10.4.2 Simulated Sequence Data -- 10.4.3 Real Sequence Data -- 10.5 Conclusion and Future Work -- Chapter 11 Multiple Sequence Alignment for Structure Detection -- 11.1 Introduction -- 11.2 RNA Secondary Structure Prediction Based on MSA -- 11.2.1 Common Information in Multiple Aligned RNA Sequences -- 11.2.2 Review of RNA SS Prediction Methods -- 11.2.3 Measures of Quality of RNA SS Prediction -- 11.3 Protein Secondary Structure Prediction Based on MSA -- Problem (Protein SS Prediction Based on a Set of Aligned Sequences).

11.3.1 Review of Protein Secondary Structure Prediction Methods -- 11.3.2 Measures of Quality of Protein SS Prediction -- 11.4 Conclusion and Future Work -- References -- Index -- EULA.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.