Distributed and Sequential Algorithms for Bioinformatics

This unique textbook/reference presents unified coverage of bioinformatics topics relating to both biological sequences and biological networks, providing an in-depth analysis of cutting-edge distributed algorithms, as well as of relevant sequential algorithms. In addition to introducing the latest...

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Bibliographic Details
Main Author Erciyes, Kayhan
Format eBook
LanguageEnglish
Published Cham Springer Nature 2015
Springer International Publishing AG
Springer International Publishing
Edition1
SeriesComputational Biology
Subjects
Algorithm Analysis and Problem Complexity
Bioinformatics
Computational Biology/Bioinformatics
Computer Science
Data processing Computer science
Distributed algorithms
Math Applications in Computer Science
Systems Biology
Online AccessGet full text
ISBN3319249665
9783319249667
9783319249643
3319249649
ISSN1568-2684
DOI10.1007/978-3-319-24966-7

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Table of Contents:
  • 8.3 Sequence Motifs -- 8.3.1 Probabilistic Approaches -- 8.3.2 Combinatorial Methods -- 8.3.3 Parallel and Distributed Motif Search -- 8.3.4 A Survey of Recent Distributed Algorithms -- 8.4 Chapter Notes -- 9 Genome Analysis -- 9.1 Introduction -- 9.2 Gene Finding -- 9.2.1 Fundamental Methods -- 9.2.2 Hidden Markov Models -- 9.2.3 Nature Inspired Methods -- 9.2.4 Distributed Gene Finding -- 9.3 Genome Rearrangement -- 9.3.1 Sorting by Reversals -- 9.3.2 Unsigned Reversals -- 9.3.3 Signed Reversals -- 9.3.4 Distributed Genome Rearrangement Algorithms -- 9.4 Haplotype Inference -- 9.4.1 Problem Statement -- 9.4.2 Clark's Algorithm -- 9.4.3 EM Algorithm -- 9.4.4 Distributed Haplotype Inference Algorithms -- 9.5 Chapter Notes -- Part IIIBiological Networks -- 10 Analysis of Biological Networks -- 10.1 Introduction -- 10.2 Networks in the Cell -- 10.2.1 Metabolic Networks -- 10.2.2 Gene Regulation Networks -- 10.2.3 Protein Interaction Networks -- 10.3 Networks Outside the Cell -- 10.3.1 Networks of the Brain -- 10.3.2 Phylogenetic Networks -- 10.3.3 The Food Web -- 10.4 Properties of Biological Networks -- 10.4.1 Distance -- 10.4.2 Vertex Degrees -- 10.4.3 Clustering Coefficient -- 10.4.4 Matching Index -- 10.5 Centrality -- 10.5.1 Degree Centrality -- 10.5.2 Closeness Centrality -- 10.5.3 Betweenness Centrality -- 10.5.4 Eigenvalue Centrality -- 10.6 Network Models -- 10.6.1 Random Networks -- 10.6.2 Small World Networks -- 10.6.3 Scale-Free Networks -- 10.6.4 Hierarchical Networks -- 10.7 Module Detection -- 10.8 Network Motifs -- 10.9 Network Alignment -- 10.10 Chapter Notes -- 11 Cluster Discovery in Biological Networks -- 11.1 Introduction -- 11.2 Analysis -- 11.2.1 Quality Metrics -- 11.2.2 Classification of Clustering Algorithms -- 11.3 Hierarchical Clustering -- 11.3.1 MST-Based Clustering -- 11.3.2 Edge-Betweenness-Based Clustering
  • 4.4.4 Distributed Processing in UNIX -- 4.5 Chapter Notes -- Part IIBiological Sequences -- 5 String Algorithms -- 5.1 Introduction -- 5.2 Exact String Matching -- 5.2.1 Sequential Algorithms -- 5.2.2 Distributed String Matching -- 5.3 Approximate String Matching -- 5.4 Longest Subsequence Problems -- 5.4.1 Longest Common Subsequence -- 5.4.2 Longest Increasing Subsequence -- 5.5 Suffix Trees -- 5.5.1 Construction of Suffix Trees -- 5.5.2 Applications of Suffix Trees -- 5.5.3 Suffix Arrays -- 5.6 Chapter Notes -- 6 Sequence Alignment -- 6.1 Introduction -- 6.2 Problem Statement -- 6.2.1 The Objective Function -- 6.2.2 Scoring Matrices for Proteins -- 6.3 Pairwise Alignment -- 6.3.1 Global Alignment -- 6.3.2 Local Alignment -- 6.4 Multiple Sequence Alignment -- 6.4.1 Center Star Method -- 6.4.2 Progressive Alignment -- 6.5 Alignment with Suffix Trees -- 6.6 Database Search -- 6.6.1 FASTA -- 6.6.2 BLAST -- 6.7 Parallel and Distributed Sequence Alignment -- 6.7.1 Parallel and Distributed SW Algorithm -- 6.7.2 Distributed BLAST -- 6.7.3 Parallel/Distributed CLUSTALW -- 6.8 Chapter Notes -- 7 Clustering of Biological Sequences -- 7.1 Introduction -- 7.2 Analysis -- 7.2.1 Distance and Similarity Measures -- 7.2.2 Validation of Cluster Quality -- 7.3 Classical Methods -- 7.3.1 Hierarchical Algorithms -- 7.3.2 Partitional Algorithms -- 7.3.3 Other Methods -- 7.4 Clustering Algorithms Targeting Biological Sequences -- 7.4.1 Alignment-Based Clustering -- 7.4.2 Other Similarity-Based Methods -- 7.4.3 Graph-Based Clustering -- 7.5 Distributed Clustering -- 7.5.1 Hierarchical Clustering -- 7.5.2 k-means Clustering -- 7.5.3 Graph-Based Clustering -- 7.5.4 Review of Existing Algorithms -- 7.6 Chapter Notes -- 8 Sequence Repeats -- 8.1 Introduction -- 8.2 Tandem Repeats -- 8.2.1 Stoye and Gusfield Algorithm -- 8.2.2 Distributed Tandem Repeat Search
  • 15.1 Introduction -- 15.2 Current Challenges -- 15.2.1 Big Data Analysis -- 15.2.2 Disease Analysis -- 15.2.3 Bioinformatics Education -- 15.3 Specific Challenges -- 15.3.1 Sequence Analysis -- 15.3.2 Network Analysis -- 15.4 Future Directions -- 15.4.1 Big Data Gets Bigger -- 15.4.2 New Paradigms on Disease Analysis -- 15.4.3 Personalized Medicine -- Index
  • Intro -- Preface -- Contents -- 1 Introduction -- 1.1 Introduction -- 1.2 Biological Sequences -- 1.3 Biological Networks -- 1.4 The Need for Distributed Algorithms -- 1.5 Outline of the Book -- Part IBackground -- 2 Introduction to Molecular Biology -- 2.1 Introduction -- 2.2 The Cell -- 2.2.1 DNA -- 2.2.2 RNA -- 2.2.3 Genes -- 2.2.4 Proteins -- 2.3 Central Dogma of Life -- 2.3.1 Transcription -- 2.3.2 The Genetic Code -- 2.3.3 Translation -- 2.3.4 Mutations -- 2.4 Biotechnological Methods -- 2.4.1 Cloning -- 2.4.2 Polymerase Chain Reaction -- 2.4.3 DNA Sequencing -- 2.5 Databases -- 2.5.1 Nucleotide Databases -- 2.5.2 Protein Sequence Databases -- 2.6 Human Genome Project -- 2.7 Chapter Notes -- 3 Graphs, Algorithms, and Complexity -- 3.1 Introduction -- 3.2 Graphs -- 3.2.1 Types of Graphs -- 3.2.2 Graph Representations -- 3.2.3 Paths, Cycles, and Connectivity -- 3.2.4 Trees -- 3.2.5 Spectral Properties of Graphs -- 3.3 Algorithms -- 3.3.1 Time and Space Complexities -- 3.3.2 Recurrences -- 3.3.3 Fundamental Approaches -- 3.3.4 Dynamic Programming -- 3.3.5 Graph Algorithms -- 3.3.6 Special Subgraphs -- 3.4 NP-Completeness -- 3.4.1 Reductions -- 3.4.2 Coping with NP-Completeness -- 3.5 Chapter Notes -- 4 Parallel and Distributed Computing -- 4.1 Introduction -- 4.2 Architectures for Parallel and Distributed Computing -- 4.2.1 Interconnection Networks -- 4.2.2 Multiprocessors and Multicomputers -- 4.2.3 Flynn's Taxonomy -- 4.3 Parallel Computing -- 4.3.1 Complexity of Parallel Algorithms -- 4.3.2 Parallel Random Access Memory Model -- 4.3.3 Parallel Algorithm Design Methods -- 4.3.4 Shared Memory Programming -- 4.3.5 Multi-threaded Programming -- 4.3.6 Parallel Processing in UNIX -- 4.4 Distributed Computing -- 4.4.1 Distributed Algorithm Design -- 4.4.2 Threads Re-visited -- 4.4.3 Message Passing Interface
  • 11.4 Density-Based Clustering -- 11.4.1 Clique Algorithms -- 11.4.2 k-core Decomposition -- 11.4.3 Highly Connected Subgraphs Algorithm -- 11.4.4 Modularity-Based Clustering -- 11.5 Flow Simulation-Based Approaches -- 11.5.1 Markov Clustering Algorithm -- 11.5.2 Distributed Markov Clustering Algorithm Proposal -- 11.6 Spectral Clustering -- 11.7 Chapter Notes -- 12 Network Motif Search -- 12.1 Introduction -- 12.2 Problem Statement -- 12.2.1 Methods of Motif Discovery -- 12.2.2 Relation to Graph Isomorphism -- 12.2.3 Frequency Concepts -- 12.2.4 Random Graph Generation -- 12.2.5 Statistical Significance -- 12.3 A Review of Sequential Motif Searching Algorithms -- 12.3.1 Network Centric Algorithms -- 12.3.2 Motif Centric Algorithms -- 12.4 Distributed Motif Discovery -- 12.4.1 A General Framework -- 12.4.2 Review of Distributed Motif Searching Algorithms -- 12.4.3 Wang et al.'s Algorithm -- 12.4.4 Schatz et al.'s Algorithm -- 12.4.5 Riberio et al.'s Algorithms -- 12.5 Chapter Notes -- 13 Network Alignment -- 13.1 Introduction -- 13.2 Problem Statement -- 13.2.1 Relation to Graph Isomorphism -- 13.2.2 Relation to Bipartite Graph Matching -- 13.2.3 Evaluation of Alignment Quality -- 13.2.4 Network Alignment Methods -- 13.3 Review of Sequential Network Alignment Algorithms -- 13.3.1 PathBlast -- 13.3.2 IsoRank -- 13.3.3 MaWIsh -- 13.3.4 GRAAL -- 13.3.5 Recent Algorithms -- 13.4 Distributed Network Alignment -- 13.4.1 A Distributed Greedy Approximation Algorithm Proposal -- 13.4.2 Distributed Hoepman's Algorithm -- 13.4.3 Distributed Auction Algorithms -- 13.5 Chapter Notes -- 14 Phylogenetics -- 14.1 Introduction -- 14.2 Terminology -- 14.3 Phylogenetic Trees -- 14.3.1 Distance-Based Algorithms -- 14.3.2 Maximum Parsimony -- 14.3.3 Maximum Likelihood -- 14.4 Phylogenetic Networks -- 14.4.1 Reconstruction Methods -- 14.5 Chapter Notes -- 15 Epilogue