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E-raamat: Distributed and Sequential Algorithms for Bioinformatics

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  • Formaat: PDF+DRM
  • Sari: Computational Biology 23
  • Ilmumisaeg: 31-Oct-2015
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319249667
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Distributed and Sequential Algorithms for BioinformaticsSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Computational Biology 23
  • Ilmumisaeg: 31-Oct-2015
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319249667
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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 algorithms in this area, more than fifteen new distributed algorithms are also proposed. Topics and features: reviews a range of open challenges in biological sequences and networks; describes in detail both sequential and parallel/distributed algorithms for each problem; suggests approaches for distributed algorithms as possible extensions to sequential algorithms, when the distributed algorithms for the topic are scarce; proposes a number of new distributed algorithms in each chapter, to serve as potential starting points for further research; concludes each chapter with self-test exercises, a summary of the key points, a comparison of the algorithms described, and a literature review

.

IntroductionPart I: BackgroundIntroduction to Molecular BiologyGraphs, Algorithms and ComplexityParallel and Distributed ComputingPart II: Biological SequencesString AlgorithmsSequence AlignmentClustering of Biological SequencesSequence RepeatsGenome AnalysisPart III: Biological NetworksAnalysis of Biological NetworksCluster Discovery in Biological NetworksNetwork Motif SearchNetwork AlignmentPhylogeneticsEpilogue

Arvustused

This volume covers a variety of algorithms-sequential, parallel, and distributed-towards the construction and analysis of biological sequences and networks. This volume is mostly suited to the computational biologist who wants to learn about the algorithms landscape, both sequential and distributed, both sequential and distributed . (Lenwood S. Heath, Mathematical Reviews, March, 2017)

Because of its clear layout and well-thought-out design, this book is suitable for students and researchers in computer science as well as in biology. It is my sincere hope that it will introduce new groups of researchers to these very important problems and motivate them to look for new and more efficient solutions to them. This book is an excellent resource for anyone with an interest in bioinformatics. (Burkhard Englert, Computing Reviews, computingreviews.com, September, 2016)

1 Introduction 1(10)
1.1 Introduction
1(1)
1.2 Biological Sequences
2(1)
1.3 Biological Networks
3(3)
1.4 The Need for Distributed Algorithms
6(1)
1.5 Outline of the Book
7(1)
Reference
8(3)
Part I Background
2 Introduction to Molecular Biology
11(16)
2.1 Introduction
11(1)
2.2 The Cell
12(4)
2.2.1 DNA
13(1)
2.2.2 RNA
14(1)
2.2.3 Genes
15(1)
2.2.4 Proteins
15(1)
2.3 Central Dogma of Life
16(4)
2.3.1 Transcription
17(1)
2.3.2 The Genetic Code
18(1)
2.3.3 Translation
18(1)
2.3.4 Mutations
19(1)
2.4 Biotechnological Methods
20(2)
2.4.1 Cloning
20(1)
2.4.2 Polymerase Chain Reaction
20(1)
2.4.3 DNA Sequencing
21(1)
2.5 Databases
22(1)
2.5.1 Nucleotide Databases
22(1)
2.5.2 Protein Sequence Databases
22(1)
2.6 Human Genome Project
23(1)
2.7
Chapter Notes
23(1)
References
24(3)
3 Graphs, Algorithms, and Complexity
27(24)
3.1 Introduction
27(1)
3.2 Graphs
27(6)
3.2.1 Types of Graphs
29(1)
3.2.2 Graph Representations
29(1)
3.2.3 Paths, Cycles, and Connectivity
30(2)
3.2.4 Trees
32(1)
3.2.5 Spectral Properties of Graphs
32(1)
3.3 Algorithms
33(10)
3.3.1 Time and Space Complexities
33(1)
3.3.2 Recurrences
34(1)
3.3.3 Fundamental Approaches
35(1)
3.3.4 Dynamic Programming
35(1)
3.3.5 Graph Algorithms
36(5)
3.3.6 Special Subgraphs
41(2)
3.4 NP-Completeness
43(4)
3.4.1 Reductions
44(1)
3.4.2 Coping with NP-Completeness
45(2)
3.5
Chapter Notes
47(2)
References
49(2)
4 Parallel and Distributed Computing
51(30)
4.1 Introduction
51(1)
4.2 Architectures for Parallel and Distributed Computing
52(2)
4.2.1 Interconnection Networks
52(1)
4.2.2 Multiprocessors and Multicomputers
53(1)
4.2.3 Flynn's Taxonomy
54(1)
4.3 Parallel Computing
54(14)
4.3.1 Complexity of Parallel Algorithms
55(1)
4.3.2 Parallel Random Access Memory Model
55(2)
4.3.3 Parallel Algorithm Design Methods
57(2)
4.3.4 Shared Memory Programming
59(4)
4.3.5 Multi-threaded Programming
63(3)
4.3.6 Parallel Processing in UNIX
66(2)
4.4 Distributed Computing
68(6)
4.4.1 Distributed Algorithm Design
69(1)
4.4.2 Threads Re-visited
69(1)
4.4.3 Message Passing Interface
70(3)
4.4.4 Distributed Processing in UNIX
73(1)
4.5
Chapter Notes
74(2)
References
76(5)
Part II Biological Sequences
5 String Algorithms
81(30)
5.1 Introduction
81(1)
5.2 Exact String Matching
82(9)
5.2.1 Sequential Algorithms
82(8)
5.2.2 Distributed String Matching
90(1)
5.3 Approximate String Matching
91(1)
5.4 Longest Subsequence Problems
92(4)
5.4.1 Longest Common Subsequence
92(3)
5.4.2 Longest Increasing Subsequence
95(1)
5.5 Suffix Trees
96(11)
5.5.1 Construction of Suffix Trees
97(5)
5.5.2 Applications of Suffix Trees
102(2)
5.5.3 Suffix Arrays
104(3)
5.6
Chapter Notes
107(2)
References
109(2)
6 Sequence Alignment
111(24)
6.1 Introduction
111(1)
6.2 Problem Statement
112(3)
6.2.1 The Objective Function
112(2)
6.2.2 Scoring Matrices for Proteins
114(1)
6.3 Pairwise Alignment
115(5)
6.3.1 Global Alignment
115(3)
6.3.2 Local Alignment
118(2)
6.4 Multiple Sequence Alignment
120(3)
6.4.1 Center Star Method
121(1)
6.4.2 Progressive Alignment
122(1)
6.5 Alignment with Suffix Trees
123(1)
6.6 Database Search
124(2)
6.6.1 FASTA
124(1)
6.6.2 BLAST
125(1)
6.7 Parallel and Distributed Sequence Alignment
126(4)
6.7.1 Parallel and Distributed SW Algorithm
126(1)
6.7.2 Distributed BLAST
127(2)
6.7.3 Parallel/Distributed CLUSTALW
129(1)
6.8
Chapter Notes
130(2)
References
132(3)
7 Clustering of Biological Sequences
135(26)
7.1 Introduction
135(1)
7.2 Analysis
136(2)
7.2.1 Distance and Similarity Measures
136(1)
7.2.2 Validation of Cluster Quality
137(1)
7.3 Classical Methods
138(6)
7.3.1 Hierarchical Algorithms
138(2)
7.3.2 Partitional Algorithms
140(3)
7.3.3 Other Methods
143(1)
7.4 Clustering Algorithms Targeting Biological Sequences
144(2)
7.4.1 Alignment-Based Clustering
144(1)
7.4.2 Other Similarity-Based Methods
144(1)
7.4.3 Graph-Based Clustering
145(1)
7.5 Distributed Clustering
146(10)
7.5.1 Hierarchical Clustering
146(6)
7.5.2 k-means Clustering
152(2)
7.5.3 Graph-Based Clustering
154(1)
7.5.4 Review of Existing Algorithms
155(1)
7.6
Chapter Notes
156(3)
References
159(2)
8 Sequence Repeats
161(22)
8.1 Introduction
161(2)
8.2 Tandem Repeats
163(3)
8.2.1 Stoye and Gusfield Algorithm
164(2)
8.2.2 Distributed Tandem Repeat Search
166(1)
8.3 Sequence Motifs
166(13)
8.3.1 Probabilistic Approaches
169(2)
8.3.2 Combinatorial Methods
171(3)
8.3.3 Parallel and Distributed Motif Search
174(4)
8.3.4 A Survey of Recent Distributed Algorithms
178(1)
8.4
Chapter Notes
179(2)
References
181(2)
9 Genome Analysis
183(30)
9.1 Introduction
183(1)
9.2 Gene Finding
184(6)
9.2.1 Fundamental Methods
185(1)
9.2.2 Hidden Markov Models
186(1)
9.2.3 Nature Inspired Methods
187(2)
9.2.4 Distributed Gene Finding
189(1)
9.3 Genome Rearrangement
190(10)
9.3.1 Sorting by Reversals
191(2)
9.3.2 Unsigned Reversals
193(3)
9.3.3 Signed Reversals
196(3)
9.3.4 Distributed Genome Rearrangement Algorithms
199(1)
9.4 Haplotype Inference
200(6)
9.4.1 Problem Statement
202(1)
9.4.2 Clark's Algorithm
202(1)
9.4.3 EM Algorithm
203(1)
9.4.4 Distributed Haplotype Inference Algorithms
204(2)
9.5
Chapter Notes
206(2)
References
208(5)
Part III Biological Networks
10 Analysis of Biological Networks
213(28)
10.1 Introduction
213(1)
10.2 Networks in the Cell
214(3)
10.2.1 Metabolic Networks
214(1)
10.2.2 Gene Regulation Networks
215(1)
10.2.3 Protein Interaction Networks
216(1)
10.3 Networks Outside the Cell
217(4)
10.3.1 Networks of the Brain
217(2)
10.3.2 Phylogenetic Networks
219(1)
10.3.3 The Food Web
220(1)
10.4 Properties of Biological Networks
221(3)
10.4.1 Distance
221(1)
10.4.2 Vertex Degrees
222(1)
10.4.3 Clustering Coefficient
223(1)
10.4.4 Matching Index
223(1)
10.5 Centrality
224(6)
10.5.1 Degree Centrality
224(1)
10.5.2 Closeness Centrality
225(1)
10.5.3 Betweenness Centrality
225(4)
10.5.4 Eigenvalue Centrality
229(1)
10.6 Network Models
230(4)
10.6.1 Random Networks
230(1)
10.6.2 Small World Networks
231(1)
10.6.3 Scale-Free Networks
232(1)
10.6.4 Hierarchical Networks
233(1)
10.7 Module Detection
234(1)
10.8 Network Motifs
235(1)
10.9 Network Alignment
235(1)
10.10
Chapter Notes
236(3)
References
239(2)
11 Cluster Discovery in Biological Networks
241(34)
11.1 Introduction
241(1)
11.2 Analysis
242(4)
11.2.1 Quality Metrics
242(3)
11.2.2 Classification of Clustering Algorithms
245(1)
11.3 Hierarchical Clustering
246(6)
11.3.1 MST-Based Clustering
247(3)
11.3.2 Edge-Betweenness-Based Clustering
250(2)
11.4 Density-Based Clustering
252(11)
11.4.1 Clique Algorithms
252(2)
11.4.2 k-core Decomposition
254(4)
11.4.3 Highly Connected Subgraphs Algorithm
258(1)
11.4.4 Modularity-Based Clustering
259(4)
11.5 Flow Simulation-Based Approaches
263(4)
11.5.1 Markov Clustering Algorithm
263(2)
11.5.2 Distributed Markov Clustering Algorithm Proposal
265(2)
11.6 Spectral Clustering
267(2)
11.7
Chapter Notes
269(3)
References
272(3)
12 Network Motif Search
275(28)
12.1 Introduction
275(1)
12.2 Problem Statement
276(5)
12.2.1 Methods of Motif Discovery
277(1)
12.2.2 Relation to Graph Isomorphism
278(1)
12.2.3 Frequency Concepts
279(1)
12.2.4 Random Graph Generation
280(1)
12.2.5 Statistical Significance
280(1)
12.3 A Review of Sequential Motif Searching Algorithms
281(10)
12.3.1 Network Centric Algorithms
282(4)
12.3.2 Motif Centric Algorithms
286(5)
12.4 Distributed Motif Discovery
291(8)
12.4.1 A General Framework
291(1)
12.4.2 Review of Distributed Motif Searching Algorithms
292(1)
12.4.3 Wang et al.'s Algorithm
292(2)
12.4.4 Schatz et al.'s Algorithm
294(1)
12.4.5 Riberio et al.'s Algorithms
294(5)
12.5
Chapter Notes
299(2)
References
301(2)
13 Network Alignment
303(20)
13.1 Introduction
303(1)
13.2 Problem Statement
304(4)
13.2.1 Relation to Graph Isomorphism
304(1)
13.2.2 Relation to Bipartite Graph Matching
305(1)
13.2.3 Evaluation of Alignment Quality
305(2)
13.2.4 Network Alignment Methods
307(1)
13.3 Review of Sequential Network Alignment Algorithms
308(3)
13.3.1 PathBlast
308(1)
13.3.2 IsoRank
309(1)
13.3.3 MaWIsh
309(1)
13.3.4 GRAAL
310(1)
13.3.5 Recent Algorithms
310(1)
13.4 Distributed Network Alignment
311(7)
13.4.1 A Distributed Greedy Approximation Algorithm Proposal
311(3)
13.4.2 Distributed Hoepman's Algorithm
314(2)
13.4.3 Distributed Auction Algorithms
316(2)
13.5
Chapter Notes
318(2)
References
320(3)
14 Phylogenetics
323(28)
14.1 Introduction
323(1)
14.2 Terminology
324(1)
14.3 Phylogenetic Trees
325(18)
14.3.1 Distance-Based Algorithms
326(9)
14.3.2 Maximum Parsimony
335(7)
14.3.3 Maximum Likelihood
342(1)
14.4 Phylogenetic Networks
343(2)
14.4.1 Reconstruction Methods
344(1)
14.5
Chapter Notes
345(3)
References
348(3)
15 Epilogue
351(12)
15.1 Introduction
351(1)
15.2 Current Challenges
352(4)
15.2.1 Big Data Analysis
352(1)
15.2.2 Disease Analysis
353(2)
15.2.3 Bioinformatics Education
355(1)
15.3 Specific Challenges
356(4)
15.3.1 Sequence Analysis
356(1)
15.3.2 Network Analysis
357(3)
15.4 Future Directions
360(2)
15.4.1 Big Data Gets Bigger
360(1)
15.4.2 New Paradigms on Disease Analysis
360(1)
15.4.3 Personalized Medicine
361(1)
References
362(1)
Index 363
Dr. K. Erciyes is Rector of Izmir University, Turkey, where he also serves as a professor in the Computer Engineering Department. His other publications include the Springer title Distributed Graph Algorithms for Computer Networks.
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