Bioinformatics Algorithms: Techniques and Applications
This book introduces algorithmic techniques in bioinformatics, emphasizing their application to solving novel problems in post-genomic molecular biology. Beginning with a thought-provoking discussion on the role of algorithms in twenty-first-century bioinformatics education, Bioinformatics Algorithms covers:
General algorithmic techniques, including dynamic programming, graph-theoretical methods, hidden Markov models, the fast Fourier transform, seeding, and approximation algorithms
Algorithms and tools for genome and sequence analysis, including formal and approximate models for gene clusters, advanced algorithms for non-overlapping local alignments and genome tilings, multiplex PCR primer set selection, and sequence/network motif finding
Microarray design and analysis, including algorithms for microarray physical design, missing value imputation, and meta-analysis of gene expression data
Algorithmic issues arising in the analysis of genetic variation across human population, including computational inference of haplotypes from genotype data and disease association search in case/control epidemiologic studies
Algorithmic approaches in structural and systems biology, including topological and structural classification in biochemistry, and prediction of protein-protein and domain-domain interactions
Each chapter begins with a self-contained introduction to a computational problem; continues with a brief review of the existing literature on the subject and an in-depth description of recent algorithmic and methodological developments; and concludes with a brief experimental study and a discussion of open research challenges. This clear and approachable presentation makes the book appropriate for researchers, practitioners, and graduate students alike.
1. Educating Biologists in the 21st century: Bioinformatics Scientists versus Bioinformatics Technicians (Pavel Pevznher)
Part I: Techniques.
2. Dynamic Programming Algorithms for Biological Sequence and Structure Comparison (Yuzhen Ye and Haixu Tang)
3. Graph Theoretical Approaches to Delineate Dynamics of biological Processes (Teresa M. Przytycka and Elena Zotenko)
4. Advances in Hidden Markov Models for Sequence Annotation (Brona Brejova, Daniel G. Brown, and Tomas Vinar)
5. Sorting- and FFT-Based Techniques in the Discovery of Biopatterns (Sudha Balla, Sanguthevar Rajasekaran, and Jaime Davila)
6. A Survey of Seeding for Sequence Alignment (Daniel G. Brown)
7. The Comparison of Phylogenetic Networks: Algorithms and complexity (Paola Bonizzoni, Gianluca Della Vedova, Riccardo Dondi, and Giancarlo Mauri)
Part II: Genome and Sequence Analysis.
8. Formal Models of Gene Clusters (Anne Bergeron, Cedric Chauve,and Yannick Gingras)
9. Integer Linear Programming Techniques for Discovering Approximate gene Clusters (Sven Rahmann and Gunnar W. Klau)
10. Efficient Combinatorial Algorithms for DNA Sequence Processing (Bhaskar DasGupta and Ming-Yang Kao)
11. Algorithms for Multiplex PCR Primer Set Selection with Amplification Length constraints (K.M. Konwar, I.I. Mandoiu, A.C. Russell, and A.A. Shvartsman)
12. Recent Developments in Alignment and Motif Finding for Sequences and Networks (Sing-Hoi Sze)
Part III: Microarray Design and Data Analysis.
13. Algorithms for Oligonucleotide Microarray Layout (Sergio A. De Carvalho Jr. Sven Rahmann)
14. Classification Accuracy Based Microarray Missing Value Imputation (Yi Shi, Zhipeng Cai, and Guohui Lin)
15. Meta-Analysis of Microarray Data (Saumyadipta Pyne, Steve Skiena, and Bruce Futcher)
Part IV: Genetic Variation Analysis.
16. Phasing Genotypes Using a Hidden Markov Model (P. Rastas, M. Koivisto, H. Mannila, and Ukkonen)
17. Analytical and Algorithmic Methods for Haplotype Frequency Inference: What Do they Tell Us? (Steven Hecth Orzack, Daniel Gusfield, Lakshamn Subrahmanyan, Laurent Essioux, and Sebastein Lissarrague)
18. Optimization Methods for Genotype Data Analysis in Epidemiological Studies (Dumitru Brinza, Jingwu He, and Alexander Zelikovsky)
Part V: Structural and Systems Biology.
19. Topological Indices in Combinatorial Chemistry (Sergey Bereg)
20. Efficient Algorithms for Structural Recall in Databases (Hao Wang, Patra Volarath, and Robert W. Harrison)
21. Computational Approaches to Predict Protein-Protein and Domain-Domain Interactions (Raja Jothi and Teresa M. Przytycka)
Alexander Zelikovsky, PhD, is Associate Professor in the Computer Science Department at Georgia State University. His research focuses on discrete algorithms and their applications in bio-technology, bioinformatics, VLSI computer-aided design, and wireless networks.
"This clear and approachable presentation makes the book appropriate for researchers, practioners, and graduate students." (Mathematical Reviews, Issue 2009b)
"This volume will be a nice addition to the bioinformatician's bookshelf." (Quarterly Review of Biology, December 2008)
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