Presents algorithmic techniques for solving problems in
bioinformatics, including applications that shed new light on
molecular biology
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.
Зміст
Preface ix
Contributors xi
1 Educating Biologists in the 21st Century: Bioinformatics Scientists versus Bioinformatics Technicians 1
Pavel Pevzner
Part I Techniques 7
2 Dynamic Programming Algorithms for Biological Sequence and Structure Comparison 9
Yuzhen Ye and Haixu Tang
3 Graph Theoretical Approaches to Delineate Dynamics of Biological Processes 29
Teresa M. Przytycka and Elena Zotenko
4 Advances in Hidden Markov Models for Sequence Annotation 55
Brona Brejová, Daniel G. Brown, and Tomás Vinar
5 Sorting- and FFT-Based Techniques in the Discovery of Biopatterns 93
Sudha Balla, Sanguthevar Rajasekaran, and Jaime Davila
6 A Survey of Seeding for Sequence Alignment 117
Daniel G. Brown
7 The Comparison of Phylogenetic Networks: Algorithms and Complexity 143
Paola Bonizzoni, Gianluca Della Vedova, Riccardo Dondi, and Giancarlo Mauri
Part II Genome and Sequence Analysis 175
8 Formal Models of Gene Clusters 177
Anne Bergeron, Cedric Chauve, and Yannick Gingras
9 Integer Linear Programming Techniques for Discovering Approximate Gene Clusters 203
Sven Rahmann and Gunnar W. Klau
10 Efficient Combinatorial Algorithms for DNA Sequence Processing 223
Bhaskar Das Gupta and Ming-Yang Kao
11 Algorithms for Multiplex PCR Primer Set Selection with Amplification Length Constraints 241
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 259
Sing-Hoi Sze
Part III Microarray Design and Data Analysis 277
13 Algorithms for Oligonucleotide Microarray Layout 279
Sérgio A. De Carvalho Jr. and Sven Rahmann
14 Classification Accuracy Based Microarray Missing Value Imputation 303
Yi Shi, Zhipeng Cai, and Guohui Lin
15 Meta-Analysis of Microarray Data 329
Saumyadipta Pyne, Steve Skiena, and Bruce Futcher
Part IV Genetic Variation Analysis 353
16 Phasing Genotypes Using a Hidden Markov Model 355
P. Rastas, M. Koivisto, H. Mannila, and E. Ukkonen
17 Analytical and Algorithmic Methods for Haplotype Frequency Inference: What Do They Tell Us? 373
Steven Hecht Orzack, Daniel Gusfield, Lakshman Subrahmanyan, Laurent Essioux, and Sebastien Lissarrague
18 Optimization Methods for Genotype Data Analysis in Epidemiological Studies 395
Dumitru Brinza, Jingwu He, and Alexander Zelikovsky
Part V Structural and Systems Biology 417
19 Topological Indices in Combinatorial Chemistry 419
Sergey Bereg
20 Efficient Algorithms for Structural Recall in Databases 439
Hao Wang, Patra Volarath, and Robert W. Harrison
21 Computational Approaches to Predict Protein-Protein and Domain-Domain Interactions 465
Raja Jothi and Teresa M. Przytycka
Index 493
Про автора
Alexander Zelikovsky, Ph D, 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.
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