9780262101066

An Introduction to Bioinformatics Algorithms

by
  • ISBN13:

    9780262101066

  • ISBN10:

    0262101068

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 8/1/2004
  • Publisher: MIT PRESS

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Summary

This introductory text offers a clear exposition of the algorithmic principles driving advances in bioinformatics. Accessible to students in both biology and computer science, it strikes a unique balance between rigorous mathematics and practical techniques, emphasizing the ideas underlying algorithms rather than offering a collection of apparently unrelated problems. The book introduces biological and algorithmic ideas together, linking issues in computer science to biology and thus capturing the interest of students in both subjects. It demonstrates that relatively few design techniques can be used to solve a large number of practical problems in biology, and presents this material intuitively. An Introduction to Bioinformatics Algorithmsis one of the first books on bioinformatics that can be used by students at an undergraduate level. It includes a dual table of contents, organized by algorithmic idea and biological idea; discussions of biologically relevant problems, including a detailed problem formulation and one or more solutions for each; and brief biographical sketches of leading figures in the field. These interesting vignettes offer students a glimpse of the inspirations and motivations for real work in bioinformatics, making the concepts presented in the text more concrete and the techniques more approachable. PowerPoint presentations, practical bioinformatics problems, sample code, diagrams, demonstrations, and other materials can be found at the Author's website.

Table of Contents

Preface xv
Introduction
1(6)
Algorithms and Complexity
7(50)
What Is an Algorithm?
7(7)
Biological Algorithms versus Computer Algorithms
14(3)
The Change Problem
17(3)
Correct versus Incorrect Algorithms
20(4)
Recursive Algorithms
24(4)
Iterative versus Recursive Algorithms
28(5)
Fast versus Slow Algorithms
33(4)
Big-O Notation
37(3)
Algorithm Design Techniques
40(9)
Exhaustive Search
41(1)
Branch-and-Bound Algorithms
42(1)
Greedy Algorithms
43(1)
Dynamic Programming
43(5)
Divide-and-Conquer Algorithms
48(1)
Machine Learning
48(1)
Randomized Algorithms
48(1)
Tractable versus Intractable Problems
49(2)
Notes
51(3)
Biobox: Richard Karp
52(2)
Problems
54(3)
Molecular Biology Primer
57(26)
What Is Life Made Of?
57(2)
What Is the Genetic Material?
59(1)
What Do Genes Do?
60(1)
What Molecule Codes for Genes?
61(1)
What Is the Structure of DNA?
61(2)
What Carries Information between DNA and Proteins?
63(2)
How Are Proteins Made?
65(2)
How Can We Analyze DNA?
67(6)
Copying DNA
67(4)
Cutting and Pasting DNA
71(1)
Measuring DNA Length
72(1)
Probing DNA
72(1)
How Do Individuals of a Species Differ?
73(1)
How Do Different Species Differ?
74(1)
Why Bioinformatics?
75(8)
Biobox: Russell Doolittle
79(4)
Exhaustive Search
83(42)
Restriction Mapping
83(4)
Impractical Restriction Mapping Algorithms
87(2)
A Practical Restriction Mapping Algorithm
89(2)
Regulatory Motifs in DNA Sequences
91(2)
Profiles
93(4)
The Motif Finding Problem
97(3)
Search Trees
100(8)
Finding Motifs
108(3)
Finding a Median String
111(3)
Notes
114(5)
Biobox: Gary Stormo
116(3)
Problems
119(6)
Greedy Algorithms
125(22)
Genome Rearrangements
125(2)
Sorting by Reversals
127(4)
Approximation Algorithms
131(1)
Breakpoints: A Different Face of Greed
132(4)
A Greedy Approach to Motif Finding
136(1)
Notes
137(6)
Biobox: David Sankoff
139(4)
Problems
143(4)
Dynamic Programming Algorithms
147(80)
The Power of DNA Sequence Comparison
147(1)
The Change Problem Revisited
148(5)
The Manhattan Tourist Problem
153(14)
Edit Distance and Alignments
167(5)
Longest Common Subsequences
172(5)
Global Sequence Alignment
177(1)
Scoring Alignments
178(2)
Local Sequence Alignment
180(4)
Alignment with Gap Penalties
184(1)
Multiple Alignment
185(8)
Gene Prediction
193(4)
Statistical Approaches to Gene Prediction
197(3)
Similarity-Based Approaches to Gene Prediction
200(3)
Spliced Alignment
203(4)
Notes
207(4)
Biobox: Michael Waterman
209(2)
Problems
211(16)
Divide-and-Conquer Algorithms
227(20)
Divide-and-Conquer Approach to Sorting
227(3)
Space-Efficient Sequence Alignment
230(4)
Block Alignment and the Four-Russians Speedup
234(4)
Constructing Alignments in Subquadratic Time
238(2)
Notes
240(4)
Biobox: Webb Miller
241(3)
Problems
244(3)
Graph Algorithms
247(64)
Graphs
247(13)
Graphs and Genetics
260(2)
DNA Sequencing
262(2)
Shortest Superstring Problem
264(1)
DNA Arrays as an Alternative Sequencing Technique
265(3)
Sequencing by Hybridization
268(3)
SBH as a Hamiltonian Path Problem
271(1)
SBH as an Eulerian Path Problem
272(3)
Fragment Assembly in DNA Sequencing
275(5)
Protein Sequencing and Identification
280(4)
The Peptide Sequencing Problem
284(3)
Spectrum Graphs
287(3)
Protein Identification via Database Search
290(2)
Spectral Convolution
292(1)
Spectral Alignment
293(6)
Notes
299(3)
Problems
302(9)
Combinatorial Pattern Matching
311(28)
Repeat Finding
311(2)
Hash Tables
313(3)
Exact Pattern Matching
316(2)
Keyword Trees
318(2)
Suffix Trees
320(4)
Heuristic Similarity Search Algorithms
324(2)
Approximate Pattern Matching
326(4)
BLAST: Comparing a Sequence against a Database
330(1)
Notes
331(6)
Biobox: Gene Myers
333(4)
Problems
337(2)
Clustering and Trees
339(48)
Gene Expression Analysis
339(4)
Hierarchical Clustering
343(3)
k-Means Clustering
346(2)
Clustering and Corrupted Cliques
348(6)
Evolutionary Trees
354(4)
Distance-Based Tree Reconstruction
358(3)
Reconstructing Trees from Additive Matrices
361(5)
Evolutionary Trees and Hierarchical Clustering
366(2)
Character-Based Tree Reconstruction
368(2)
Small Parsimony Problem
370(4)
Large Parsimony Problem
374(5)
Notes
379(5)
Biobox: Ron Shamir
380(4)
Problems
384(3)
Hidden Markov Models
387(22)
CG-Islands and the ``Fair Bet Casino''
387(3)
The Fair Bet Casino and Hidden Markov Models
390(3)
Decoding Algorithm
393(4)
HMM Parameter Estimation
397(1)
Profile HMM Alignment
398(2)
Notes
400(7)
Biobox: David Haussler
403(4)
Problems
407(2)
Randomized Algorithms
409(10)
The Sorting Problem Revisited
409(3)
Gibbs Sampling
412(2)
Random Projections
414(2)
Notes
416(1)
Problems
417(2)
Using Bioinformatics Tools 419(2)
Bibliography 421(8)
Index 429

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