Bioinformatics : a computing perspective

Title
Contents
Chapter 1 Road Map
	1.1 What Is Bioinformatics?
	1.2 A Bioinformatics Team
	1.3 What Defines Life?
	1.4 A Systems Approach to Biology
	1.5 Bioinformatics in Action
		1.5.1 Deciphering a Killer: HIV and Bioinformatics
	1.6 The Road Ahead
	Summary
	Key Terms
	Bibliography
Chapter 2 Biological Basics
	2.1 The Blind Engineer
		2.1.1 The Case of the Peppered Moth
		2.1.2 How Evolution Works
		2.1.3 Evolution’s Palette
	2.2 Compute Machine par Excellence
		2.2.1 Cellular Organization and Complexity
		2.2.2 Chemistry and Life
		2.2.3 A Parts List for Life
	2.3 The Languages of the Cell
		2.3.1 Operating Systems for Cells
		2.3.2 Deciphering the Language of Cells
		2.3.3 Compiling DNAStrings Programs
		2.3.4 Executing Code from DNAStrings
	2.4 Further Nuances in DNAStrings
	2.5 Proteins: Cellular Machines
		2.5.1 Proteins as Molecules
		2.5.2 Proteins as Engineered Machines
	2.6 Data Maintenance and Integrity Tasks
		2.6.1 Backing up DNA Data
		2.6.2 The Challenges of Data Management
	Key Terms
	Bibliography
Chapter 3 Wet and Dry Lab Techniques
	3.1 Hybridization: Putting Base Pairs to Work
	3.2 Making Copies of Nucleotide Sequences
	3.3 An Explosion of Copies
	3.4 Sequencing DNA Strings
	3.5 The Human Genome Project: Computing to the Rescue
		3.5.1 Mission Impossible: Sequencing the Human Genome
	3.6 Human Genome Sequencing Strategies
	3.7 From Structure to Function
	3.8 Profiling the Transcriptome
	3.9 A Few Proteomics Techniques
	3.10 Putting It All Together
	3.11 A Few Selected Dry Lab Techniques
		3.11.1 Algorithms
		3.11.2 Analysis
	Key Terms
	Bibliography
Chapter 4 Fragment Assembly
	4.1 The Nature of the Problem
		4.1.1 Two Analogies
		4.1.2 The Need for Multiple Coverage
	4.2 Putting the Pieces Together
		4.2.1 Location, Location, Location
		4.2.2 Mapping
		4.2.3 Using Overlaps
		4.2.4 Whole-Genome Sequencing
		4.2.5 The Problem of Repeats
		4.2.6 A Worked Example
	4.3 The Size of the Problem
	4.4 A Purely Combinatorial Problem
		4.4.1 Problem Statement
	4.5 Solving the Combinatorial Problem
		4.5.1 Overlaps
		4.5.2 FragmentsWithin Fragments
		4.5.3 A Graph Model
		4.5.4 A Nonoptimal Greedy Algorithm
		4.5.5 Improving on Greed
	4.6 Biological Sequence Reassembly
	4.7 Sequencing by Hybridization
		4.7.1 A Worked Example
	4.8 Exercises for Chapter 4
	Key Terms
	Bibliography
Chapter 5 Sequence Alignment
	5.1 Exact Pattern Matching
		5.1.1 The Naïve Algorithm
		5.1.2 Algorithm Analysis
		5.1.3 Other Pattern-Matching Algorithms
		5.1.4 DFAs for Search
		5.1.5 DFAs as Programs
		5.1.6 Suffix Trees
		5.1.7 A Worked Example: abracadabara
		5.1.8 Recap of Exact Pattern Matching
	5.2 Things People Do Well: Similarity Detection
	5.3 Computers Helping People: Presenting DotPlots
		5.3.1 Straight DotPlot: Searching for Areas of Exact Matching
		5.3.2 A Worked Example: Can You Dance the Can-Can?
		5.3.3 Controlling Sensitivity and Selectivity
	5.4 People Helping Computers: Algorithms
		5.4.1 Alignment
		5.4.2 Quality of Alignments: Scoring Schemes
		5.4.3 Global Alignments: The Needleman–Wunsch Algorithms
		5.4.4 A Worked Example
		5.4.5 Local Alignments: The Smith–Waterman Algorithm
		5.4.6 A Worked Example
	5.5 Affine Gap Penalties
	5.6 Evolutionary Considerations
		5.6.1 PAM and BLOSUM
	5.7 Space/Time Analysis of Dynamic Programming Algorithms
	5.8 Heuristic Approaches: Fast A and BLAST
		5.8.1 A Worked Example: Bill Gates at Ballgames
	5.9 Multiple Alignments
		5.9.1 A Worked Example
		5.9.2 Analysis of Multiple-Alignment Algorithms
	5.10 Exercises for Chapter 5
	Key Terms
	Bibliography
Chapter 6 Simulating and Modeling Evolution
	6.1 The Biological Time Machine
		6.1.1 Evolutionary Processes
	6.2 E. coli Evolution
	6.3 Simulating Evolution in Silico
		6.3.1 Genetic Algorithms: A First Pass
		6.3.2 Monkey Shakespeare: An Extended Example
		6.3.3 Monkey Evolution: Making Whales from Weasels
		6.3.4 A Worked Example: A Genetic Algorithm for Gene Finding
	6.4 Modeling Evolutionary Relationships
		6.4.1 Models of Mutation
	6.5 Discovering Evolutionary Relationships
		6.5.1 Parsimony
		6.5.2 Other Ways to Build Trees
		6.5.3 Maximum Likelihood
	Key Terms
	Bibliography
Chapter 7 Gene Finding
	7.1 A Modern Cryptographic Puzzle
		7.1.1 Detecting Encryption
		7.1.2 Encoding Information
	7.2 Cracking the Genome: A First Pass
		7.2.1 A Worked Example: HIV Integration Sites
		7.2.2 Regulating Genes: Transcription Factor-Binding Sites
	7.3 A Biological Decoder Ring
		7.3.1 A First Try at Decryption: ORF Finding
		7.3.2 Accounting for Discontinuous Coding Regions
	7.4 Finding Genes Through Mathematics
		7.4.1 Linguistic Complexity
		7.4.2 Looks Like a
		7.4.3 Markov Models
		7.4.4 Genes as Markov Processes
	Key Terms
	Bibliography
	7.5 Gene Finding by Learning: Letting a Computer Do It
	7.6 Exercises for Chapter 7
Chapter 8 Gene Expression
	8.1 Introduction
	8.2 Genes in Context
	8.3 Genotype to Phenotype
	8.4 The Expected (by now) Complications of Biology
	8.5 A Flood of Data
	8.6 Noisy Data
		8.6.1 Turning down the Noise
	8.7 The Many Modes of Gene Expression Data
	8.8 A Worked Example: Gene Expression in HIV-Infected Cells
		8.8.1 Data Preprocessing
	8.9 Programs to Work with Genes and Expression Vectors
	8.10 Mining the Gene Expression Data
		8.10.1 A Worked Example: Looking for Differentially Expressed Genes
		8.10.2 Testing Biological Hypotheses with Statistical Hypotheses
	8.11 A Worked Example: Forming New Hypotheses
		8.11.1 Organizing the Data
		8.11.2 Clustering
		8.11.3 Classification
		8.11.4 Using Visualization Techniques to Aid Interpretation
		8.11.5 Advanced Classification Algorithms
	8.12 Data Management
		8.12.1 Controlled Vocabularies and Standardization of Microarray Data
	8.13 Exercises for Chapter 8
	Key Terms
	Bibliography
Chapter 9 Projects
	9.1 Visualization and Exploration of Complex Datasets
		9.1.1 Sequencing Gel Visualization
		9.1.2 Microarray Data Visualization
		9.1.3 Data Visualization Tools
		9.1.4 Over to You
		9.1.5 Resources for Visualization
	9.2 RNA Structure and Function Prediction
		9.2.1 Solving Structures for Functional RNAs: Early Successes
		9.2.2 Structural RNAs and Gene Regulation
		9.2.3 RNA Structures in Machines: Solving Complex Structures
		9.2.4 Over to You
		9.2.5 Resources for Structure Prediction
	9.3 Rational Drug Design Through Protein Structure and Function Prediction
		9.3.1 A Pharmaceutical Fairy Tale
		9.3.2 Drug Development: One in a Million Chances
		9.3.3 Structure-Based Drug Design
		9.3.4 A Pharmaceutical Cautionary Tale
		9.3.5 Over to You
		9.3.6 Resources for Rational Drug Design
	9.4 Information-Based Medicine
		9.4.1 Identifying Simple Disease Genes
		9.4.2 The Challenge of Mapping Complex Diseases
		9.4.3 Over to You
		9.4.4 Resources for Information-Based Medicine
	9.5 Systems Biology
		9.5.1 Introduction
		9.5.2 Inputs
		9.5.3 Outputs
		9.5.4 Modern Approach to Systems Biology
		9.5.5 Feedback, Equilibrium, and Attractors
		9.5.6 What Kind of Model?
		9.5.7 Over to You
		9.5.8 Resources for Systems Biology
	Key Terms
	Bibliography
Index