Bioinformatics:
Getting Results in the Era of High-Throughput Genomics

 

 

Chapter 1
INTRODUCTION: Turning Data into Drugs
Chapter 2
SCIENTIFIC BACKGROUND: Taming the Data Flux

2.1 The Data Wave
Sequence Data
Gene Expression Data
Protein Expression Data
Protein Structure Data
Single Nucleotide Polymorphism Maps
Gene/Protein Function
2.2 The Role of the Public Domain
2.3 Lots of Data, Some of It of Questionable Quality
Sidebar: Annotation, Curation, and Redundancy
2.4 The Integration Challenge
Sidebar: File Formats and File Structure
Sidebar: Standards
Chapter 3
CURRENT AND EMERGING TECHNOLOGIES: Data Collection and Analysis
3.1 Introduction: The Data Flow
Collecting the Data in the Laboratory
Higher-Level Questions and Systemwide Analysis
Scientific Integration
Reaching High-Throughput Capacity
3.2 Sequencing--Data Collection and Analysis
New Findings
Traditional Sequence Analysis
Sidebar: What Is a Gene?
Gene Regulation
Gene Prediction
Comparative Genomics
The Significance of Single Nucleotide Polymorphisms
Mining Sequence Information
Sidebar: Mining Genomes with Genome Therapeutics Corporation (GTC)
Outlook
3.3 Gene Expression Analysis
Sidebar: The Advantages of an Analysis Information Management System
Image Analysis
Analyzing Microarray Expression Data
Issues with Standards
Genomewide Analysis
 Expressed Sequence Tag Databases
Gene Expression Databases
Mining Gene Expression Data
Outlook
Sidebar: Affymetrix on Software and Data Integration: An Interview with Dave Craford
3.4 Protein Expression
Sidebar: What Is Proteomics?
2D Gel Analysis
Protein-Identification Software
Moving to High-Throughput
Sidebar: Applied Biosystems' Rapid Integration Solutions for Proteomics
Protein Expression Databases
Mining Protein Expression Data
Outlook
3.5 Protein Structure
 Homology Modeling and Ab Initio Predictive Methods
Sidebar: Why Study Protein Structure?
Advanced Docking Tools and Other Approaches to Improving Screening
Sidebar: Better Lead Development Through Structural Information
Protein Structure Databases
Mining Protein Structure Data
Outlook
Chapter 4
BUSINESS AND STRATEGIC CONSIDERATIONS: A Rapidly Changing Landscape
4.1 The Business Landscape
4.2 The Role of Large Pharmaceutical Companies
4.3 Evolving Business Models
Sidebar: Top Deals by Millennium Pharmaceuticals and Vertex Pharmaceuticals
4.4 Are The Profits Downstream?
Chapter 5
APPLICATIONS: Better Drug Discovery, Better Treatment?
5.1 Drug Discovery
5.2 Tumor Classification
 Sidebar: David Botstein on Getting Results with Microarrays
5.3 Use of Single Nucleotide Polymorphisms for  Drug Selection and Disease-Association Studies
Drug Selection
Genetic Basis of Premature Heart Disease
5.4 Outlook
Chapter 6
OUTLOOK: Waiting for the Integration Solution
6.1 Key Areas for Change
Breakthroughs Needed
Fundamental Issues and Challenges
6.2 Future Targets
Functional Data Feed Complex System Modeling
Sidebar: Bioinformatics for Analysis of Complex Systems
Will Chemoinformatics and Clinical Informatics Groups Invade the Bioinformatics Field?
Pharmacogenomics
Sidebar: Lincoln Stein on Single Nucleotide Polymorphisms
Data Integration Remains the Biggest Challenge
6.3 Commentaries from Editorial Advisory Board Members (10 commentaries)
Appendix A: Selected Bioinformatics Companies and Their Areas of Expertise (over 80 companies)
Appendix B: Profiles of Selected Companies (over 40 companies)
Appendix C: Selected Basic Tools for Sequence Analysis (Pairwise Sequence Comparison, Comparison of Multiple Sequences, Protein Domain Identification, and Hidden Markov Analysis)