Informatics in Proteomics

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ISBN: 9781574444803 | 1574444808
Cover: Hardcover
Copyright: 6/24/2005

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The handling and analysis of data generated by proteomics investigations represent a challenge for computer scientists, biostatisticians, and biologists to develop tools for storing, retrieving, visualizing, and analyzing genomic data. Informatics in Proteomics examines the ongoing advances in the application of bioinformatics to proteomics research and analysis.Through computer simulations, scientists can determine more about how diseases affect cells, predict how various drug interventions would work, and ultimately use proteins as therapeutic targets. This book first addresses the infrastructure needed for public protein databases. It discusses information management systems and user interfaces for storage, retrieval, and visualization of the data as well as issues surrounding data standardization and integration of protein sequences recorded in the last two decades. The authors subsequently examine the application of statistical and bioinformatic tools to data analysis, data presentation, and data mining. They discuss the implementation of algorithms, statistical methods, and computer applications that facilitate pattern recognition and biomarker discovery by integrating data from multiple sources.This book offers a well-rounded resource of informatic approaches to data storage, retrieval, and protein analysis as well as application-specific bioinformatic tools that can be used in disease detection, diagnosis, and treatment. Informatics in Proteomics captures the current state-of-the-art and provides a valuable foundation for future directions.

Chapter 1 The Promise of Proteomics: Biology, Applications, and Challenges

(16)

Paul D. Wagner and Sudhir Srivastava

1.1 Introduction

(1)

1.2 Why Is Proteomics Useful?

(1)

1.3 Gene-Environment Interactions

(1)

1.4 Organelle-Based Proteomics

(1)

1.5 Cancer Detection

(1)

1.6 Why Proteomics Has Not Succeeded in the Past: Cancer as an Example

(1)

1.7 How Have Proteomic Approaches Changed over the Years?

(4)

1.8 Future of Proteomics in Drug Discovery, Screening, Early Detection, and Prevention

(2)

References

(4)

Chapter 2 Proteomics Technologies and Bioinformatics

(14)

Sudhir Srivastava and Mukesh Verma

2.1 Introduction: Proteomics in Cancer Research

(6)

2.1.1 Two-Dimensional Gel Electrophoresis (2DE)

(1)

2.1.2 Mass Spectrometry

(1)

2.1.3 Isotope-Coded Affinity Tags (ICAT)

(1)

2.1.4 Differential 2DE (DIGE)

(1)

2.1.5 Protein-Based Microarrays

(3)

2.2 Current Bioinformatics Approaches in Proteomics

(3)

2.2.1 Clustering

(1)

2.2.2 Artificial Neural Networks

(1)

2.2.3 Support Vector Machine (SVM)

(1)

2.3 Protein Knowledge System

(1)

2.4 Market Opportunities in Computational Proteomics

(1)

2.5 Challenges

(1)

2.6 Conclusion

(1)

References

(3)

Chapter 3 Creating a National Virtual Knowledge Environment for Proteomics and Information Management

(22)

Daniel Crichton, Heather Kincaid, Sean Kelly, Sudhir Srivastava, J. Steven Hughes, and Donald Johnsey

3.1 Introduction

(1)

3.2 A Data Architecture for Distributed Information Management

(7)

3.2.1 Descriptors for Describing Metadata

(1)

3.2.2 Creating Standard Data Elements

(1)

3.2.3 Metadata Registries

(1)

3.2.4 Data Architecture Approaches for Supporting Resource Discovery

(3)

3.3 A Technical Architecture for Distributed Information Management

(6)

3.3.1 Distributed Framework Communication

(2)

3.3.2 Profile Servers

(1)

3.3.3 Product Servers

(1)

3.3.4 Query Servers

(1)

3.3.5 Software Deployment

(1)

3.4 Knowledge System Applications

(4)

3.4.1 EDRN Resource Network Exchange

(2)

3.4.2 EDRN Validation Studies Information Management System

(2)

3.5 Conclusions

(1)

Acknowledgments

(1)

References

(2)

Chapter 4 Public Protein Databases and Interfaces

(26)

Jane Meejung Chang Oh

4.1 Introduction

(2)

4.1.1 The Evergrowing Web of Proteomics Data

(1)

4.1.2 Public Protein Databases

(1)

4.1.3 WWW and FTP Proteomics Servers

(1)

4.2 Catalog of Public Protein Databases

(15)

4.2.1 Online Catalog of Databases

(1)

4.2.2 List of Public Protein Databases

(14)

4.2.2.1 Protein Sequence Databases

(2)

4.2.2.2 Protein Family Databases

(3)

4.2.2.3 Protein Structure Databases

(2)

4.2.2.4 Protein Structural Classification Databases

(1)

4.2.2.5 Protein-Protein Interaction Databases

(1)

4.2.2.6 Posttranslational Modification Databases

(1)

4.2.2.7 2-D PAGE Databases

(1)

4.2.2.8 Mass Spectrometry Databases

(1)

4.2.2.9 Other Protein Databases

(3)

4.2.3 Nucleotide Sequence Databases

(1)

4.3 Database Retrieval Systems

(2)

4.4 Integrating Protein Databases

(3)

4.4.1 Protein Knowledge Base

(1)

4.4.2 United Protein Database

(1)

4.4.3 One-Stop Shop for Protein Data

(1)

4.4.4 Protein Experiment Database

(1)

References

(4)

Chapter 5 Proteomics Knowledge Databases: Facilitating Collaboration and Interaction between Academia, Industry, and Federal Agencies

(30)

Denise B. Warzel, Marcy Winget, Cim Edelstein, Chenwei Lin, and Mark Thornquist

5.1 Introduction

(1)

5.1.1 Knowledge Databases vs. Databases

(1)

5.1.2 Proteomics Knowledge Database Challenges

(1)

5.2 Multiparty Collaboration

(21)

5.2.1 Why Collaborate?

(1)

5.2.2 Collaborative Framework

(1)

5.2.3 Processes for Facilitating Collaboration

(1)

5.2.3.1 Communication

(1)

5.2.3.2 Infrastructure

(1)

5.2.4 Barriers to Collaboration

(2)

5.2.4.1 Information Has Value

(1)

5.2.4.2 Human Subject Information Is Private and Tightly Regulated

(1)

5.2.4.3 There Are Costs to Sharing Data

(1)

5.2.4.4 There Is No Central Gateway for Dissemination of Data

(1)

5.2.5 Institutional Review Board (IRB) Considerations

(16)

5.2.5.1 Approaches for Enabling Multiparty IRB Review and Streamlining IRB Process

101

(1)

5.3 Metadata and Data Standars for Multiparty Collaborations

102

(3)

5.3.1 Metadata vs. Data

103

(1)

5.3.2 Standards Resources

104

(1)

5.4 Web Resources and Existing Databases

105

(1)

5.4.1 ca BIO

105

(1)

5.5 Conclusions

106

(1)

References

107

(2)

Chapter 6 Proteome Knowledge Bases in the Context of Cancer

109

(32)

Djamel Medjahed and Peter A. Lemkin

6.1 Introduction

110

(2)

6.2 Virtual 2-D: A Web-Accessible Predictive Database for Proteomics Analysis

112

(8)

6.2.1 Database Mining

113

(3)

6.2.2 Comparison with Experimental Data

116

(4)

6.3 TMAP (Tissue Molecular Anatomy Project)

120

(15)

6.3.1 Data Mining

121

(2)

6.3.2 ProtPlot

123

(22)

6.3.2.1 Using ProtPlot for Data Mining Virtual Protein Expression Patterns

125

(3)

6.3.2.2 The Scatterplot Display Mode

128

(1)

6.3.2.3 Effect of Display Mode on Filtering, Clustering, and Reporting

129

(1)

6.3.2.4 Selecting Samples

130

(1)

6.3.2.5 Listing a Report on Sample Assignments

130

(1)

6.3.2.6 Assigning the X Set and Y Set Condition Names

131

(1)

6.3.2.7 Status Reporting Window

131

(1)

6.3.2.8 Data Filtering

131

(1)

6.3.2.9 Saving Filtered Proteins in Sets for Use in Subsequent Data Filtering

131

(1)

6.3.2.10 Filter Dependence on the Display Mode

131

(2)

6.3.2.11 The Data Mining State

133

(1)

6.3.2.12 The Molecular Mass vs. pI Scatterplot: Expression or Ratio

133

(1)

6.3.2.13 X Sample(s) vs. Y Samples Scatterplot

133

(1)

6.3.2.14 Expression Profile Plot of a Specific Protein

134

(1)

6.3.2.15 Clustering of Expression Profiles

134

(1)

6.3.2.16 Reports

135

(1)

6.3.2.17 Genomic Databases

135

(1)

6.4 Results and Data Analysis

135

(3)

6.5 Conclusion

138

(1)

References

139

(2)

Chapter 7 Data Standards in Proteomics: Promises and Challenges

141

(22)

Veerasamy Ravichandran, Ram D. Sriram, Gary L. Gilliland, and Sudhir Srivastava

7.1 Introduction

142

(1)

7.2 Data Issues in Proteomics

143

(2)

7.3 Data Standards

145

(3)

7.3.1 The Standards Developmental Process

146

(1)

7.3.2 Examples of Standards Activities in Proteomics

146

(1)

7.3.3 Data Standards Activities for Proteomic Data

147

(1)

7.4 Techniques for Representing Proteomics Data

148

(3)

7.4.1 Standard Ontology

148

(1)

7.4.2 Classification of Proteins

149

(1)

7.4.3 Synonyms

149

(2)

7.5 Biological Data Exchange Options

151

(6)

7.5.1 Abstract Syntax Notation One (ASN.1)

152

(1)

7.5.2 XML for Proteomics Data Exchange

152

(1)

7.5.3 Data Standards for 2D PAGE Data: Markup Language for Electrophoresis Data

153

(4)

7.6 Unifying Proteomics Data: Common Data Repository

157

(3)

7.6.1 Data Mining

157

(1)

7.6.2 Data Source

158

(1)

7.6.3 Data Annotation/Validation

158

(1)

7.6.4 Data Management and Archiving

159

(1)

7.6.5 Data Distribution

160

(1)

7.7 Future Directions

160

(1)

Acknowledgment

161

(1)

References

161

(2)

Chapter 8 Data Standardization and Integration in Collaborative Proteomics Studies

163

(30)

Marcin Adamski, David J. States, and Gilbert S. Omenn

8.1 Collaborative Proteomics

164

(8)

8.1.1 The Necessity of Collaboration

164

(1)

8.1.2 The Necessity of Data Standardization

164

(3)

8.1.3 Information Flow in Collaborative Studies

167

(4)

8.1.3.1 Data Exchange Strategies

167

(2)

8.1.3.2 Data Exchange Technologies

169

(1)

8.1.3.3 Data Collection and Data Integration in Collaborative Studies

170

(1)

8.1.4 HUPO-The Human Proteome Organization

171

(1)

8.2 Standardization Initiatives in Proteomics

172

(11)

8.2.1 HUPO-PSI: Proteome Standards Initiative

173

(7)

8.2.1.1 MIAPE and PEDRo

173

(3)

8.2.1.2 mzData

176

(2)

8.2.1.3 HUP-ML

178

(1)

8.2.1.4 PSI MI: Molecular Interactions

178

(2)

8.2.2 SASHIMI

180

(2)

8.2.3 TWODML

182

(1)

8.3 Pilot Phase of the HUPO-PPP: A Case Study

183

(7)

8.3.1 Setting up the Collaboration

184

(1)

8.3.2 Data Standardization and Data Exchange Protocols

185

(1)

8.3.3 Data Integration

186

(3)

8.3.4 Discussion

189

(1)

References

190

(3)

Chapter 9 Informatics Tools for Functional Pathway Analysis Using Genomics and Proteomics

193

(12)

Chad Creighton and Samir M. Hanash

9.1 Introduction

193

(1)

9.2 Profiling Gene Expression at the RNA and Protein Levels

194

(1)

9.3 Complexities of Biological Pathway Analyses

195

(1)

9.4 The Kyoto Encyclopedia of Genes and Genomes (KEGG)

196

(1)

9.5 Gene Microarray Pathway Profiler

197

(1)

9.6 Protein Interaction Databases

197

(3)

9.7 The Kinase Pathway Database

200

(1)

9.8 Other Databases under Development

201

(1)

9.9 Conclusion

202

(1)

References

202

(1)

Additional References

203

(2)

Chapter 10 Data Mining in Proteomics

205

(22)

R. Gangal

10.1 Introduction

206

(1)

10.2 The General Data Mining Process

207

(3)

10.2.1 Filter, Clean, and Standardize

208

(1)

10.2.2 Feature Selection

209

(1)

10.3 Approaches to Data Mining

210

(9)

10.3.1 Statistics or Machine Learning

210

(7)

10.3.1.1 The Role of Machine Learning

211

(1)

10.3.1.2 Basic Concept of Neural Networks

212

(5)

10.3.2 Supervised vs. Unsupervised Learning

217

(1)

10.3.3 Pattern Discovery or Pattern Recognition

218

(1)

10.3.4 The Data Type (Spectroscopic Data, Sequence Data, Structure Data, Image/Binary Object Data)

218

(1)

10.3.5 High Throughput/Low Throughput

219

(1)

10.4 Model Performance Evaluation and Validation

219

(2)

10.5 Emerging Areas in Proteomic Data Mining

221

(1)

10.6 Cross-Pollination of Concepts

222

(1)

Acknowledgments

223

(1)

References

223

(2)

Appendix

225

(2)

Chapter 11 Protein Expression Analysis

227

(28)

Guoan Chen and David G. Beer

11.1 Introduction

228

(1)

11.2 Specimen Preparation

228

(1)

11.3 Two-Dimensional Polyacrylamide Gel Electrophoresis (2D PAGE)

229

(1)

11.3.1 Protein Extraction

229

(1)

11.3.2 2D PAGE

229

(1)

11.3.3 Silver Staining

229

(1)

11.3.3.1 Procedure

230

(1)

11.4 Protein Spot Detection, Quantification, and Matching

230

(3)

11.4.1 2D Gel Digitization (Scanning)

230

(1)

11.4.2 Protein Spot Detection and Quantification

230

(1)

11.4.2.1 Procedure

230

(1)

11.4.3 Matching the Protein Spots

231

(2)

11.4.3.1 Procedure

231

(2)

11.5 Quantitative Adjustment of Protein Spots

233

(6)

11.5.1 Neighboring Reference Spot Adjustments

233

(3)

11.5.2 Batch Adjustment

236

(3)

11.5.2.1 Why Do We Need Batch Adjustment?

236

(1)

11.5.2.2 Procedure

237

(2)

11.6 Identification, Confirmation, and Validation of the Proteins of Interest

239

(2)

11.6.1 Mass Spectrometry

239

(1)

11.6.2 Verification by 2D Western Blot

239

(2)

11.6.3 Tissue Arrays

241

(1)

11.7 Practical Application of Protein Expression Analysis of 2D PAGE in Lung Cancer

241

(11)

11.7.1 Reproducibility Analysis

241

(2)

11.7.2 Identification of Potential Lung Tumor Biomarkers

243

(3)

11.7.3 Discovery of Survival-Related Proteins

246

(3)

11.7.4 Protein-mRNA Correlation Analysis

249

(1)

11.7.5 Proteins Related to Tumor Differentiation

250

(2)

11.8 Conclusions

252

(1)

Acknowledgment

252

(1)

References

252

(3)

Chapter 12 Nonparametric, Distance-Based, Supervised Protein Array Analysis

255

(12)

Mei-Fen Yeh, Jeanne Kowalski, Nicole White, and Zhen Zhang

12.1 Introduction

255

(2)

12.2 Nonparametric Method for Supervised Proteomic Analysis

257

(5)

12.2.1 Construction of Replicate-Composite Measure

258

(1)

12.2.2 Relating Proteome Heterogeneity to Pathological Disease States

258

(3)

12.2.3 Translating Observed Proteome Heterogeneity to Peak Selection

261

(1)

12.3 Data Application

262

(2)

12.4 Discussion

264

(1)

12.5 Summary

265

(1)

References

265

(2)

Chapter 13 Protein Identification by Searching Collections of Sequences with Mass Spectrometric Data

267

(10)

D. Fenyö, J. Eriksson, and R.C. Beavis

13.1 Introduction

267

(2)

13.2 The Information Value of Mass Measurements

269

(1)

13.3 Algorithms for Protein Identification

270

(3)

13.4 Significance Testing

273

(1)

13.5 Summary

274

(1)

References

274

(3)

Chapter 14 Bioinformatics Tools for Differential Analysis of Proteomic Expression Profiling Data from Clinical Samples

277

(16)

Zhen Zhang

14.1 Challenge of Expression Profiling Using Clinical Samples

277

(2)

14.2 Two Schools of Bioinformatics Approaches to Expression Data Analysis

279

(1)

14.3 Considerations of Bioinformatics Tools for Differential Analysis of Proteomic Expression Profile Data

280

(1)

14.4 The Unified Maximum Separability Analysis Algorithm

281

(2)

14.5 UMSA-Based Procedures for Expression Profile Data Analysis

283

(3)

14.6 Application of UMSA-Based Tools for Biomarker Discovery Using Clinical Proteomics Data from Mass Spectrometry

286

(2)

14.7 The Importance of Study Design

288

(1)

14.8 Final Notes

289

(1)

References

290

(3)

Chapter 15 Sample Characterization Using Large Data Sets

293

(42)

Brian T. Luke

15.1 Introduction

293

(4)

15.2 Scaling the Samples

297

(2)

15.3 Outlier Detection

299

(7)

15.4 Constructing a Classification Model

306

(23)

15.4.1 Feature Selection Methods

306

(9)

15.4.1.1 Heuristic Searches

306

(2)

15.4.1.2 Stochastic Feature Selection Using a Single Solution

308

(2)

15.4.1.3 Stochastic Feature Selection Using a Population of Solutions

310

(5)

15.4.2 Distance Metrics

315

(5)

15.4.3 Classification Models

320

(9)

15.4.3.1 K-Nearest Neighbors

320

(3)

15.4.3.2 Clustering

323

(3)

15.4.3.3 Neural Networks

326

(3)

15.5 Examination of the Model

329

(3)

15.5.1 Classification Uncertainty

329

(1)

15.5.2 Cluster Statistics

329

(1)

15.5.3 Determining a Threshold in Fuzzy Classification

330

(1)

15.5.4 Verification of the Model

331

(1)

15.6 Conclusions

332

(1)

Acknowledgments

332

(1)

References

332

(3)

Chapter 16 Computational Tools for Tandem Mass Spectrometry-Based High-Throughput Quantitative Proteomics

335

(18)

Jimmy K. Eng, Andrew Keller, Xiao-jun Li, Alexey I. Nesvizhskii, and Ruedi Aebersold

16.1 Introduction

335

(1)

16.2 Computational Methods to Identify Peptides and Proteins Using MS/MS Spectra

336

(2)

16.3 Validation of Identifications at the Peptide Level

338

(4)

16.4 Inferring Proteins in the Sample

342

(3)

16.5 Quantitative Approaches Using Tandem MS

345

(4)

16.6 Conclusion

349

(1)

References

349

(4)

Chapter 17 Pattern Recognition Algorithms and Disease Biomarkers

353

(14)

Ben A. Hitt, Emanuel Petricoin, and Lance Liotta

17.1 Introduction

353

(1)

17.2 What Is the Need?

354

(1)

17.2.1 The Problem with Single Biomarkers

354

(1)

17.2.2 The Pattern Advantage

354

(1)

17.3 Pattern Recognition Methods

355

(3)

17.3.1 Nonlinear Feed-Forward Neural Networks: The Basics

355

(2)

17.3.2 Interpretation of Results

357

(1)

17.3.3 ANN Pros and Cons

358

(1)

17.4 Self-Organizing Maps: The Basics

358

(4)

17.4.1 Interpretation of Results

360

(1)

17.4.2 SOM Pros and Cons

361

(1)

17.5 Adaptive Pattern Recognition: The Basics

362

(3)

17.5.1 Interpretation of Results

363

(1)

17.5.2 Pros and Cons

363

(2)

17.6 Feature Selection

365

(1)

References

365

(2)

Chapter 18 Statistical Design and Analytical Strategies for Discovery of Disease-Specific Protein Patterns

367

(24)

Ziding Feng, Yutaka Yasui, Dale McLerran, Bao-Ling Adam, and John Semmes

18.1 Introduction

368

(1)

18.2 Statistical Properties of Protein Expression Measurements

369

(4)

18.3 Study Design

373

(5)

18.3.1 The Issue of Overfitting

373

(1)

18.3.2 Use of Training and Test Data Sets and Sample Size Considerations

374

(2)

18.3.3 Controlling for Potential Confounders by Frequency Matching

376

(1)

18.3.4 Replicated Assays

377

(1)

18.3.5 Balance between Comparison Groups and Replicate over the Major Sources of Variance Components

377

(1)

18.4 Data Analysis

378

(10)

18.4.1 Identifying Poor-Quality Spectra

378

(1)

18.4.2 Peak Identification and Alignment

378

(2)

18.4.3 Reduction of Dimensionality

380

(3)

18.4.4 Classifier Construction

383

(4)

18.4.4.1 Logistic Regression

383

(2)

18.4.4.2 Boosting

385

(2)

18.4.5 Assessment of Prediction Error

387

(1)

18.5 Conclusion

388

(1)

References

388

(3)

Chapter 19 Image Analysis in Proteomics

391

(42)

Stephen Lockett

19.1 Introduction

392

(1)

19.2 Fluorescence Labeling Proteins in Cells

393

(3)

19.2.1 Use of Fluorescence Dyes

393

(1)

19.2.2 Fluorescence Labeling of Proteins with Antibodies

394

(1)

19.2.3 Fluorescence Labeling of Proteins with Green Fluorescence Protein (GFP) and DsRed

394

(1)

19.2.4 Other Fluorescence Labels: Cell Structures and Organelles, and Indicators of Cell Activity

395

(1)

19.3 Acquisition of Optical Images

396

(4)

19.3.1 Conventional Fluorescence Microscopy

396

(1)

19.3.2 Three-Dimensional Fluorescence Microscopy

397

(1)

19.3.3 Confocal Microscopy

398

(1)

19.3.4 Near-Field Microscopy

398

(2)

19.4 Analysis of Fluorescence Images

400

(13)

19.4.1 The Digital Image

400

(1)

19.4.2 Common Distortions in Microscope Images

401

(3)

19.4.2.1 Saturation

401

(1)

19.4.2.2 Linearity

401

(1)

19.4.2.3 Spatial Resolution and Point Spread Function

401

(1)

19.4.2.4 Background

402

(1)

19.4.2.5 Noise

402

(1)

19.4.2.6 Signal-to-Noise Ratio

403

(1)

19.4.3 Image Processing

404

(3)

19.4.3.1 Point Operations

404

(1)

19.4.3.2 Neighborhood Operations

404

(1)

19.4.3.3 Deconvolution of Fluorescence Images

405

(2)

19.4.4 Quantitative Image Analysis

407

(5)

19.4.4.1 Image Segmentation

407

(3)

19.4.4.2 Feature Measurement

410

(1)

19.4.4.3 Object Classification

410

(2)

19.4.5 Visualization of 3D Images

412

(1)

19.4.5.1 Gallery and Orthogonal Views

412

(1)

19.4.5.2 Volume Rendering

412

(1)

19.4.5.3 Surface Rendering

413

(1)

19.5 Microscope Techniques to Study Protein Dynamics and Interactions

413

(6)

19.5.1 Time-Lapse Imaging

413

(1)

19.5.2 Fluorescence Recovery after Photobleaching

413

(4)

19.5.3 Colocalization Analysis

417

(1)

19.5.4 Fluorescence Resonance Energy Transfer

417

(2)

19.6 Emerging Microscope Techniques for Protein Analysis in Cells

419

(7)

19.6.1 Emerging Labeling Methods: Nanoparticles

420

(1)

19.6.2 Emerging Acquisition Methods

420

(2)

19.6.2.1 Spectral Imaging

420

(1)

19.6.2.2 Multiphoton Microscopy

421

(1)

19.6.3 Emerging Image Analysis Methods

422

(3)

19.6.3.1 Advanced Methods to Detect Cell Nuclei and Whole Cells in Tissue

422

(2)

19.6.3.2 Objective Analysis of the Subcellular Distribution of Proteins

424

(1)

19.6.4 Emerging Microscope Techniques

425

(1)

19.6.4.1 Fluorescence Correlation Spectroscopy and Fluorescence Intensity Distribution Analysis

425

(1)

19.6.4.2 Fluorescence Lifetime Imaging

425

(1)

19.6.4.3 Microscope-Based High-Throughput Screening (HTS) of Cells

426

(1)

19.7 Conclusion

426

(1)

References

427

(6)

Index

433

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Informatics in Proteomics

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Informatics in Proteomics

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