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Fundamentals of
Advanced Omics
Technologies: From
Genes to Metabolites
Comprehensive Analytical Chemistry
Volume 63
Edited by
Carolina Simo
Laboratory of Foodomics, Institute of Food Science Research (CIAL),CSIC. Nicolas Cabrera 9, Madrid, Spain
Alejandro CifuentesLaboratory of Foodomics, Institute of Food Science Research (CIAL),
CSIC. Nicolas Cabrera 9, Madrid, Spain
Virginia Garda-Canas
Laboratory of Foodomics, Institute of Food Science Research (CIAL),
CSIC. Nicolas Cabrera 9, Madrid, Spain
AMSTERDAM • BOSTON • HEIDELBERG • LONDON • NEW YORK • OXFORD
PARIS • SAN DIEGO • SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO
Contents
Contributors to Volume 63 xiii
Series Editor's Preface xvii
Preface xix
1. DNA Microarrays Technology: Overviewand Current Status 1
Alex Sanchez-Pla
1. Introduction and Overview 1
1.1. A Brief History of Microarrays 3
2. Types of DNA Microarrays 4
2.1. Spotted or Printed Microarrays 4
2.2. In Situ Synthesized Microarrays 5
2.3. High-Density Bead Arrays 9
3. Applications of Microarrays 11
3.1. Microarrays for Gene Expression Analysis 11
3.2. SNP Arrays for Variation Analysis and Genotyping 16
3.3. CGH Arrays for Comparative Genomic Hybridization 17
3.4. ChlP-on-Chip Arrays for Transcription Factor
Binding Analysis 17
3.5. Arrays for the Analysis of Alternative Splicingand Related Issues 18
4. Microarray Bioinformatics 18
4.1. The MIAME Standard 19
4.2. Microarray Databases 19
5. Discussion and Concluding Remarks 20
References 21
2. Challenges and Future Trends in DNA
Microarray Analysis 25
Abootaleb Sedighi and Paul C.H. Li
1. Introduction 25
2. Toward Microarray POC Devices 27
2.1. Microfluidic Microarrays 28
2.2. Label-Free Detection 33
2.3. Miniaturized Nanoarray Platforms 35
2.4. Integrated LOC Devices 36
3. Validity of Microarray Data 37
4. Clinical Adoption 39
v
Contents
5. Future Trends of Microarray 41
6. Conclusion 43
References 44
3. Next-Generation Sequencing: New Toolsto Solve Old Challenges 47
/. Gobernado, A. Sanchez-Herranz and A. Jimenez-Escrig
1. Introduction 47
2. Basis for NGS 50
3. Sample Preparation for NGS 51
3.1. Clonal Amplification 51
3.2. Single-Molecule Sequencing 52
4. Sequencing Techniques 54
4.1. Sequencing-by-Synthesis 54
4.2. Sequencing-by-Ligation 59
4.3. Other Sequencing Technologies 59
5. NGS Data Analysis 63
6. Main Applications of NGS 63
6.1. Whole-Genome Sequencing 63
6.2. Targeted Region Resequencing 66
6.3. Metagenomics 68
6.4. RNA-Sequencing 69
6.5. Other NGS Applications 70
7. Integrating Omics Data 72
References 73
4. Omics Tools for the Genome-Wide Analysisof Methylation and Histone Modifications 81
josep C. Jimenez-Chillaron, Ruben Diaz
and Marta Ramon-Krauel
t. Omics Meets Epigenetics 81
1.1. Omics 81
1.2. Epigenetics 82
1.3. Epigenomics: When -Omics Met Epigenetics 86
2. Methods in Epigenomics 87
2.1. DNA Methylation 87
2.2. Histone Modifications 96
2.3. Noncoding RNAs 99
3. Concluding Remarks 106
References 106
5. An Overview of Quantitative Proteomic Approaches 111
Adam J. McShane, Vahid Farrokhi, Reza Nemati, Song Li
and Xudong Yao
1. Introduction 111
2. Immuno-Based Detection Methods 112
Contents ( vii )
2.1. Gel-Based Approaches 112
2.2. Non-Gel-Based Approaches 113
3. Mass Spectrometry-Based Detection Methods 115
3.1. Introduction 115
3.2. Mass Spectrometers 115
3.3. Derivatization-Free Techniques 117
3.4. Derivatization-Based Techniques 119
3.5. Activity-Based Probes 121
3.6. Sample Preparation 122
3.7. Liquid Chromatography 125
3.8. Software 128
3.9. Analyte Multiplexing and Sample Throughput 128
4. Conclusions 128
Acknowledgments 129
References 129
6. Emerging Nanotechniques in Proteomics 137
Noelia Dasilva, Maria Gonzalez-Gonzalez, Paula Diez,Ricardo Jara-Acevedo, Lucia Lourido, j.M. Sayagues,Alberto Orfao and Manuel Fuentes
1. Introduction 137
2. Overview of Protein Microarrays 138
2.1. Target Microarray 139
2.2. Reverse-Phase Protein Array (RPP) 139
2.3. In Situ Expressed Protein Array 140
3. Detection Platforms in Nanoproteomics 140
3.1. Label Detection Techniques 141
3.2. Label-Free Detection Methods 141
4. Biomarker Discovery by Nanoproteomics Approaches 146
4.1. AuNPs and QDs 148
4.2. Surface Plasmon Resonance 148
4.3. Microcantilevers and AFM 150
4.4. ESI-MS 150
4.5. CyTof Applications 152
5. Conclusions 154
Acknowledgments 154
References 155
7. Mass Spectrometry Imaging in Proteomics and
Metabolomics 159
Benjamin Balluff, Ricardo J. Carreira and Liam
A. McDonnell
1. The Need for Imaging-Based Proteomics and Metabolomics 159
2. Mass Spectrometry Imaging 161
2.1. Mass Spectrometry 161
( viii ) Contents
2.2. Sample Preparation 168
2.3. Data Processing in MSI 170
2.4. Statistical Analysis of MSI Data 172
2.5. Strategies for the Identification of m/z Signals 175
3. Applications of MSI 178
3.1. Applications in Disease Pathology 178
3.2. Applications in Drug Imaging 179
4. Future Developments 180
References 181
8. Advances in NMR-Based Metabolomics 187
C.A. Nagana Gowda and Daniel Raftery
1. Introduction 187
2. NMR Methods , 188
2.1. One-Dimensional NMR Methods 189
2.2. Two-Dimensional NMR Methods 190
2.3. Isotope-Enhanced NMR Methods 191
3. Micro-Coil NMR 193
4. Fast NMR Methods 195
5. Hyperpolarization in NMR 195
6. Statistical and Data Analysis Methods 196
7. Spectral Assignment and Metabolite Quantitation Methods 198
7.1. Automation 199
7.2. Analysis of Heteronuclear 2D Spectra 199
8. STOCSY and RANSY Methods 200
9. Applications 201
10. Conclusions 204
11. Acknowledgment 205
12. Disclosure of Potential Conflict of Interest 205
References 205
9. The Role of Mass Spectrometry in NontargetedMetabolomics 213
Helen C. Cika, Ian D. Wilson and
Ceorgios A. Theodoridis
1. Introduction 213
2. Study Design 215
3. Sample Preparation 216
4. Analytical Strategies 218
5. MS-Based Untargeted Metabolomics 221
5.1. LC Opportunities and Issues 221
5.2. GC Opportunities and Issues 223
5.3. MS Opportunities and Issues 224
6. Data Analysis 225
Contents GD
7. Identification of Biomarkers and Biochemical PathwayVisualization 227
8. Synopsis 230
References 231
10. Direct Mass Spectrometry-Based Approachesin Metabolomics 235
Clara Ibanez, Virginia Garda-Cahas, Alberto Valdes
and Carolina Simo
1. Introduction 235
2. Matrix-Assisted and Matrix-Free Laser
Desorption/lonization MS 236
3. Direct-Infusion MS 238
4. Ambient-lonization MS 240
5. Imaging MS 243
6. Conclusions 250
References 250
11. Functional Glycomics Analysis: Challenges and
Methodologies 255
Nathan W. Stebbins and Ram Sasisekharan
1. Introduction 255
2. Structural Analysis of Clycans 258
2.1. General Consideration for Glycan Analysis 259
2.2. High-Performance Liquid Chromatography 261
2.3. Capillary Electrophoresis 262
2.4. MS for Glycomics 264
2.5. Lectins as Structural Probes 266
3. Functional Analysis of Glycans 267
3.1. Genetics Approach to Glycomics 268
3.2. Glycan Array and Synthesis Technologies 270
4. Integrating Structure and Function: A Case Study 271
5. Conclusions 275
Acknowledgments 276
References 276
12. Applications of Glycan Microarrays to Functional
Glycomics 281
Ying Yu, Xuezheng Song, David F. Smith and
Richard D. Cummings
1. Introduction 281
2. Generation of Glycan Microarrays 283
2.1. Glycan Sources 283
2.2. Glycan Immobilization 285
2.3. Shotgun Glycan Microarray (SGM) 288
CD Contents
3. Examples of Reported Glycan Microarrays 290
4. Identification of Virus Receptors with Glycan Microarrays 294
4.1. Influenza Virus 294
4.2. Minute Virus of Mice 297
4.3. Rotavirus 298
5. Conclusions 299
Acknowledgments 300
References 300
13. High-Resolution Analytical Tools for QuantitativePeptidomics 305
Sayani Dasgupta and Lloyd D. Fricker
1. Introduction 305
2. Absolute Quantification 306
3. Relative Quantification'
307
3.1. Label-Free Quantification 308
3.2. Metabolic Labeling 311
3.3. Chemical Labeling 312
3.4. Proteolytic Labeling 319
4. Concluding Remarks 320
References 320
14. Analysis of Deep Sequencing Data: Insightsand Challenges 325
yacoo W. Malcom and John H. Malone
1. Introduction 325
2. Fundamentals 326
2.1. Quality Control 327
2.2. Mapping 329
3. Applications 333
3.1. DNA Applications 334
3.2. RNA Applications 338
3.3. Relating Sequence and Expression Variation
to Phenotypes 344
3.4. Application Pitfalls: Data Heterogeneity,Normalization, and False Discovery 346
4. The Computing Side of Deep Sequencing 347
4.1. Data Management and Computational Power 348
4.2. Visualization 348
4.3. Public Data 349
4.4. Communicating Analyses: Galaxy and Code Repositories 349
4.5. Resources 351
5. Summary and Future Directions 351
Acknowledgments 352
References 352
Contents ( xi )
15. Gene Expression Analysis and Profiling of
Microarrays Data and RNA-Sequencing Data 355
Javier De Las Rivas, Sara Aibar and Beatriz Roson
1. Human Genome and Transcriptome: From Gene Loci
to Gene Products 356
1.1. Redefinition of Gene in the Context of Modern
Transcriptomics 356
1.2. Types of Genes and RNA Complexity: EmergingInterest in ncRNAs 357
1.3. Protein-Coding Genes: cDNA Collections and ORFeomes 359
1.4. Biological Databases to Identify and Explore Expressionof Protein-Coding Genes: Entrez Gene, ENSEMBL,
GATExplorer, and ProteinAtlas 360
1.5. Gene Expression Regulation: Transcription Factors 362
2. Experimental Technologies for Genome-Wide ExpressionAnalysis 364
2.1. Measuring Gene Expression: Real-Time qPCR 364
2.2. Microarrays 365
2.3. RNA Sequencing 370
2.4. Other High-Throughput Platforms to InvestigateRegulation of Gene Expression 372
3. Bioinformatic Analysis of Gene Expression Data 375
3.1. Computational Methods to Achieve Gene
Expression Profiling and Find Gene Signatures 375
3.2. Algorithms to Analyze Microarray Expression Data 377
3.3. Algorithms to Analyze RNA-Seq Data 378
3.4. Methods for Functional and Pathway Enrichment
Analysis of Selected Gene Lists 381
References 382
16. Bioinformatic Approaches to Increase Proteome
Coverage 385
Francesco M. Mancuso, Salvatore Cappadonaand Eduard Sabido
1. Introduction 385
2. Increasing the Number of Fragmented Features 388
2.1. Protein-Level Separation 388
2.2. Peptide-Level Separation 389
2.3. MS-Level Separation 391
3. Reducing the Number of Unassigned Spectra 393
3.1. Reducing Rates of Unidentified Peptides 394
3.2. Increasing Rates of Identifiable Peptides 399
4. Summary 409
Acknowledgments 411
References 411
Contents
17. Transcriptome and Metabolome Data
Integration—Technical Perquisites for SuccessfulData Fusion and Visualization 421
Michael Witting and Philippe Schmitt-Kopplin
1. Introduction 421
2. Extraction, Measurement, Raw Data Analysis, and
Data Fusion 424
2.1. Transcriptomics 424
2.2. Metabolomics 427
2.3. Data Fusion Types 430
3. Visualization 433
3.1. Visualization on KEGC Pathways 433
3.2. Visualization on MetaCyc Pathways 435
3.3. Network Visualization and Analysis 435
4. MassTRIX Reloaded—Combined'Analysis and
Visualization of Metabolome and Trascriptome Data 436
4.1. Annotation of Mass Spectrometric Data 436
4.2. Analysis of Transcriptomic Data 437
4.3. Comparison Against Other Existing Resources 437
4.4. Future Directions for MassTRIX 439
5. Conclusions 440
References 441
18. Computational Approaches for Visualizationand Integration of Omics Data 443
Vasudha Sehgal, Tyler J. Moss and Prahlad T. Ram
1. Introduction 443
2. Data Overview 444
2.1. Data Types 444
2.2. Data Sources 445
3. Data Processing and Analyzing Tools 448
4. Network and Pathway Databases 448
4.1. Protein Interaction Databases 449
4.2. Pathway Commons 449
5. Visualization of Omics Data 450
5.1. Clustering and Heatmaps 450
5.2. Tools for Network Creation, Visualization, and Analysis 451
6. Conclusion 453
References 453
Index 455