Microarrays for an integrative genomics / Isaac S. Kohane, Alvin T. Kho, and Atul J. Butte.

Format:

Book

Author/Creator:

Kohane, Isaac S.

Contributor:

Kho, Alvin T.

Butte, Atul J.

Series:

Computational molecular biology

Language:

English

Subjects (All):

DNA microarrays.

Genomics--Automation.

Genomics.

Bioinformatics.

Oligonucleotide Array Sequence Analysis.

Computational Biology.

Automation.

Medical Subjects:

Oligonucleotide Array Sequence Analysis.

Genomics.

Computational Biology.

Automation.

Physical Description:

xviii, 306 pages : illustrations ; 24 cm.

Place of Publication:

Cambridge, Mass. : MIT Press, [2003]

Summary:

An introduction to the use of DNA microarrays in functional genomics.

Contents:

1.1 The Future Is So Bright... 1

1.2 Functional Genomics 4

1.2.1 Informatics and advances in enabling technology 5

1.2.2 Why do we need new techniques? 10

1.3 Missing the Forest for the Dendrograms 13

1.3.1 Sociology of a functional genomics pipeline 18

1.4 Functional Genomics, Not Genetics 19

1.4.1 In silico analysis will never substitute for in vitro and in vivo 20

1.5 Basic Biology 25

1.5.1 Biological caveats in mRNA measurements 31

1.5.2 Sequence-level genomics 33

1.5.3 Proteomics 34

2 Experimental Design 37

2.1 The Safe Conception of a Functional Genomic Experiment 37

2.1.1 Experiment design space 37

2.1.2 Expression space 39

2.1.3 Exercising the expression space 43

2.1.4 Discarding data and low-hanging fruit 53

2.2 Gene-Clustering Dogma 60

2.2.1 Supervised versus unsupervised learning 61

2.2.2 Figure of merit: The elusive gold standard in functional genomics 63

3 Microarray Measurements to Analyses 69

3.1 Generic Features of Microarray Technologies 69

3.1.1 Robotically spotted microarrays 73

3.1.2 Oligonucleotide microarrays 77

3.2 Replicate Experiments, Reproducibility, and Noise 88

3.2.1 What is a replicate experiment? A reproducible experimental outcome? 90

3.2.2 Reproducibility across repeated microarray experiments: Absolute expression level and fold difference 92

3.2.3 Cross-platform (technology) reproducibility 96

3.2.4 Pooling sample probes and PCR for replicate experiments 98

3.2.5 What is noise? 99

3.2.6 Sources and examples of noise in the generic microarray experiment 100

3.2.7 Biological variation as noise: The Human Genome Project and irreproducibility of expression measurements 109

3.2.8 Managing noise 112

3.3 Prototypical Objectives and Questions 116

3.3.1 Two examples: Inter-array and intra-array 118

3.4 Preprocessing: Filters and Normalization 121

3.4.1 Normalization 122

3.5 Background on Fold 127

3.5.1 Fold calculation and significance 130

3.5.2 Fold change may not mean the same thing in different expression measurement technologies 134

3.6 Dissimilarity and Similarity Measures 137

3.6.1 Linear correlation 139

3.6.2 Entropy and mutual information 140

3.6.3 Dynamics 146

4 Genomic Data-Mining Techniques 149

4.2 What Can Be Clustered in Functional Genomics? 149

4.3 What Does it Mean to Cluster? 150

4.4 Hierarchy of Bioinformatics Algorithms 151

4.5 Data Reduction and Filtering 155

4.5.1 Variation filter 155

4.5.2 Low entropy filter 156

4.5.3 Minimum expression level filter 160

4.5.4 Target ambiguity filter 161

4.6 Self-Organizing Maps 161

4.6.1 K-means clustering 164

4.7 Finding Genes That Split Sets 169

4.8 Phylogenetic-Type Trees 172

4.8.1 Two-dimensional dendrograms 176

4.9 Relevance Networks 181

4.10 Other Methods 189

4.11 Which Technique Should I Use? 191

4.12 Determining the Significance of Findings 195

4.12.1 Permutation testing 196

4.12.2 Testing and training sets 197

4.12.3 Performance metrics 200

4.12.4 Receiver operating characteristic curves 201

4.13 Genetic Networks 203

4.13.1 What is a genetic network? 203

4.13.2 Reverse-engineering and modeling a genetic network using limited data 204

4.13.3 Bayesian networks for functional genomics 208

5 Bio-Ontologies, Data Models, Nomenclature 215

5.1 Ontologies 216

5.1.1 Bio-ontology projects 218

5.1.2 Advanced knowledge representation systems for bio-ontology 222

5.2 Expressivity versus Computability 224

5.3 Ontology versus Data Model versus Nomenclature 226

5.3.1 Exploiting the explicit and implicit ontologies of the biomedical literature 228

5.4 Data Model Introduction 231

5.5.1 The unique gene identifier 243

5.6 Postanalysis Challenges 247

5.6.1 Linking to downstream biological validation 247

5.6.2 Problems in determining the results 248

6 From Functional Genomics to Clinical Relevance 249

6.1 Electronic Medical Records 249

6.2 Standardized Vocabularies for Clinical Phenotypes 251

6.3 Privacy of Clinical Data 252

6.3.1 Anonymization 253

6.3.2 Privacy rules 255

6.4 Costs of Clinical Data Acquisition 256

7 The Near Future 257

7.1 New Methods for Gene Expression Profiling 257

7.1.1 Electronic positioning of molecules: Nanogen 259

7.1.2 Ink-jet spotting of arrays: Agilent 260

7.1.3 Coded microbeads bound to oligonucleotides: Illumina 262

7.1.4 Serial Analysis of Gene Expression (SAGE) 264

7.1.5 Parallel signature sequencing on microbead arrays: Lynx 264

7.1.6 Gel pad technology: Motorola 266

7.2 Respecting the Older Generation 266

7.2.1 The generation gap 267

7.2.2 Separating the wheat from the chaff 267

7.2.3 A persistent problem 270

7.3 Selecting Software 271

7.4 Investing in the Future of the Genomic Enterprise 273.

Notes:

"A Bradford book."

Includes bibliographical references (pages [283]-295) and index.

ISBN:

026211271X

OCLC:

49305644