Microarrays for an Integrative Genomics

by Kohane, Isaac S.; Kho, Alvin T.; Butte, Atul J.

• ISBN13: 9780262612104
• ISBN10: 0262612100
• Format: Paperback
• Copyright: 2005-09-01
• Publisher: Bradford Books

Summary

Functional genomics-the deconstruction of the genome to determine the biological function of genes and gene interactions-is one of the most fruitful new areas of biology. The growing use of DNA microarrays allows researchers to assess the expression of tens of thousands of genes at a time. This quantitative change has led to qualitative progress in our ability to understand regulatory processes at the cellular level. This book provides a systematic introduction to the use of DNA microarrays as an investigative tool for functional genomics. The presentation is appropriate for readers from biology or bioinformatics. After presenting a framework for the design of microarray-driven functional genomics experiments, the book discusses the foundations for analyzing microarray data sets, genomic data-mining, the creation of standardized nomenclature and data models, clinical applications of functional genomics research, and the future of functional genomics.

Author Biography

Isaac S. Kohane is Director of the Children's Hospital Informatics Program, Associate Professor of Pediatrics at Harvard Medical School, and an Attending Physician in Endocrinology.

Table of Contents

Foreword	xi
Preface	xiii
Acknowledgments	xvii
Introduction	1	(36)
The Future Is So Bright...	1	(3)
Functional Genomics	4	(9)
Informatics and advances in enabling technology	5	(5)
Why do we need new techniques?	10	(3)
Missing the Forest for the Dendrograms	13	(6)
Sociology of a functional genomics pipeline	18	(1)
Functional Genomics, Not Genetics	19	(6)
In silico analysis will never substitute for in vitro and in vivo	20	(5)
Basic Biology	25	(12)
Biological caveats in mRNA measurements	31	(2)
Sequence-level genomics	33	(1)
Proteomics	34	(3)
Experimental Design	37	(32)
The Safe Conception of a Functional Genomic Experiment	37	(23)
Experiment design space	37	(2)
Expression space	39	(4)
Exercising the expression space	43	(10)
Discarding data and low-hanging fruit	53	(7)
Gene-Clustering Dogma	60	(9)
Supervised versus unsupervised learning	61	(2)
Figure of merit: The elusive gold standard in functional genomics	63	(6)
Microarray Measurements to Analyses	69	(80)
Generic Features of Microarray Technologies	69	(19)
Robotically spotted microarrays	73	(4)
Oligonuclcotide microarrays	77	(11)
Replicate Experiments, Reproducibility, and Noise	88	(28)
What is a replicate experiment? A reproducible experimental outcome?	90	(2)
Reproducibility across repeated microarray experiments: Absolute expression level and fold difference	92	(4)
Cross-platform (technology) reproducibility	96	(2)
Pooling sample probes and PCR for replicate experiments	98	(1)
What is noise?	99	(1)
Sources and examples of noise in the generic microarray experiment	100	(9)
Biological variation as noise: The Human Genome Project and irreproducibility of expression measurements	109	(3)
Managing noise	112	(4)
Prototypical Objectives and Questions	116	(5)
Two examples: Inter-array and intra-array	118	(3)
Preprocessing: Filters and Normalization	121	(6)
Normalization	122	(5)
Background on Fold	127	(10)
Fold calculation and significance	130	(4)
Fold change may not mean the same thing in different expression measurement technologies	134	(3)
Dissimilarity and Similarity Measures	137	(12)
Linear correlation	139	(1)
Entropy and mutual information	140	(6)
Dynamics	146	(3)
Genomic Data-Mining Techniques	149	(66)
Introduction	149	(1)
What Can Be Clustered in Functional Genomics?	149	(1)
What Does it Mean to Cluster?	150	(1)
Hierarchy of Bioinformatics Algorithms	151	(4)
Data Reduction and Filtering	155	(6)
Variation filter	155	(1)
Low entropy filter	156	(4)
Minimum expression level filter	160	(1)
Target ambiguity filter	161	(1)
Self-Organizing Maps	161	(8)
K-means clustering	164	(5)
Finding Genes That Split Sets	169	(3)
Phylogenetic-Type Trees	172	(9)
Two-dimensional dendrograms	176	(5)
Relevance Networks	181	(8)
Other Methods	189	(2)
Which Technique Should I Use?	191	(4)
Determining the Significance of Findings	195	(8)
Permutation testing	196	(1)
Testing and training sets	197	(3)
Performance metrics	200	(1)
Receiver operating characteristic curves	201	(2)
Genetic Networks	203	(12)
What is a genetic network?	203	(1)
Reverse-engineering and modeling a genetic network using limited data	204	(4)
Bayesian networks for functional genomics	208	(7)
Bio-Ontologies, Data Models, Nomenclature	215	(34)
Ontologies	216	(8)
Bio-ontology projects	218	(4)
Advanced knowledge representation systems for bio-ontology	222	(2)
Expressivity versus Computability	224	(2)
Ontology versus Data Model versus Nomenclature	226	(5)
Exploiting the explicit and implicit ontologies of the biomedical literature	228	(3)
Data Model Introduction	231	(8)
Nomenclature	239	(8)
The unique gene identifier	243	(4)
Postanalysis Challenges	247	(2)
Linking to downstream biological validation	247	(1)
Problems in determining the results	248	(1)
From Functional Genomics to Clinical Relevance	249	(8)
Electronic Medical Records	249	(2)
Standardized Vocabularies for Clinical Phenotypes	251	(1)
Privacy of Clinical Data	252	(4)
Anonymization	253	(2)
Privacy rules	255	(1)
Costs of Clinical Data Acquisition	256	(1)
The Near Future	257	(20)
New Methods for Gene Expression Profiling	257	(9)
Electronic positioning of molecules: Nanogen	259	(1)
Ink-jet spotting of arrays: Agilent	260	(2)
Coded microbeads bound to oligonucleotides: Illumina	262	(2)
Serial Analysis of Gene Expression (Sage)	264	(1)
Parallel signature sequencing on microbead arrays: Lynx	264	(2)
Gel pad technology: Motorola	266	(1)
Respecting the Older Generation	266	(5)
The generation gap	267	(1)
Separating the wheat from the chaff	267	(3)
A persistent problem	270	(1)
Selecting Software	271	(2)
Investing in the Future of the Genomic Enterprise	273	(4)
Glossary	277	(6)
References	283	(13)
Index	296