Microarray Technology in Practice
Published on 18. November 2008
Book
Paperback/Softback
464 pages
978-0-12-372516-5 (ISBN)
Description
Using chips composed of thousands of spots, each with the capability of holding DNA molecules corresponding to a given gene, DNA microarray technology has enabled researchers to measure simultaneously gene expression across the genome. As with other large-scale genomics approaches, microarray technologies are broadly applicable across disciplines of life and biomedical sciences, but remain daunting to many researchers. This guide is designed to demystify the technology and inform more biologists about this critically important experimental technique.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Life science professionals and graduate students
Product notice
Paperback (trade)
Dimensions
Height: 226 mm
Width: 150 mm
Thickness: 25 mm
Weight
635 gr
ISBN-13
978-0-12-372516-5 (9780123725165)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Steve Russell | Lisa A. Meadows | Roslin R. Russell
Microarray Technology in Practice
E-Book
11/2008
Academic Press
€49.95
Available for download
Persons
Content
Contents
1. What are microarrays?
A. The basics
2. Designing and producing a microarray
A. Platform options
B. Oligonucleotide design
C. Whole genome or boutique arrays
D. The problems of multiple testing
E. Array layout
F. Quality assessment
3. Sample collection and labelling
A. RNA extraction
B. Quality assessment
C. Direct labelling techniques
D. Indirect labelling techniques
E. End labelling techniques
F. Amplification techniques
G. Hybridization
4. Data acquisition
A. Scanners
B. Image acquisition
C. Multiple scans and data merging
D. Finding spots
E. Background measurement
G. Quality control basics
5. Experimental Design and Data Normalisation
A. Experimental design
B. Why normalize?
C. Unwanted signal variation
D. Common normalization approaches
E. Data transformation
F. Dealing with 'S'-shaped trends
G. Replicates and spatial detrending
H. Normalization of single channel arrays
6. Meta analysis
A. Data filtering
B. Screening for differentially expressed genes
C. Hierarchical clustering
D. Other clustering techniques
E. Components analysis
F. Prediction tools
7. Data annotation, storage and submission
A. Functional data annotation
B. Microarray databases
C. Public repositories
8. Applications in health & disease
A. Malaria - parasite expression
B. Cancer classification
C. Diagnostic Arrays
9. It's not all gene expression
A. ChIP-array
B. CGH
C. Protein arrays
D. Cell arrays
1. What are microarrays?
A. The basics
2. Designing and producing a microarray
A. Platform options
B. Oligonucleotide design
C. Whole genome or boutique arrays
D. The problems of multiple testing
E. Array layout
F. Quality assessment
3. Sample collection and labelling
A. RNA extraction
B. Quality assessment
C. Direct labelling techniques
D. Indirect labelling techniques
E. End labelling techniques
F. Amplification techniques
G. Hybridization
4. Data acquisition
A. Scanners
B. Image acquisition
C. Multiple scans and data merging
D. Finding spots
E. Background measurement
G. Quality control basics
5. Experimental Design and Data Normalisation
A. Experimental design
B. Why normalize?
C. Unwanted signal variation
D. Common normalization approaches
E. Data transformation
F. Dealing with 'S'-shaped trends
G. Replicates and spatial detrending
H. Normalization of single channel arrays
6. Meta analysis
A. Data filtering
B. Screening for differentially expressed genes
C. Hierarchical clustering
D. Other clustering techniques
E. Components analysis
F. Prediction tools
7. Data annotation, storage and submission
A. Functional data annotation
B. Microarray databases
C. Public repositories
8. Applications in health & disease
A. Malaria - parasite expression
B. Cancer classification
C. Diagnostic Arrays
9. It's not all gene expression
A. ChIP-array
B. CGH
C. Protein arrays
D. Cell arrays