CGHAnalyzer: a stand-alone software package for cancer genome analysis using array-based DNA copy number data

This synopsis provides an overview of array-based comparative genomic hybridization data display, abstraction and analysis using CGHAnalyzer, a software suite, designed specifically for this purpose. CGHAnalyzer can be used to simultaneously load copy number data from multiple platforms, query and d...

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Bibliographic Details
Published inBioinformatics Vol. 21; no. 15; pp. 3308 - 3311
Main Authors Margolin, Adam A., Greshock, Joel, Naylor, Tara L., Mosse, Yael, Maris, John M., Bignell, Graham, Saeed, Alexander I., Quackenbush, John, Weber, Barbara L.
Format Journal Article
LanguageEnglish
Published Oxford Oxford University Press 01.08.2005
Oxford Publishing Limited (England)
Subjects
Algorithms
Animals
Biological and medical sciences
Chromosome Mapping - methods
Computer Graphics
DNA Mutational Analysis - methods
Fundamental and applied biological sciences. Psychology
Gene Dosage
General aspects
Humans
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Neoplasms - genetics
Oligonucleotide Array Sequence Analysis - methods
Software
User-Computer Interface
Data analysis
Comparative genomic hybridization
Copy number
DNA chip
Software package
Software
Review
Malignant tumor
Microarray
Display
Online AccessGet full text
ISSN1367-4803
1367-4811
1460-2059
1367-4811
DOI10.1093/bioinformatics/bti500

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Summary:This synopsis provides an overview of array-based comparative genomic hybridization data display, abstraction and analysis using CGHAnalyzer, a software suite, designed specifically for this purpose. CGHAnalyzer can be used to simultaneously load copy number data from multiple platforms, query and describe large, heterogeneous datasets and export results. Additionally, CGHAnalyzer employs a host of algorithms for microarray analysis that include hierarchical clustering and class differentiation. Availability: CGHAnalyzer, the accompanying manual, documentation and sample data are available for download at http://acgh.afcri.upenn.edu. This is a Java-based application built in the framework of the TIGR MeV that can run on Microsoft Windows, Macintosh OSX and a variety of Unix-based platforms. It requires the installation of the free Java Runtime Environment 1.4.1 (or more recent) (http://www.java.sun.com). Contact: weberb@mail.med.upenn.edu
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ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/bti500