Abstract
BackgroundThe rapidly growing amount of array CGH data requires improved visualization software supporting the process of identifying candidate cancer genes. Optimally, such software should work across multiple microarray platforms, should be able to cope with data from different sources and should be easy to operate.ResultsWe have developed a web-based software FISH Oracle to visualize data from multiple array CGH experiments in a genomic context. Its fast visualization engine and advanced web and database technology supports highly interactive use. FISH Oracle comes with a convenient data import mechanism, powerful search options for genomic elements (e.g. gene names or karyobands), quick navigation and zooming into interesting regions, and mechanisms to export the visualization into different high quality formats. These features make the software especially suitable for the needs of life scientists.ConclusionsFISH Oracle offers a fast and easy to use visualization tool for array CGH and SNP array data. It allows for the identification of genomic regions representing minimal common changes based on data from one or more experiments. FISH Oracle will be instrumental to identify candidate onco and tumor suppressor genes based on the frequency and genomic position of DNA copy number changes. The FISH Oracle application and an installed demo web server are available at http://www.zbh.uni-hamburg.de/fishoracle.
Highlights
The rapidly growing amount of array CGH data requires improved visualization software supporting the process of identifying candidate cancer genes
fluorescence in situ hybridization (FISH) Oracle comes with a convenient data import mechanism, powerful search options for genomic elements and mechanisms to export the visualization into different high quality formats
User interface The data import process in FISH Oracle consists of two steps
Summary
The rapidly growing amount of array CGH data requires improved visualization software supporting the process of identifying candidate cancer genes. Such software should work across multiple microarray platforms, should be able to cope with data from different sources and should be easy to operate. One important strategy to reveal genetic loci containing putative cancer genes is to perform multiple experiments and identify chromosomal regions representing minimal common alterations. Since large alterations spanning many megabases are typically more common than the small ones containing only a few genes, as many experiments as possible should be included into such kind of analysis. A number of software tools for array CGH analysis and visualization are available — both from academia and commercial vendors — they are often limited to a particular data format, cannot be operated, or lack interactivity
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