It also provides tools for viewing and analysis of the data, allowing for identification of genes involved in various aspects of tumor progression.
The goal of CGAP is to characterize cancer at a molecular level by providing a platform with readily accessible updated data and a set of tools such that researchers can easily relate their findings to existing knowledge.
[3] CGAP's initial goal was to establish a Tumor Gene Index (TGI) to store the expression profiles.
This cellular heterogeneity made gene expression information in terms of cancer biology less accurate.
SNPs are valuable in cancer research as they can be used in several different genetic studies, commonly to track transmission, identify alternate forms of genes and analyze complex molecular pathways that regulate cell metabolism, growth, or differentiation.
[5] SNPs in the CGAP-GAI are either found as a result of resequencing genes of interest in different individuals or looking through existing human EST databases and making comparisons.
SNPs that are found undergo statistical analysis using the CGAP SNP pipeline to calculate the probability that the variant is in fact a polymorphism.
[6] Genomic instability is a common feature of cancer; therefore understanding structural and chromosomal abnormalities can give insight into the progression of disease.
The CCAP has several goals:[7] There is cytogenetic information from over 64,000 patient cases, including more than 2000 gene fusions, contained in the database.
[1] An early technique used by CGAP is digital differential display (DDD), which uses the Fisher exact test to compare libraries against each other, in order to find a significant difference between populations.
The project includes human and mouse genes, and later cow cDNAs generated by Genome Canada were added.
[13] CGAP is now a centralised location for several genomics tools and genetic databases and is employed widely in cancer and molecular biology research.
The transcriptome databases can also be used in non-cancer related research, as they contain information that can be used to quickly and easily identify particular sequenced genes.
The data also has clinical impact, as cDNAs can be used to create microarrays for diagnosis and treatment comparison purposes.