Cancer Genome Anatomy Project (CGAP)/Cancer Genome Characterization Initiative (CGCI)
The National Cancer Institute (NCI) Cancer Genome Anatomy Project (CGAP) is an online resource designed to provide the research community access to biological tissue characterization data. CGAP provides a wide range of genomic data that include gene expression profiles of normal, precancerous, and cancerous cells [based on expressed sequence tags (EST) and serial analysis of gene expression (SAGE)], single nucleotide polymorphism (SNP) analysis of cancer-related genes, and the Mitelman database of chromosomal aberrations in cancer (which contains >50,000 cases).
NCI continues to build on CGAP and provide the research community with comprehensive genomic characterization data on several cancer types through the Cancer Genome Characterization Initiative (CGCI). CGCI incorporates multiple genomic characterization methods including exome and transcriptome analysis using second-generation sequencing. To encourage collaboration and leverage the collective knowledge and innovation of the entire cancer research community, all data collected are made publicly available through caBIG compatible databases.
By collaborating with scientists worldwide, CGAP and CGCI seek to increase its scientific expertise and expand its databases for the benefit of all cancer researchers. Access to all CGAP and CGCI data, clones, and analytic tools is made available to the research community through the CGAP/CGCI Web site.
*NEW: Access to Cancer Genome Characterization Initiative’s (CGCI) Diffuse large B-cell lymphoma dataset is now available here.
To learn more about the Cancer Genome Anatomy Project (CGAP) and Cancer Genome Characterization Initiative (CGCI), visit http://cgap.nci.nih.gov/.
Gene information, clone resources, SNP500Cancer, GAI, and transcriptome analysis
cDNA library information, methods, and EST-based gene expression analysis
Diagrams of biological pathways and protein complexes, with links to genetic resources for each known protein
RNA-interference constructs, targeted specifically against cancer relevant genes
FISH-mapped BAC clones, SNP500Cancer, and the Mitelman database of chromosome aberrations
Analysis of gene expression using long and short SAGE tag data for both human and mouse
Direct access to all analytic and data mining tools developed for the project