Copy number alteration databases | Genome annotation
Cancer is known to have abundant copy number alterations (CNAs) that greatly contribute to its pathogenesis and progression. Investigation of CNA regions could potentially help identify oncogenes and tumor suppressor genes and infer cancer mechanisms.
Provides a public platform for investigators to share and compare their molecular cytogenetic data. SKY/M-FISH & CGH Database is open to everyone and all users can view an individual investigator's public data or compare public cases from different investigators. Those wishing to contribute their own data must register and can choose to keep their data private for a period not to exceed two years.
Allows users to access to a large-scale resource of genetics data. SCAN offers, via a web-interface and several methods and algorithms, a way to mine the data available. This repository includes two categories of single nucleotide polymorphism (SNP) annotations: physical-based annotation where SNPs are classified according principally to their position relative to genes (intronic, inter-genic, etc.) and functional annotation where SNPs are categorized according to their effects on expression levels.
Consists of a searchable reference data built for nexus integration. Nexus DB is a database that permits users to host their data thanks to the Amazon’s AWS platform. This platform is configured in such a way that users can perform query by gene or region of interest to find samples and clinical annotations. Additionally, this repository includes an option to share data with selected people.
A curated reference database and bioinformatics resource targeting copy number profiling data in human cancer. The arrayMap database provides a platform for meta-analysis and systems level data integration of high-resolution oncogenomic CNA data. The 2014 release of arrayMap contains more than 64 000 genomic array data sets, representing about 250 tumor diagnoses. The large amount of tumor CNA data in arrayMap can be freely downloaded by users to promote data mining projects, and to explore special events such as chromothripsis-like genome patterns.
Generates, analyzes, and makes available genomic sequence, expression, methylation, and copy number variation (CNV) data on over 11,000 individuals who represent over 30 different types of cancer. The information generated by TCGA is centrally managed and entered into databases as it becomes available, making the data rapidly accessible to the entire research community. TCGA is a collaborative effort led by the National Cancer Institute and the National Human Genome Research Institute to map the genomic and epigenomic changes that occur in types of human cancer, including nine rare tumors. Its goal is to support new discoveries through the generation of a catalog of somatic aberrations occurring in the different neoplasms, and accelerate the pace of research aimed at improving the diagnosis, treatment, and prevention of cancer.
A public, web-based database for storing quantitative microarray data and relevant metadata about the measurements and samples. CanGEM supports the MIAME standard and in addition, stores clinical information using standardized controlled vocabularies whenever possible. Microarray probes are re-annotated with their physical coordinates in the human genome and aCGH data is analyzed to yield gene-specific copy numbers. Users can build custom datasets by querying for specific clinical sample characteristics or copy number changes of individual genes. Aberration frequencies can be calculated for these datasets, and the data can be visualized on the human genome map with gene annotations.
A comprehensive, curated oncogenomic database that provides copy number aberration data to the human cancer research community. Over the past years, the database has undergone an extensive expansion and significant qualitative enhancements. Particularly, the database has made the transition from a ‘cytogenetic’ resource based on cancer cytogenetic data to an integrated resource incorporating cancer genome data from increasing variety of genome analysis techniques. Likewise, many ideas of the user interface improvements and data analysis tools have been implemented based on suggestions from users.