  Background: The delineation of genomic copy number abnormalities (CNAs) from
cancer samples has been instrumental for identification of tumor suppressor
genes and oncogenes and proven useful for clinical marker detection. An
increasing number of projects have mapped CNAs using high-resolution microarray
based techniques. So far, no single resource does provide a global collection
of readily accessible oncoge- nomic array data.
  Methodology/Principal Findings: We here present arrayMap, 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. To
date, the resource incorporates more than 40,000 arrays in 224 cancer types
extracted from several resources, including the NCBI's Gene Expression Omnibus
(GEO), EBIs ArrayExpress (AE), The Cancer Genome Atlas (TCGA), publication
supplements and direct submissions. For the majority of the included datasets,
probe level and integrated visualization facilitate gene level and genome wide
data re- view. Results from multi-case selections can be connected to
downstream data analysis and visualization tools.
  Conclusions/Significance: To our knowledge, currently no data source provides
an extensive collection of high resolution oncogenomic CNA data which readily
could be used for genomic feature mining, across a representative range of
cancer entities. arrayMap represents our effort for providing a long term
platform for oncogenomic CNA data independent of specific platform
considerations or specific project dependence. The online database can be
accessed at http://www.arraymap.org.
