Introduction
High-throughput genomic technologies (HGTs), including next-generation DNA sequencing (NGS), microarray, and serial analysis of gene expression (SAGE), have become effective tools for cancer genomics through various applications. Especially, the applications of NGS include whole-genome, exome, and transcriptome approaches for searching for a wide range of cancer-specific genomic alterations, such as point mutations, insertions, and deletions; copy number changes; and rearrangements, leading to the development of cancer [1].
One of the hurdles in HGT-based cancer genomics is to identify causative mutations or genes out of many candidates from the analysis. A useful approach is to refer to known cancer genes and associated information [2]. The list of known cancer genes can be used to determine candidates of cancer driver mutations, while cancer gene-related information, such as gene expression, protein-protein interaction, and pathway information, can be useful for scoring novel candidates.
Some useful cancer genomic databases for this purpose exist, including Cancer Gene Census (CGC) [3] and Catalogue of Somatic Mutations in Cancer (COSMIC) [4], which have been constructed and managed by the Cancer Genome Project of the Welcome Trust Sanger Institute; and Cancer Gene Index (CGI, http://ncicb.nci.nih.gov/NCICB/projects/cgdcp), The Cancer Genome Atlas (TCGA, http://cancergenome.nih.gov), and Cancer Genome Anatomy Project (CGAP, http://cgap.nci.nih.gov), which are maintained by the National Cancer Institute of the U. S. National Institute of Health and National Human Genome Research Institute. Among them, COSMIC and CGC are the two most commonly used resources among researchers when checking reported cancer driver mutations. The COSMIC database stores information on somatic mutations and associated information extracted from the scientific literature, while the CGC database is a catalog of cancer genes with manually screened somatic mutations extracted from the literature. These cancer genes are annotated with information concerning chromosomal location, tumor types in which mutations are found, classes of mutations that contribute to oncogenesis, and other genetic properties. Also, CGI is known to be created through an automated linguistic text analysis of millions of MEDLINE abstracts, with manual validation and annotation of the extracted data by expert curators. It is a high-quality data resource consisting of genes that have been experimentally associated with human cancer diseases and/or pharmacological compounds, the evidence of these associations, and relevant annotations on the data. Thus, it can also be a valuable resource to annotate mutation data from cancer genomics studies.
Currently, few specialized tools exist for an automated analysis of a long gene list from HGT-based cancer genomics. While previously described cancer genomic information databases can be useful, they are not effective in processing results from NGS-based cancer genomic studies. Meanwhile, there are many functional annotation systems to analyze gene lists derived from high-throughput microarray experiments, including DAVID [5], GoMiner [6], GOstat [7], Onto-express [8], and gene set enrichment analysis (GSEA) [9]. DAVID is one of the most popular general purpose functional annotation systems. It provides various effective analysis tools and is also applicable in the analysis of gene lists from cancer genomics research. However, DAVID is not optimized for processing results from HGT-based cancer genomics data, especially for NGS-based cancer genomics downstream data; therefore, a more specialized cancer gene annotation system is needed.
Here, we introduce a web-accessible cancer genome annotation system, named CaGe, to provide users with information on cancer genes, mutations, pathways, and associated annotations, based on several cancer gene databases composed of reported cancer-causing genes and associated cancer pathways. For a given gene list, CaGe searches cancer gene databases with converted standard gene symbols, analyzes the biological pathways, and provides various cancer gene-related annotations through intuitive web user interfaces. It also serves additional functions, including processing various input types and formats, managing jobs for user submitted tasks, and browsing for cancer genes and pathways with various useful hyperlinks between the annotations and the external public annotation databases. We hope CaGe will be useful in identifying cancer-causing mutations and genes in HGT-based cancer genomics.