CaGe: A Web-Based Cancer Gene Annotation System for Cancer Genomics.
- Author:
Young Kyu PARK
1
;
Tae Wook KANG
;
Su Jin BAEK
;
Kwon Il KIM
;
Seon Young KIM
;
Doheon LEE
;
Yong Sung KIM
Author Information
- Publication Type:Original Article
- Keywords: annotation; cancer gene; high-throughput genomic technology; mutation; next-generation sequencing; pathway
- MeSH: Gene Expression; Genes, Neoplasm; Genome; Genomics; Sequence Analysis, DNA; Small Cell Lung Carcinoma
- From:Genomics & Informatics 2012;10(1):33-39
- CountryRepublic of Korea
- Language:English
- Abstract: High-throughput genomic technologies (HGTs), including next-generation DNA sequencing (NGS), microarray, and serial analysis of gene expression (SAGE), have become effective experimental tools for cancer genomics to identify cancer-associated somatic genomic alterations and genes. The main hurdle in cancer genomics is to identify the real causative mutations or genes out of many candidates from an HGT-based cancer genomic analysis. One useful approach is to refer to known cancer genes and associated information. The list of known cancer genes can be used to determine candidates of cancer driver mutations, while cancer gene-related information, including gene expression, protein-protein interaction, and pathways, can be useful for scoring novel candidates. Some cancer gene or mutation databases exist for this purpose, but few specialized tools exist for an automated analysis of a long gene list from an HGT-based cancer genomic analysis. This report presents a new web-accessible bioinformatic tool, called CaGe, a cancer genome annotation system for the assessment of candidates of cancer genes from HGT-based cancer genomics. The tool provides users with information on cancer-related genes, mutations, pathways, and associated annotations through annotation and browsing functions. With this tool, researchers can classify their candidate genes from cancer genome studies into either previously reported or novel categories of cancer genes and gain insight into underlying carcinogenic mechanisms through a pathway analysis. We show the usefulness of CaGe by assessing its performance in annotating somatic mutations from a published small cell lung cancer study.
