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http://dx.doi.org/10.5808/GI.2012.10.1.33

CaGe: A Web-Based Cancer Gene Annotation System for Cancer Genomics  

Park, Young-Kyu (Medical Genomics Research Center, KRIBB)
Kang, Tae-Wook (Medical Genomics Research Center, KRIBB)
Baek, Su-Jin (Medical Genomics Research Center, KRIBB)
Kim, Kwon-Il (Department of Bio and Brain Engineering, KAIST)
Kim, Seon-Young (Medical Genomics Research Center, KRIBB)
Lee, Do-Heon (Department of Bio and Brain Engineering, KAIST)
Kim, Yong-Sung (Medical Genomics Research Center, KRIBB)
Publication Information
Genomics & Informatics / v.10, no.1, 2012 , pp. 33-39 More about this Journal
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.
Keywords
annotation; cancer gene; high-throughput genomic technology; mutation; next-generation sequencing; pathway;
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1 Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet 2010;11:685-696.   DOI   ScienceOn
2 Zang ZJ, Ong CK, Cutcutache I, Yu W, Zhang SL, Huang D, et al. Genetic and structural variation in the gastric cancer kinome revealed through targeted deep sequencing. Cancer Res 2011;71:29-39.   DOI
3 Futreal PA, Coin L, Marshall M, Down T, Hubbard T, Wooster R, et al. A census of human cancer genes. Nat Rev Cancer 2004;4:177-183.   DOI   ScienceOn
4 Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D, et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res 2011;39:D945-D950.   DOI
5 Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57.
6 Zeeberg BR, Qin H, Narasimhan S, Sunshine M, Cao H, Kane DW, et al. High-Throughput GoMiner, an 'industrial- strength' integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID). BMC Bioinformatics 2005;6:168.   DOI
7 Beissbarth T, Speed TP. GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 2004;20:1464-1465.   DOI   ScienceOn
8 Khatri P, Voichita C, Kattan K, Ansari N, Khatri A, Georgescu C, et al. Onto-Tools: new additions and improvements in 2006. Nucleic Acids Res 2007;35:W206-W211.   DOI
9 Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome- wide expression profiles. Proc Natl Acad Sci U S A 2005;102:15545-15550.   DOI   ScienceOn
10 Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 2003;31:3812-3814.   DOI   ScienceOn
11 Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods 2010;7:248-249.   DOI   ScienceOn
12 Pleasance ED, Stephens PJ, O'Meara S, McBride DJ, Meynert A, Jones D, et al. A small-cell lung cancer genome with complex signatures of tobacco exposure. Nature 2010;463:184-190.   DOI   ScienceOn