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

Park YK, Kang TW, Baek SJ, Kim KI, Kim SY, Lee D, Kim YS - Genomics Inform (2012)

Bottom Line: 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 tool provides users with information on cancer-related genes, mutations, pathways, and associated annotations through annotation and browsing functions.We show the usefulness of CaGe by assessing its performance in annotating somatic mutations from a published small cell lung cancer study.

View Article: PubMed Central - PubMed

Affiliation: Medical Genomics Research Center, KRIBB, Daejeon 305-806, Korea. ; Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, Korea.

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.

No MeSH data available.


Related in: MedlinePlus

Cancer gene annotation results page of CaGe web interface.
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Related In: Results  -  Collection

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Figure 3: Cancer gene annotation results page of CaGe web interface.

Mentions: After acquiring user input, CaGe converts various input gene IDs into standard gene symbols, finds known cancer genes and pathways, links various cancer-related annotations to matched genes, and outputs them in the form of tables or text files through the web interface (Fig. 3). Another function of CaGe is to identify over-represented cancer-related or other biological pathways from the input gene list by performing one-tailed Fisher's exact test.


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

Park YK, Kang TW, Baek SJ, Kim KI, Kim SY, Lee D, Kim YS - Genomics Inform (2012)

Cancer gene annotation results page of CaGe web interface.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3475486&req=5

Figure 3: Cancer gene annotation results page of CaGe web interface.
Mentions: After acquiring user input, CaGe converts various input gene IDs into standard gene symbols, finds known cancer genes and pathways, links various cancer-related annotations to matched genes, and outputs them in the form of tables or text files through the web interface (Fig. 3). Another function of CaGe is to identify over-represented cancer-related or other biological pathways from the input gene list by performing one-tailed Fisher's exact test.

Bottom Line: 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 tool provides users with information on cancer-related genes, mutations, pathways, and associated annotations through annotation and browsing functions.We show the usefulness of CaGe by assessing its performance in annotating somatic mutations from a published small cell lung cancer study.

View Article: PubMed Central - PubMed

Affiliation: Medical Genomics Research Center, KRIBB, Daejeon 305-806, Korea. ; Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, Korea.

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.

No MeSH data available.


Related in: MedlinePlus