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Generating a focused view of disease ontology cancer terms for pan-cancer data integration and analysis.

Wu TJ, Schriml LM, Chen QR, Colbert M, Crichton DJ, Finney R, Hu Y, Kibbe WA, Kincaid H, Meerzaman D, Mitraka E, Pan Y, Smith KM, Srivastava S, Ward S, Yan C, Mazumder R - Database (Oxford) (2015)

Bottom Line: There are multiple initiatives that are developing biomedical terminologies for the purpose of providing better annotation, data integration and mining capabilities.The disease ontology (DO) was developed over the past decade to address data integration, standardization and annotation issues for human disease data.For example, the COSMIC term 'kidney, NS, carcinoma, clear_cell_renal_cell_carcinoma' and TCGA term 'Kidney renal clear cell carcinoma' were both grouped to the term 'Disease Ontology Identification (DOID):4467 / renal clear cell carcinoma' which was mapped to the TopNodes_DOcancerslim term 'DOID:263 / kidney cancer'.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA.

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An example showing pan-cancer view of gene mutations mapped to DO cancer terms. A. Six oncogenes were mapped to 110 DO terms. The bandwidth represents the number of unique SNVs found in that gene in different cancer types. B. TopNodes_DOcancerslim display of the same analysis which shows 46 cancer terms associated with mutations found in six oncogenes. Overall, panel B displays a clearer view and the summarization enables large-scale analysis on an entire set of oncogenes or tumor suppressors across multiple cancer types. DOID terms are available in Table 1. HGNC gene symbols are used to represent the cancer genes.
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bav032-F3: An example showing pan-cancer view of gene mutations mapped to DO cancer terms. A. Six oncogenes were mapped to 110 DO terms. The bandwidth represents the number of unique SNVs found in that gene in different cancer types. B. TopNodes_DOcancerslim display of the same analysis which shows 46 cancer terms associated with mutations found in six oncogenes. Overall, panel B displays a clearer view and the summarization enables large-scale analysis on an entire set of oncogenes or tumor suppressors across multiple cancer types. DOID terms are available in Table 1. HGNC gene symbols are used to represent the cancer genes.

Mentions: Figure 3 displays a pan-cancer view of gene mutations mapped to DO cancer terms. In this figure, 6 oncogenes were selected from 13 significantly mutated genes based on our previous pan-cancer analysis (17). The oncogene categories were provided by Vogelstein et al. (47) in Cancer Genome Landscape study (Supplementary Table S1). Figure 3A displays 6 oncogenes mapped to 110 DO terms. In this figure, the bandwidth represents the number of unique mutations found in that gene labeled with that cancer type. Figure 3B displays the application of the TopNodes_DOcancerslim which shows 46 cancer terms associated with mutations found in six oncogenes and therefore provides a bird’s eye view of the mapping. Overall, Figure 3B displays a clearer view and the summarization enables large-scale analysis on an entire set of oncogenes or tumor suppressors across multiple cancer types.Figure 3.


Generating a focused view of disease ontology cancer terms for pan-cancer data integration and analysis.

Wu TJ, Schriml LM, Chen QR, Colbert M, Crichton DJ, Finney R, Hu Y, Kibbe WA, Kincaid H, Meerzaman D, Mitraka E, Pan Y, Smith KM, Srivastava S, Ward S, Yan C, Mazumder R - Database (Oxford) (2015)

An example showing pan-cancer view of gene mutations mapped to DO cancer terms. A. Six oncogenes were mapped to 110 DO terms. The bandwidth represents the number of unique SNVs found in that gene in different cancer types. B. TopNodes_DOcancerslim display of the same analysis which shows 46 cancer terms associated with mutations found in six oncogenes. Overall, panel B displays a clearer view and the summarization enables large-scale analysis on an entire set of oncogenes or tumor suppressors across multiple cancer types. DOID terms are available in Table 1. HGNC gene symbols are used to represent the cancer genes.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4385274&req=5

bav032-F3: An example showing pan-cancer view of gene mutations mapped to DO cancer terms. A. Six oncogenes were mapped to 110 DO terms. The bandwidth represents the number of unique SNVs found in that gene in different cancer types. B. TopNodes_DOcancerslim display of the same analysis which shows 46 cancer terms associated with mutations found in six oncogenes. Overall, panel B displays a clearer view and the summarization enables large-scale analysis on an entire set of oncogenes or tumor suppressors across multiple cancer types. DOID terms are available in Table 1. HGNC gene symbols are used to represent the cancer genes.
Mentions: Figure 3 displays a pan-cancer view of gene mutations mapped to DO cancer terms. In this figure, 6 oncogenes were selected from 13 significantly mutated genes based on our previous pan-cancer analysis (17). The oncogene categories were provided by Vogelstein et al. (47) in Cancer Genome Landscape study (Supplementary Table S1). Figure 3A displays 6 oncogenes mapped to 110 DO terms. In this figure, the bandwidth represents the number of unique mutations found in that gene labeled with that cancer type. Figure 3B displays the application of the TopNodes_DOcancerslim which shows 46 cancer terms associated with mutations found in six oncogenes and therefore provides a bird’s eye view of the mapping. Overall, Figure 3B displays a clearer view and the summarization enables large-scale analysis on an entire set of oncogenes or tumor suppressors across multiple cancer types.Figure 3.

Bottom Line: There are multiple initiatives that are developing biomedical terminologies for the purpose of providing better annotation, data integration and mining capabilities.The disease ontology (DO) was developed over the past decade to address data integration, standardization and annotation issues for human disease data.For example, the COSMIC term 'kidney, NS, carcinoma, clear_cell_renal_cell_carcinoma' and TCGA term 'Kidney renal clear cell carcinoma' were both grouped to the term 'Disease Ontology Identification (DOID):4467 / renal clear cell carcinoma' which was mapped to the TopNodes_DOcancerslim term 'DOID:263 / kidney cancer'.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA.

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Related in: MedlinePlus