Computational approaches to identify functional genetic variants in cancer genomes
International Cancer Genome Consortium members review and recommend computational approaches for identifying mutations that drive cancer progression from among the many sequence variants present in tumor genomes. The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities...
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| Veröffentlicht in: | Nature methods 2013-08, Vol.10 (8), p.723-729 |
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| creator | Gonzalez-Perez, Abel Mustonen, Ville Reva, Boris Ritchie, Graham R S Creixell, Pau Karchin, Rachel Vazquez, Miguel Fink, J Lynn Kassahn, Karin S Pearson, John V Bader, Gary D Boutros, Paul C Muthuswamy, Lakshmi Ouellette, B F Francis Reimand, Jüri Linding, Rune Shibata, Tatsuhiro Valencia, Alfonso Butler, Adam Dronov, Serge Flicek, Paul Shannon, Nick B Carter, Hannah Ding, Li Sander, Chris Stuart, Josh M Stein, Lincoln D Lopez-Bigas, Nuria |
| description | International Cancer Genome Consortium members review and recommend computational approaches for identifying mutations that drive cancer progression from among the many sequence variants present in tumor genomes.
The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype. |
| doi_str_mv | 10.1038/nmeth.2562 |
| format | Article |
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The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype.</description><identifier>ISSN: 1548-7091</identifier><identifier>EISSN: 1548-7105</identifier><identifier>DOI: 10.1038/nmeth.2562</identifier><identifier>PMID: 23900255</identifier><language>eng</language><publisher>New York: Nature Publishing Group US</publisher><subject>631/114 ; 631/1647/514/2184 ; 631/208/726/649 ; 631/67/69 ; Bioinformatics ; Biological Microscopy ; Biological Techniques ; Biomedical Engineering/Biotechnology ; Cancer ; Cancer genomics ; Computational biology ; Computational Biology - methods ; Computational biology and bioinformatics ; Genetic aspects ; Genetic variance ; Genetic Variation ; Genome, Human ; Genomics ; Genotype & phenotype ; Humans ; Life Sciences ; Mutation ; Neoplasms - genetics ; perspective ; Physiological aspects ; Proteomics ; Sequence annotation ; Tumors</subject><ispartof>Nature methods, 2013-08, Vol.10 (8), p.723-729</ispartof><rights>Springer Nature America, Inc. 2013</rights><rights>COPYRIGHT 2013 Nature Publishing Group</rights><rights>Copyright Nature Publishing Group Aug 2013</rights><rights>info:eu-repo/semantics/openAccess © Springer Nature Publishing AG. Gonzalez-Perez A, Mustonen V, Reva B, Ritchie GR, Creixell P, Karchin R et al. Computational approaches to identify functional genetic variants in cancer genomes. Nat Methods. 2013; 10(8):723-9. 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The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype.</description><subject>631/114</subject><subject>631/1647/514/2184</subject><subject>631/208/726/649</subject><subject>631/67/69</subject><subject>Bioinformatics</subject><subject>Biological Microscopy</subject><subject>Biological Techniques</subject><subject>Biomedical Engineering/Biotechnology</subject><subject>Cancer</subject><subject>Cancer genomics</subject><subject>Computational biology</subject><subject>Computational Biology - methods</subject><subject>Computational biology and bioinformatics</subject><subject>Genetic aspects</subject><subject>Genetic variance</subject><subject>Genetic Variation</subject><subject>Genome, Human</subject><subject>Genomics</subject><subject>Genotype & 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| fulltext | fulltext |
| identifier | ISSN: 1548-7091 |
| ispartof | Nature methods, 2013-08, Vol.10 (8), p.723-729 |
| issn | 1548-7091 1548-7105 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_3919555 |
| source | MEDLINE; Recercat; Nature Journals Online; SpringerLink Journals - AutoHoldings |
| subjects | 631/114 631/1647/514/2184 631/208/726/649 631/67/69 Bioinformatics Biological Microscopy Biological Techniques Biomedical Engineering/Biotechnology Cancer Cancer genomics Computational biology Computational Biology - methods Computational biology and bioinformatics Genetic aspects Genetic variance Genetic Variation Genome, Human Genomics Genotype & phenotype Humans Life Sciences Mutation Neoplasms - genetics perspective Physiological aspects Proteomics Sequence annotation Tumors |
| title | Computational approaches to identify functional genetic variants in cancer genomes |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-09T03%3A45%3A28IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Computational%20approaches%20to%20identify%20functional%20genetic%20variants%20in%20cancer%20genomes&rft.jtitle=Nature%20methods&rft.au=Gonzalez-Perez,%20Abel&rft.aucorp=International%20Cancer%20Genome%20Consortium%20Mutation%20Pathways%20and%20Consequences%20Subgroup%20of%20the%20Bioinformatics%20Analyses%20Working%20Group&rft.date=2013-08-01&rft.volume=10&rft.issue=8&rft.spage=723&rft.epage=729&rft.pages=723-729&rft.issn=1548-7091&rft.eissn=1548-7105&rft_id=info:doi/10.1038/nmeth.2562&rft_dat=%3Cgale_pubme%3EA338418710%3C/gale_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1415792459&rft_id=info:pmid/23900255&rft_galeid=A338418710&rfr_iscdi=true |