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http://hdl.handle.net/2445/207742
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DC Field | Value | Language |
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dc.contributor.author | Shuai, Shimin | - |
dc.contributor.author | PCAWG Drivers and Functional Interpretation Working Group | - |
dc.contributor.author | Gallinger, Steven | - |
dc.contributor.author | Stein, Lincoln D. | - |
dc.contributor.author | PCAWG Consortium | - |
dc.contributor.author | Deu-Pons, Jordi | - |
dc.contributor.author | Frigola, Joan | - |
dc.contributor.author | González-Pérez, Abel | - |
dc.contributor.author | Muiños, Ferran | - |
dc.contributor.author | Mularoni, Loris | - |
dc.contributor.author | Pich, Oriol | - |
dc.contributor.author | Reyes-Salazar, Iker | - |
dc.contributor.author | Rubio-Perez, Carlota | - |
dc.contributor.author | Sabarinathan, Radhakrishnan | - |
dc.contributor.author | Tamborero, David | - |
dc.contributor.author | Aymerich Gregorio, Marta | - |
dc.contributor.author | Campo Güerri, Elias | - |
dc.contributor.author | López Guillermo, Armando | - |
dc.contributor.author | Gelpi Buchaca, Josep Lluís | - |
dc.contributor.author | Rabionet Janssen, Raquel | - |
dc.date.accessioned | 2024-02-19T15:10:37Z | - |
dc.date.available | 2024-02-19T15:10:37Z | - |
dc.date.issued | 2020-02-05 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.uri | http://hdl.handle.net/2445/207742 | - |
dc.description.abstract | The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower’s background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery. | - |
dc.format.extent | 12 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isformatof | Reproducció del document publicat a: https://doi.org/https://doi.org/10.1038/s41467-019-13929-1 | - |
dc.relation.ispartof | Nature Communications, 2020, vol. 11, num.1, p. 1-12 | - |
dc.relation.uri | https://doi.org/https://doi.org/10.1038/s41467-019-13929-1 | - |
dc.rights | cc-by (c) Shuai, S. et al., 2020 | - |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | - |
dc.source | Articles publicats en revistes (Fonaments Clínics) | - |
dc.subject.classification | Processament de dades | - |
dc.subject.classification | Càncer | - |
dc.subject.classification | Genòmica | - |
dc.subject.other | Data processing | - |
dc.subject.other | Cancer | - |
dc.subject.other | Genomics | - |
dc.title | Combined burden and functional impact tests for cancer driver discovery using DriverPower | - |
dc.type | info:eu-repo/semantics/article | - |
dc.type | info:eu-repo/semantics/publishedVersion | - |
dc.identifier.idgrec | 728365 | - |
dc.date.updated | 2024-02-19T15:10:37Z | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
Appears in Collections: | Articles publicats en revistes (Fonaments Clínics) Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer) Articles publicats en revistes (Institut de Recerca Biomèdica (IRB Barcelona)) |
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255385.pdf | 663.31 kB | Adobe PDF | View/Open |
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