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   <dc:title>Combined burden and functional impact tests for cancer driver discovery using DriverPower</dc:title>
   <dc:creator>Shuai, Shimin</dc:creator>
   <dc:creator>PCAWG Drivers and Functional Interpretation Working Group</dc:creator>
   <dc:creator>Gallinger, Steven</dc:creator>
   <dc:creator>Stein, Lincoln D.</dc:creator>
   <dc:creator>PCAWG Consortium</dc:creator>
   <dc:creator>Deu-Pons, Jordi</dc:creator>
   <dc:creator>Frigola, Joan</dc:creator>
   <dc:creator>González-Pérez, Abel</dc:creator>
   <dc:creator>Muiños, Ferran</dc:creator>
   <dc:creator>Mularoni, Loris</dc:creator>
   <dc:creator>Pich, Oriol</dc:creator>
   <dc:creator>Reyes-Salazar, Iker</dc:creator>
   <dc:creator>Rubio-Perez, Carlota</dc:creator>
   <dc:creator>Sabarinathan, Radhakrishnan</dc:creator>
   <dc:creator>Tamborero, David</dc:creator>
   <dc:creator>Aymerich Gregorio, Marta</dc:creator>
   <dc:creator>Campo Güerri, Elias</dc:creator>
   <dc:creator>López Guillermo, Armando</dc:creator>
   <dc:creator>Gelpi Buchaca, Josep Lluís</dc:creator>
   <dc:creator>Rabionet Janssen, Raquel</dc:creator>
   <dc:subject>Processament de dades</dc:subject>
   <dc:subject>Càncer</dc:subject>
   <dc:subject>Genòmica</dc:subject>
   <dc:subject>Data processing</dc:subject>
   <dc:subject>Cancer</dc:subject>
   <dc:subject>Genomics</dc:subject>
   <dc:description>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:description>
   <dc:date>2024-02-19T15:10:37Z</dc:date>
   <dc:date>2024-02-19T15:10:37Z</dc:date>
   <dc:date>2020-02-05</dc:date>
   <dc:date>2024-02-19T15:10:37Z</dc:date>
   <dc:type>info:eu-repo/semantics/article</dc:type>
   <dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
   <dc:identifier>2041-1723</dc:identifier>
   <dc:identifier>https://hdl.handle.net/2445/207742</dc:identifier>
   <dc:identifier>728365</dc:identifier>
   <dc:language>eng</dc:language>
   <dc:relation>Reproducció del document publicat a: https://doi.org/https://doi.org/10.1038/s41467-019-13929-1</dc:relation>
   <dc:relation>Nature Communications, 2020, vol. 11, num.1, p. 1-12</dc:relation>
   <dc:relation>https://doi.org/https://doi.org/10.1038/s41467-019-13929-1</dc:relation>
   <dc:rights>cc-by (c)  Shuai, S. et al., 2020</dc:rights>
   <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
   <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
   <dc:format>12 p.</dc:format>
   <dc:format>application/pdf</dc:format>
   <dc:publisher>Nature Publishing Group</dc:publisher>
   <dc:source>Articles publicats en revistes (Fonaments Clínics)</dc:source>
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