%0 Journal Article
%A Shuai, Shimin
%A PCAWGDrivers
%A FunctionalInterpretationWorkingGroup
%A Gallinger, Steven
%A Stein, Lincoln
%A PCAWGConsortium
%T Combined burden and functional impact tests for cancer driver discovery using DriverPower.
%J Nature Communications
%V 11
%N 1
%@ 2041-1723
%C [London]
%I Nature Publishing Group UK
%M DKFZ-2021-02568
%P 734
%D 2020
%Z siehe Correction: DKFZ Autoren affiliiert im PCAWG Consortium: https://inrepo02.dkfz.de/record/212435   /  https://doi.org/10.1038/s41467-022-32343-8
%X 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
%K Algorithms
%K Genome, Human
%K Genomics: methods
%K Humans
%K MEF2 Transcription Factors: genetics
%K Mutation
%K Mutation Rate
%K Neoplasms: genetics
%K Peptide Elongation Factor 1: genetics
%K Receptors, G-Protein-Coupled: genetics
%K Software
%K Whole Genome Sequencing
%K ADGRG6 protein, human (NLM Chemicals)
%K EEF1A2 protein, human (NLM Chemicals)
%K MEF2 Transcription Factors (NLM Chemicals)
%K MEF2B protein, human (NLM Chemicals)
%K Peptide Elongation Factor 1 (NLM Chemicals)
%K Receptors, G-Protein-Coupled (NLM Chemicals)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:32024818
%2 pmc:PMC7002750
%R 10.1038/s41467-019-13929-1
%U https://inrepo02.dkfz.de/record/177481