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| 001 | 177481 | ||
| 005 | 20240320154138.0 | ||
| 024 | 7 | _ | |a 10.1038/s41467-019-13929-1 |2 doi |
| 024 | 7 | _ | |a pmid:32024818 |2 pmid |
| 024 | 7 | _ | |a pmc:PMC7002750 |2 pmc |
| 024 | 7 | _ | |a altmetric:75124889 |2 altmetric |
| 037 | _ | _ | |a DKFZ-2021-02568 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 500 |
| 100 | 1 | _ | |a Shuai, Shimin |b 0 |
| 245 | _ | _ | |a Combined burden and functional impact tests for cancer driver discovery using DriverPower. |
| 260 | _ | _ | |a [London] |c 2020 |b Nature Publishing Group UK |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1710945647_32161 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a siehe Correction: DKFZ Autoren affiliiert im PCAWG Consortium: https://inrepo02.dkfz.de/record/212435 / https://doi.org/10.1038/s41467-022-32343-8 |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 312 - Functional and structural genomics (POF3-312) |0 G:(DE-HGF)POF3-312 |c POF3-312 |f POF III |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, , Journals: inrepo01.inet.dkfz-heidelberg.de |
| 650 | _ | 7 | |a ADGRG6 protein, human |2 NLM Chemicals |
| 650 | _ | 7 | |a EEF1A2 protein, human |2 NLM Chemicals |
| 650 | _ | 7 | |a MEF2 Transcription Factors |2 NLM Chemicals |
| 650 | _ | 7 | |a MEF2B protein, human |2 NLM Chemicals |
| 650 | _ | 7 | |a Peptide Elongation Factor 1 |2 NLM Chemicals |
| 650 | _ | 7 | |a Receptors, G-Protein-Coupled |2 NLM Chemicals |
| 650 | _ | 2 | |a Algorithms |2 MeSH |
| 650 | _ | 2 | |a Genome, Human |2 MeSH |
| 650 | _ | 2 | |a Genomics: methods |2 MeSH |
| 650 | _ | 2 | |a Humans |2 MeSH |
| 650 | _ | 2 | |a MEF2 Transcription Factors: genetics |2 MeSH |
| 650 | _ | 2 | |a Mutation |2 MeSH |
| 650 | _ | 2 | |a Mutation Rate |2 MeSH |
| 650 | _ | 2 | |a Neoplasms: genetics |2 MeSH |
| 650 | _ | 2 | |a Peptide Elongation Factor 1: genetics |2 MeSH |
| 650 | _ | 2 | |a Receptors, G-Protein-Coupled: genetics |2 MeSH |
| 650 | _ | 2 | |a Software |2 MeSH |
| 650 | _ | 2 | |a Whole Genome Sequencing |2 MeSH |
| 700 | 1 | _ | |a PCAWGDrivers |b 1 |
| 700 | 1 | _ | |a FunctionalInterpretationWorkingGroup |b 2 |
| 700 | 1 | _ | |a Gallinger, Steven |b 3 |
| 700 | 1 | _ | |a Stein, Lincoln |b 4 |
| 700 | 1 | _ | |a PCAWGConsortium |b 5 |
| 773 | _ | _ | |a 10.1038/s41467-019-13929-1 |g Vol. 11, no. 1, p. 734 |0 PERI:(DE-600)2553671-0 |n 1 |p 734 |t Nature Communications |v 11 |y 2020 |x 2041-1723 |
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