Combined burden and functional impact tests for cancer driver discovery using DriverPower

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Combined burden and functional impact tests for cancer driver discovery using DriverPower. / Shuai, Shimin; PCAWG Drivers and Functional Interpretation Working Group; Gallinger, Steven et al.

I: Nature Communications, Bind 11, Nr. 1, 734, 2020.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Shuai, S, PCAWG Drivers and Functional Interpretation Working Group, Gallinger, S, Stein, L & PCAWG Consortium 2020, 'Combined burden and functional impact tests for cancer driver discovery using DriverPower', Nature Communications, bind 11, nr. 1, 734. https://doi.org/10.1038/s41467-019-13929-1

APA

Shuai, S., PCAWG Drivers and Functional Interpretation Working Group, Gallinger, S., Stein, L., & PCAWG Consortium (2020). Combined burden and functional impact tests for cancer driver discovery using DriverPower. Nature Communications, 11(1), [734]. https://doi.org/10.1038/s41467-019-13929-1

CBE

Shuai S, PCAWG Drivers and Functional Interpretation Working Group, Gallinger S, Stein L, PCAWG Consortium. 2020. Combined burden and functional impact tests for cancer driver discovery using DriverPower. Nature Communications. 11(1):Article 734. https://doi.org/10.1038/s41467-019-13929-1

MLA

Vancouver

Shuai S, PCAWG Drivers and Functional Interpretation Working Group, Gallinger S, Stein L, PCAWG Consortium. Combined burden and functional impact tests for cancer driver discovery using DriverPower. Nature Communications. 2020;11(1):734. doi: 10.1038/s41467-019-13929-1

Author

Shuai, Shimin ; PCAWG Drivers and Functional Interpretation Working Group ; Gallinger, Steven et al. / Combined burden and functional impact tests for cancer driver discovery using DriverPower. I: Nature Communications. 2020 ; Bind 11, Nr. 1.

Bibtex

@article{662c9b80f6e945d99d29b792fae6130b,
title = "Combined burden and functional impact tests for cancer driver discovery using DriverPower",
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{\textquoteright}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.",
keywords = "ACETYLATION, BINDING, CHROMATIN ORGANIZATION, EXPRESSION, HETEROGENEITY, MOF, PATHOGENICITY, RECURRENT, SOMATIC MUTATION, VARIANTS",
author = "Shimin Shuai and {PCAWG Drivers and Functional Interpretation Working Group} and Steven Gallinger and Lincolm Stein and {PCAWG Consortium} and Johanna Bertl and Qianyun Guo and Asger Hobolth and Henrik Hornsh{\o}j and Juul, {Randi Istrup} and Tobias Madsen and Nielsen, {Morten Muhlig} and Pedersen, {Jakob Skou}",
year = "2020",
doi = "10.1038/s41467-019-13929-1",
language = "English",
volume = "11",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
number = "1",

}

RIS

TY - JOUR

T1 - Combined burden and functional impact tests for cancer driver discovery using DriverPower

AU - Shuai, Shimin

AU - PCAWG Drivers and Functional Interpretation Working Group

AU - Gallinger, Steven

AU - Stein, Lincolm

AU - PCAWG Consortium

AU - Bertl, Johanna

AU - Guo, Qianyun

AU - Hobolth, Asger

AU - Hornshøj, Henrik

AU - Juul, Randi Istrup

AU - Madsen, Tobias

AU - Nielsen, Morten Muhlig

AU - Pedersen, Jakob Skou

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - ACETYLATION

KW - BINDING

KW - CHROMATIN ORGANIZATION

KW - EXPRESSION

KW - HETEROGENEITY

KW - MOF

KW - PATHOGENICITY

KW - RECURRENT

KW - SOMATIC MUTATION

KW - VARIANTS

UR - http://www.scopus.com/inward/record.url?scp=85079072523&partnerID=8YFLogxK

U2 - 10.1038/s41467-019-13929-1

DO - 10.1038/s41467-019-13929-1

M3 - Journal article

C2 - 32024818

AN - SCOPUS:85079072523

VL - 11

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 734

ER -