Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

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

Nature communications - 5 Feb 2020

Shuai Shimin, Gallinger Steven, Stein Lincoln D

Abstract excerpt

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...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Genome, Human
Share to MEF2 Transcription Factors

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Mutation Rate

Share to Peptide Elongation Factor 1

Share to Receptors, G-Protein-Coupled

Share to Software

Share to Whole Genome Sequencing

Source

Open in PubMed Open full text

Identifiers

PMID: 32024818
DOI: 10.1038/s41467-019-13929-1
PMCID: PMC7002750

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Combined burden and functional impact tests for cancer driver discovery using DriverPower.PMID 32024818Read ↗

Cancer Driver Gene Discovery: A Patient-Level Statistical FrameworkSemantic proximity Read ↗

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Semantic proximity Read ↗

driveR: A Novel Method for Prioritizing Cancer Driver Genes Using Somatic Genomics DataSemantic proximity Read ↗

driveR: a novel method for prioritizing cancer driver genes using somatic genomics data.Semantic proximity Read ↗

Finding cancer driver mutations in the era of big data researchSemantic proximity Read ↗

Comprehensive identification of mutational cancer driver genes across 12 tumor types.Semantic proximity Read ↗

Cancer driver mutations: predictions and reality.Semantic proximity Read ↗

iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomesSemantic proximity Read ↗

The whole-genome panorama of cancer driversSemantic proximity Read ↗

Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences.Semantic proximity Read ↗

DriverMP enables improved identification of cancer driver genes.Semantic proximity Read ↗

iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing cancer driver genes in personal genomesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.