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

OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes.

Bioinformatics (Oxford, England) - 15 Sept 2013

Tamborero David, Gonzalez-Perez Abel, Lopez-Bigas Nuria

Abstract excerpt

MOTIVATION: Gain-of-function mutations often cluster in specific protein regions, a signal that those mutations provide an adaptive advantage to cancer cells and consequently are positively selected during clonal evolution of tumours. We sought to determine the overall extent of this feature in cancer and the possibility to use this feature to identify drivers. RESULTS: We have developed OncodriveCLUST, a method...

Read the complete abstract on PubMed

Topics

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

Share to Cluster Analysis
Share to Genes, Neoplasm
Share to Genomics

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Mutation

Share to Neoplasm Proteins

Share to Sequence Analysis, Protein

Share to Software

Source

Identifiers

PMID: 23884480
DOI: 10.1093/bioinformatics/btt395

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes.PMID 23884480Read ↗

OncodriveCLUSTL: a sequence-based clustering method to identify cancer driversSemantic proximity Read ↗

A compendium of mutational cancer driver genes.Semantic proximity Read ↗

Oncodrive3D: Fast and accurate detection of structural clusters of somatic mutations under positive selectionSemantic proximity Read ↗

Systematic integration of protein-affecting mutations, gene fusions, and copy number alterations into a comprehensive somatic mutational profile.Semantic proximity Read ↗

OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations.Semantic proximity Read ↗

COSMIC: exploring the world's knowledge of somatic mutations in human cancer.Semantic proximity Read ↗

Systematic integration of protein affecting mutations, gene fusions, and copy number alterations into a comprehensive somatic mutational profileSemantic proximity Read ↗

Oncodomains: A protein domain-centric framework for analyzing rare variants in tumor samples.Semantic proximity Read ↗

Systematic analysis of somatic mutations driving cancer: uncovering functional protein regions in disease development.Semantic proximity Read ↗

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

OncodriveROLE classifies cancer driver genes in loss of function and activating mode of action.Semantic proximity Read ↗

The whole-genome panorama of cancer driversSemantic proximity Read ↗

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