MEScan
MEScan performs genome-scale mutual exclusivity analysis of cancer mutations to detect mutually exclusive gene alterations and help distinguish driver from passenger mutations.
Key Features:
- Statistical Rigor: Incorporates a statistical test for mutual exclusivity that adjusts for background mutation rates and the impact of highly mutated genes.
- Computational Efficiency: Operates at least two orders of magnitude faster than most existing methods, enabling genome-wide analyses.
- False Discovery Rate Control: Implements a multi-step procedure to control false discovery rates during genome-wide screening.
- Genome-Wide Capability: Performs comprehensive analysis across the entire genome rather than being limited to pre-selected gene sets.
Scientific Applications:
- Discovery of mutually exclusive gene sets: Applied to cancer cohorts, including ovarian cancer data from The Cancer Genome Atlas (TCGA), to identify biologically meaningful mutually exclusive gene sets.
- Distinguishing driver and passenger mutations: Helps prioritize candidate driver mutations by identifying mutually exclusive alteration patterns indicative of functional redundancies.
- Insight into driver relationships and therapeutic targets: Reveals relationships among driver mutations that can inform molecular mechanisms and aid identification of potential therapeutic targets.
Methodology:
Data preparation of mutation data (e.g., TCGA), application of a fast statistical test for mutual exclusivity that accounts for background mutation rates and highly mutated genes, and a multi-step genome-wide screening procedure controlling the false discovery rate.
Topics
Details
- Tool Type:
- command-line tool, library
- Programming Languages:
- R, Python, C
- Added:
- 1/18/2021
- Last Updated:
- 11/24/2024
Operations
Publications
Liu S, Liu J, Xie Y, Zhai T, Hinderer EW, Stromberg AJ, Vanderford NL, Kolesar JM, Moseley HNB, Chen L, Liu C, Wang C. MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations. Bioinformatics. 2020;37(9):1189-1197. doi:10.1093/bioinformatics/btaa957. PMID:33165532. PMCID:PMC8189684.