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.

PMID: 33165532
PMCID: PMC8189684
Funding: - National Institutes of Health: P20GM103436-15, R21CA205778, UL1TR001998 - Kentucky Lung Cancer Research Program: PO2 415 1400004000, PO2 415 1600001032 - Biostatistics and Bioinformatics Shared Resource Facility of the University of Kentucky Markey Cancer Center: P30CA177558