MOAT

MOAT identifies genomic regions with an unexpectedly high mutation count to detect potential cancer driver mutations using a non-parametric, permutation-based approach.

Key Features:

• Non-parametric permutation testing: Uses permutation-based analysis that avoids explicit parametric modeling of background mutation rates.
• Background mutation rate assumption: Assumes the background mutation rate (BMR) varies smoothly with genomic features rather than requiring cell-type-matched covariates.
• Permutation targets: Performs large-scale random permutations of single-nucleotide variants or predefined target regions to assess mutation overburden.
• GPU acceleration: Employs graphics processing unit (GPU) acceleration to expedite permutations, achieving an approximately 250-fold speed-up compared to traditional methods.
• Robust burden analysis: Enables statistical assessment of mutation enrichment across genomic regions via extensive permutation sampling.

Scientific Applications:

• Cancer genomics: Identification of regions with mutation overburden to discover potential driver mutations involved in oncogenesis.
• Large-scale mutational landscape analysis: Application to large genomic datasets where covariate-independent burden testing and rapid computation are required.

Methodology:

MOAT applies non-parametric, permutation-based analysis by performing large-scale random permutations of single-nucleotide variants or target regions under the assumption that BMR varies smoothly with genomic features, and uses GPU acceleration to speed permutations (≈250-fold).