MutEnricher

MutEnricher performs somatic mutation enrichment analysis on whole-genome sequencing (WGS) data to identify and quantify enriched mutation burden in protein-coding and non-coding genomic regions.

Key Features:

• Comprehensive genome-wide analysis: Analyzes somatic mutations across WGS data including both protein-coding and non-coding regions.
• Dual-module architecture: Provides separate modules for coding and non-coding loci and calculates sample-specific and feature-specific background mutation rates.
• Enrichment statistics: Computes feature-level somatic mutation enrichment statistics and local "hotspot" mutation enrichments.
• Configurable analysis parameters: Allows customization of background rate calculations and enrichment-testing parameters.
• Parallelization: Supports parallel processing to accelerate computation on large WGS datasets.
• Python compatibility: Implemented in Python and compatible with Python 2 and Python 3.

Scientific Applications:

• Cancer driver discovery: Identification of novel cancer driver genes by detecting enriched somatic mutation burden in coding and non-coding regions.
• Regulatory element mutation analysis: Assessment of mutations in non-coding regulatory elements for potential contributions to tumorigenesis.
• Mutational landscape characterization: Characterization of mutational hotspots and enriched genomic features across cancers using WGS data.

Methodology:

Implemented in Python with separate coding and non-coding modules that compute sample-specific and feature-specific background mutation rates and derive feature-level and local "hotspot" enrichment statistics from WGS input, with support for parallel processing.