MutationalPatterns

MutationalPatterns analyzes mutational processes from whole-genome sequencing data by extracting mutational patterns from catalogues of single and double base substitutions and small insertions and deletions to study underlying mutation accumulation mechanisms.

Key Features:

• Mutational signature identification: Identifies characteristic mutational signatures and allows comparisons between different tumor types and other datasets.
• Regional mutation spectra analysis: Performs regional mutation spectra analyses across genomic regions.
• Strand asymmetry detection: Detects strand asymmetry phenomena, such as lesion segregation.
• Damaging mutation likelihood: Determines how likely it is that a signature can cause damaging mutations that affect protein function.
• Stricter signature refitting: Supports stricter signature refitting on known signatures to prevent overfitting, achieving reliable refitting with as few as 50 mutations per signature.
• Bootstrapped signature refitting: Incorporates bootstrapped signature refitting to assess the robustness of signature analyses.
• Comprehensive visualization: Provides comprehensive analyses and visualization of mutational patterns.

Scientific Applications:

• Cancer genomics: Investigates mutational processes operative in cancer cells through analysis of somatic mutation records in tumor genomes.
• DNA repair mechanism studies: Analyzes genome mutation data from cell lines with deleted specific DNA repair processes to generate novel biological insights.
• Cancer diagnostics: Contributes to improved biological insights in mechanisms of mutation accumulation and aids cancer diagnostics through in-depth mutational analyses.
• Large-scale cancer dataset analysis: Applied to large cancer datasets to study mutational processes across diverse tumor types.

Methodology:

The package uses simulated mutation matrices containing varied signature contributions to validate refitting performance and incorporates bootstrapped signature refitting for robustness assessment.