MutViz 2.0

MutViz 2.0 analyzes and visualizes mutation enrichment across user-selected genomic regions to characterize somatic mutational processes in cancer.

Key Features:

• Preloaded somatic SNV data: Includes somatic single nucleotide variant (SNV) datasets from public sources spanning multiple cancer types.
• Regional analysis of small genomic regions: Performs enrichment analysis on user-specified small regions such as promoters and transcription factor binding sites.
• Trinucleotide-context aware analysis: Considers the immediate trinucleotide sequence context surrounding mutations for context-dependent pattern assessment.
• Clinical annotation integration: Associates tumor sample clinical annotations with mutation data for sample-level analyses.
• Signature refitting for limited regions: Implements a method for refitting mutational signatures on restricted genomic regions to estimate contributions of individual mutational processes.
• Visualization of mutation enrichments: Produces visual representations of mutation enrichment across genomic regions and cancer types.

Scientific Applications:

• Mutational signature characterization: Deconvolves and refits mutational signatures to identify processes such as ultraviolet light exposure and cytosine deamination.
• Regional mutation enrichment detection: Detects and quantifies enrichment of somatic SNVs within specific genomic elements like promoters and transcription factor binding sites.
• Comparative analysis across tumor types: Compares mutation patterns and signature contributions across diverse cancer types using preloaded somatic SNV datasets.
• Translational biomarker and target discovery: Integrates clinical annotations with regional mutation patterns to support identification of potential biomarkers and therapeutic targets.

Methodology:

Uses preloaded public somatic SNV datasets; computes mutation enrichments in selected genomic regions while accounting for immediate trinucleotide sequence context; applies a signature refitting method on limited genomic regions and integrates clinical annotations for sample-level analyses, with results presented as visualizations.