SigProfilerMatrixGenerator

SigProfilerMatrixGenerator generates mutation matrices that classify single base substitutions, doublet base substitutions, insertions, deletions, and transcriptional strand bias to characterize mutational patterns and support mutational signature analysis in cancer genomes.

Key Features:

• Mutation types covered: Classifies single base substitutions (SBS), doublet base substitutions (DBS), insertions, and deletions across samples.
• Matrix outputs: Produces fourteen distinct mutational matrices that capture varied contextual classifications of small mutational events.
• Transcriptional strand bias: Incorporates transcriptional strand bias into matrix classifications for strand-specific mutational analyses.
• Efficiency and scalability: Optimized for speed and memory efficiency to process large-scale genomic datasets.
• Implementation languages: Implemented in Python with an available R wrapper package.

Scientific Applications:

• Cancer genomics research: Enables detailed cataloging of small mutational events to study mutational landscapes in cancer genomes.
• Etiology of mutational processes: Supports identification and elucidation of the etiology of underlying mutational processes.
• Mechanistic studies of genomic alteration: Provides deeper classification to gain insights into mechanisms driving genomic alterations in cancer.
• Studies of development, progression, and treatment: Facilitates analyses that relate mutational patterns to cancer development, progression, and therapeutic implications.

Methodology:

Generates fourteen mutation matrices by classifying SBS, DBS, insertions, and deletions across contexts and incorporating transcriptional strand bias into the matrix representations.