YAPSA

YAPSA analyzes somatic mutational signatures in cancer genomic datasets by decomposing mutational profiles into contributions from predefined somatic signatures and by analyzing stratified mutational catalogues to characterize underlying mutational processes.

Key Features:

• Signature analysis with Known Signatures (LCD): Employs Linear Combination Decomposition (LCD) to fit mutational profiles as linear combinations of predefined somatic signatures and quantify per-sample contributions.
• Signature Analysis on Stratified Mutational Catalogue (SMC): Performs Signature Analysis on Stratified Mutational Catalogue (SMC) to analyze stratified datasets and identify distribution of mutational processes across genomic contexts.
• R and Bioconductor implementation: Implemented in R as a Bioconductor package, enabling interoperability with other Bioconductor packages.

Scientific Applications:

• Driver mutation and pathway identification: Attributes mutation patterns to specific mutational processes to aid identification of driver mutations and implicated pathways.
• Cancer classification by mutational profile: Facilitates differentiation of cancer types or subtypes based on somatic mutational signature compositions.
• Environmental and treatment impact analysis: Enables investigation of the impact of environmental exposures or treatment regimens on mutation patterns through signature attribution.

Methodology:

Performs Linear Combination Decomposition (LCD) for fitting known signatures and Signature Analysis on Stratified Mutational Catalogue (SMC); implemented in R within the Bioconductor framework.