PicNiC

PicNiC extracts ensemble-level cancer progression models from cross-sectional next-generation sequencing (NGS) cancer genomes to analyze selective advantage relationships among driver mutations and identify fitness-equivalent alterations.

Key Features:

• Modular Design: Implements a modular pipeline architecture to compose and run distinct analysis components.
• Sample Stratification: Incorporates methods for stratifying samples based on genetic and epigenetic characteristics.
• Driver Mutation Analysis: Analyzes selective advantage relationships among driver mutations at the ensemble level.
• Identification of Fitness-Equivalent Alterations: Detects mutually exclusive alterations interpreted as fitness-equivalent.
• Progression Model Inference: Infers cancer progression models from cross-sectional sequencing data.
• Multi-omics Integration: Integrates multiple -omics datasets, including genomic and epigenomic data.

Scientific Applications:

• Colorectal cancer modeling: Reproduces known colorectal cancer progression patterns and generates experimentally testable hypotheses.
• Translational research: Provides ensemble-level progression models that inform hypothesis generation and experimental validation in oncology.
• Mechanistic investigation: Elucidates coordination of genomic and epigenomic events in cancer initiation and development via multi-omics integration.

Methodology:

Applies machine learning techniques and biological insights to infer ensemble-level progression models from cross-sectional NGS data, integrates diverse -omics data, performs sample stratification, analyzes selective-advantage relationships among driver mutations, and identifies mutually exclusive fitness-equivalent alterations.