Crambled

Crambled analyzes whole-genome sequencing (WGS) data from tumor samples to evaluate cellularity, genome-wide copy number variation, and tumor subclonal structure.

Key Features:

• Cellularity and Copy Number Assessment: Evaluates tumor sample purity and genome-wide copy number variation from WGS data.
• Subclonal Behavior Analysis: Supports examination of tumor subclonal structure and heterogeneity.
• Comparison of Analytical Solutions: Enables comparison of conflicting results generated by different computational models affected by model identifiability issues.
• Visualization of Genomic Profiles: Generates graphical representations of sequencing depth, cellularity estimates, and clonality patterns derived from WGS datasets.

Scientific Applications:

• Cancer Genomics Analysis: Supports interpretation of tumor whole-genome sequencing data to characterize genomic alterations.
• Tumor Heterogeneity Studies: Enables investigation of subclonal populations within tumors.
• Copy Number Variation Profiling: Facilitates genome-wide analysis of copy number alterations in cancer genomes.

Methodology:

Crambled analyzes tumor whole-genome sequencing data to estimate cellularity, evaluate genome-wide copy number variation, and compare alternative computational solutions generated from sequencing coverage and clonality analyses.