synggen

synggen generates synthetic cancer sequencing datasets for whole-exome and targeted sequencing to support benchmarking of computational methods in translational cancer genomics and precision medicine.

Key Features:

• Fast and Scalable Data Generation: Produces large-scale synthetic datasets for whole-exome and targeted sequencing to meet high-throughput benchmarking needs.
• Realistic and Heterogeneous Datasets: Simulates phased germline single nucleotide polymorphisms (SNPs) and complex allele-specific somatic events to mimic tumor heterogeneity.
• Versatility Across Platforms: Generates datasets representative of distinct sequencing platforms to enable cross-platform evaluation of computational methods.

Scientific Applications:

• Benchmarking Computational Tools: Provides realistic synthetic datasets for rigorous testing and comparison of somatic genomic profiling and other computational methods.
• Precision Medicine Research: Supports translational cancer genomics and precision medicine studies that require controlled, complex datasets for method validation.

Methodology:

Employs algorithms to simulate phased germline SNPs and allele-specific somatic variations to generate large-scale synthetic whole-exome and targeted sequencing datasets.