SCSsim

SCSsim simulates single-cell genome sequencing (SCS) datasets to model allele dropout (ADO), unbalanced allelic amplification, and sequencing-read artifacts for benchmarking SCS bioinformatics methods.

Key Features:

• simuVars: generates single-cell genomes from a reference by incorporating user-defined genomic variations at specific loci.
• learnProfile: infers sequencing platform-dependent profiles from real sequencing data to capture platform-specific biases.
• genReads: simulates Multiple Annealing and Looping-based Amplification Cycles (MALBAC) amplification and produces sequencing reads using inferred profiles.
• Allele dropout and allelic imbalance modeling: models allele dropout (ADO) and unbalanced amplification of alleles to reproduce single-cell amplification artifacts.
• Sequencing-read generation: produces reads that reflect platform-dependent error and bias profiles inferred from real data.
• Parallel processing: leverages parallel processing capabilities to accelerate simulation workloads.

Scientific Applications:

• Benchmarking SCS bioinformatics tools: generate realistic SCS datasets to evaluate and compare SCS-specific analysis methods.
• Evaluate allele dropout effects: quantify the impact of allele dropout (ADO) on downstream analyses.
• Assess variation detection efficiency: measure sensitivity and precision of variation detection under controlled simulation scenarios.
• Test genome coverage and amplification artifacts: examine genome coverage patterns and biases introduced by amplification, including unbalanced allelic amplification.

Methodology:

Simulation uses three explicit modules: simuVars introduces user-specified variants into a reference to produce single-cell genomes; learnProfile infers sequencing platform-dependent profiles from real data; genReads emulates MALBAC amplification and generates sequencing reads according to the inferred profiles, with support for parallel execution.