phastSim

phastSim simulates sequence evolution efficiently for pandemic-scale genomic datasets, enabling simulation along phylogenies with up to 100,000 tips to support genomic epidemiology such as SARS-CoV-2 studies.

Key Features:

• Efficient Simulation Algorithm: Employs a Gillespie-based algorithm optimized for phylogenetic trees with typically short branches and scalable to phylogenies with up to 100,000 tips.
• Multi-layered Search Tree: Implements an efficient multi-layered search tree structure that exploits the sparsity of mutations per branch to reduce computational cost.
• Model Support: Supports a variety of evolutionary models, including indel models and hypermutatability models relevant to SARS-CoV-2 evolution.
• Rate and Process Variation: Accommodates variation in mutation rates and non-stationary evolution, and models indels.
• Integration: Provides integration with other Python packages for inclusion in bioinformatics workflows.
• Simulation of Related Genomes: Enables simulation of large numbers of closely related genomes for genomic epidemiology analyses.

Scientific Applications:

• Genomic Epidemiology: Simulating pandemic datasets such as SARS-CoV-2 to study viral spread and evolution.
• Method Benchmarking: Generating simulated sequence data to benchmark phylogenetic and genomic data analysis methods.
• Statistical Inference Support: Producing simulated evolutionary histories to support statistical inference of evolutionary scenarios.

Methodology:

Uses a Gillespie-based approach with an efficient multi-layered search tree and implements indel models, hypermutatability models, variation in mutation rates, and non-stationary evolution.