ChromaX

ChromaX simulates genetic recombination, computes genomic estimated breeding values (GEBV), and models selection processes to support simulation and optimization of plant and animal breeding programs.

Key Features:

• Python library: Implemented in Python for integration into bioinformatics workflows.
• Genetic recombination simulation: Simulates recombination events across genomes for breeding program simulations.
• Genomic estimated breeding value (GEBV) calculation: Computes GEBVs for individuals from genomic data to inform selection decisions.
• Selection process modeling: Models selection schemes and selection responses within breeding populations.
• GPU acceleration: Leverages GPU processing to accelerate simulations, with reported speedups of up to two orders of magnitude compared to standard hardware.
• Large-scale dataset handling: Supports simulation and analysis of large datasets for extensive in silico experiments.
• Simulation of breeding outcomes: Enables exploration of breeding outcomes under varying selection pressures and recombination scenarios.
• Integrated workflow components: Integrates recombination simulation, GEBV computation, and selection modeling into a cohesive computational workflow.

Scientific Applications:

• Plant breeding simulations: Simulate and optimize selection strategies and breeding schemes in crop species.
• Animal breeding simulations: Simulate and optimize selection strategies and breeding schemes in livestock species.
• Predicting genetic outcomes: Predict genetic outcomes of crosses and selection in silico.
• Optimizing selection strategies: Evaluate and optimize selection strategies to improve genetic gain.
• Assessing recombination and selection effects: Assess effects of recombination patterns and selection pressures on genotype development at scale.

Methodology:

Simulation of genetic recombination, computation of genomic estimated breeding values (GEBV), modeling of selection processes, and GPU-accelerated computation for large-scale simulations.