ArticleG3 (Bethesda, Md.)2024
PyBrOpS: a Python package for breeding program simulation and optimization for multi-objective breeding.
Article in G3 (Bethesda, Md.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Bridging tradition and innovation: a review of computer simulations in plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Steps to transform African opportunity crops into reality crops.Nature communications · 2026Review
- Index- and Pareto-based multi-trait selection identifies dual-purpose sorghum and pearl millet cultivars for arid agro-ecosystems of Saudi Arabia.Frontiers in plant science · 2026Article
- DiffBreed: automatic differentiation enables efficient gradient-based optimization of breeding strategies.Bioinformatics (Oxford, England) · 2025Article
- Genetic Gain and Inbreeding in Different Simulated Genomic Selection Schemes for Grain Yield and Oil Content in Safflower.Plants (Basel, Switzerland) · 2024Article
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Authors and funding
2 authors.
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Abstract
Plant breeding is a complex endeavor that is almost always multi-objective in nature. In recent years, stochastic breeding simulations have been used by breeders to assess the merits of alternative breeding strategies and assist in decision-making. In addition to simulations, visualization of a Pareto frontier for multiple competing breeding objectives can assist breeders in decision-making. This paper introduces Python Breeding Optimizer and Simulator (PyBrOpS), a Python package capable of performing multi-objective optimization of breeding objectives and stochastic simulations of breeding pipelines. PyBrOpS is unique among other simulation platforms in that it can perform multi-objective optimizations and incorporate these results into breeding simulations. PyBrOpS is built to be highly modular and has a script-based philosophy, making it highly extensible and customizable. In this paper, we describe some of the main features of PyBrOpS and demonstrate its ability to map Pareto frontiers for breeding possibilities and perform multi-objective selection in a simulated breeding pipeline.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.