Genomic selection simulator that helps plant breeders predict plants with the best genomes:
Genomic selection is a new plant breeding method which uses the plants’ DNA data to create the better plants. This is more efficient than a traditional methods which just look at the appearances or the functions of the plants because those plants sometimes have bad DNA that produces bad plants in the next generation. It turns out that selecting the best plants based on genome data is more efficient. Using a very efficient algorithm, the simulator calculates the next good progeny based on the current genome and it repeats this until the progeny with the best genome is found. This tool will help the plant breeders make their breeding plan easily and much more efficiently. I used numpy for the math intensive part and the GUI is made using python/QT.
Developed as part of an undergradutate research at Iowa State University.
This is the research poster.
Core module is generic-linkage
- Takao Shibamoto - main developer
- Dr. Lizhi Wang - mentor
- ISU Industrial and Manufacturing Systems Engineering
- ISU University Honors Program