GMStool

"GMStool" is a tool for selecting optimal marker sets and predicting quantitative phenotypes in genomic prediction. With the surge in accessibility to genomic data, the ability to accurately predict organisms' phenotypes based on their genome has become increasingly important. GMStool addresses the challenge of choosing optimal markers, significantly influencing the accuracy of genomic prediction studies.

Key Features and Functionalities:

- Optimal Marker Selection: GMStool utilizes statistical and machine-learning methods to search for and select optimal markers heuristically. This process is crucial for enhancing the accuracy of predicting quantitative phenotypes.

- Integration with GWAS: The tool is based on insights gained from Genome-Wide Association Studies (GWAS), leveraging this rich source of genomic information to identify markers associated with specific phenotypes.

- Use of Statistical and Machine/Deep-Learning Models: GMStool employs a variety of prediction models, including both traditional statistical models and advanced machine/deep-learning approaches, to perform genomic predictions.

- Best Prediction Model Presentation: Among the various models tested, GMStool identifies and presents the best prediction model and the optimal set of markers, providing users with insights into the most effective combinations for phenotype prediction.