TRES

TRES ranks and evaluates single-nucleotide polymorphisms (SNPs) to select minimal informative marker subsets for population assignment and optimized SNP panel design.

Key Features:

• Computational Efficiency: Processes datasets containing millions of genotypes rapidly for high-dimensional data analysis.
• SNP Ranking and Evaluation: Employs locus-ranking algorithms to rank SNPs according to user-defined population groups to maximize information content.
• Bias-mitigating Algorithms: Implements a combination of locus-ranking algorithms that mitigate biases often associated with existing programs.
• Dataset Manipulation: Converts datasets into various file formats, splits data into training and testing sets, and generates subsets of selected SNPs for downstream analysis in tools such as GENECLASS.
• Optimized Panel Design: Enables selection of minimal SNP subsets to optimize cost-effective SNP panels applicable to species identification, wildlife management, and forensic investigations.

Scientific Applications:

• Population Assignment: Enables selection of SNPs that provide maximal discriminatory power for population assignment tasks.
• Conservation Biology: Supports species identification and informs conservation management by selecting discriminatory markers.
• Wildlife Management: Facilitates genetic monitoring and individual or species identification in wildlife management contexts.
• Forensic Investigations: Provides optimized SNP panels for precise genetic profiling in forensic investigations.
• Cost-effective Genomic Studies: Assists creation of optimized, cost-effective SNP panels for downstream genomic analyses.

Methodology:

TRES applies a combination of locus-ranking algorithms that mitigate biases, processes large-scale genotype datasets (millions of genotypes), converts file formats, splits data into training and testing sets, and generates SNP subsets for downstream analysis (e.g., GENECLASS).