rPIMS

rPIMS simplifies breed identification and genetic analysis in livestock using genomic data and machine learning approaches.

Key Features:

• Data Input Module: Supports input of genomic data for downstream analysis.
• Dimensionality Reduction: Performs reduction of high-dimensional genomic data.
• Phylogenetic Tree Construction: Generates phylogenetic trees to infer evolutionary relationships among livestock breeds.
• Population Structure Analysis: Analyzes genetic structure and diversity within and between populations.
• Machine Learning-Based Classification: Classifies livestock breeds using machine learning algorithms.

Scientific Applications:

• Livestock Breed Identification: Enables precise identification of livestock and poultry breeds using SNP data.
• Genetic Diversity Assessment: Supports identification and reporting of genetic diversity among livestock breeds.
• Evolutionary Relationship Inference: Facilitates visualization and analysis of evolutionary relationships among breeds through phylogenetic and population structure analysis.
• Genetic Resource Conservation: Supports conservation and utilization of livestock and poultry genetic resources through accurate breed classification.

Methodology:

rPIMS integrates large-scale genomic data with machine learning for breed identification, and was validated to achieve 100% classification accuracy in distinguishing 10 breeds using 860 SNPs.