数字农科院2.0

rPIMS: a ShinyR package for the precision identification and modelling of livestock breeds using genomic data and machine learning approaches

文献类型： 外文期刊

作者： Zhao, Yuhetian;Liu, Xuexue;Liang, Benmeng;Jiang, Lin

作者机构：

期刊名称： BIOINFORMATICS ADVANCES

ISSN：

年卷期： 2025 年 5 卷 1 期

页码：

收录情况： ESCI（2025版）

摘要： Accurate breed identification serves is a crucial cornerstone for the conservation and utilization of livestock and poultry genetic resources. The identification of breeds based on a variety of information sources and analytical methods has been extensively applied in the domain of animal genetics and breeding. Recently, the integration of large-scale genomic data with machine learning has become increasingly prevalent for breed identification tasks. However, such projects typically require extensive sequencing data and expertise in bioinformatics. To address this, we introduce rPIMS, a comprehensive tool designed to simplify breed identification and genetic analysis. With intuitive modules for data input, dimensionality reduction, phylogenetic tree construction, population structure analysis, and machine learning-based classification, rPIMS has the capacity to streamlines the analytical process for researchers. It promotes collaboration, facilitates efficient data sharing, and enhances the ability to identify and report genetic diversity and evolutionary relationships among livestock breeds. We performed a validation analysis to confirm that rPIMS achieved 100% classification accuracy in distinguishing 10 breeds using only 860 SNPs. In summary, rPIMS significantly simplifies complex model-building processes, making breed classification and genetic structure visualization accessible and intuitive to users.

分类号：

  • 相关文献
作者其他论文 更多>>