【摘要】 单一的组学数据难以全面揭示基因调控性状的复杂分子机制，整合不同类型和层次的生物组学数据对于理解生物体内复杂的分子网络具有重要的意义。本数据集提供了包含21个动物物种的61191个个体水平组学数据（WGS、RNA-Seq、Ch IP-Seq和ATAC-Seq）和基因组注释信息，有效数据规模为2.8 TB。此外，本数据集还收录了基于深度学习算法得到的基因与表型实体识别数据。总的来说，该多组学数据集可用于农业重要性状的基因发掘和功能验证，能够为跨物种比较研究提供有价值的资源，也可更好地服务于动物经济性状关键基因识别模型构建以及算法研究。更多还原

【Abstract】 Single-omics data alone is insufficient to comprehensively reveal the complex molecular mechanisms of gene regulation traits. Integrating different types and levels of biological omics data is of great significance for understanding the complex molecular networks within organisms. This dataset provides individual-level omics data(WGS, RNA-Seq, Ch IP-Seq, and ATAC-Seq) and genome annotation information for 61,191 individuals from 21 animal species, with an effective data size of 2.8 TB. Additionally,this dataset includes gene and phenotype entity recognition data obtained through deep learning algorithms. Overall, this multi-omics dataset can be used for gene discovery and functional validation of agriculturally important traits, offering valuable resources for cross-species comparative studies. It also supports the construction of models for identifying key genes associated with economic traits in animals and facilitates algorithm research.更多还原