Journal of Agricultural Big Data >
A Multi-Omics Dataset for Functional Gene Mining in Animals
Received date: 2024-06-06
Accepted date: 2024-09-13
Online published: 2025-02-05
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, ChIP-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.
Data summary:
| Item | Description |
|---|---|
| Dataset name | A Multi-Omics Dataset for Functional Gene Mining in Animals |
| Specific subject area | Agronomy |
| Research topic | Animal Multi-Omics Dataset |
| Time range | 2000-2022 |
| Data types and technical formats | .txt,.vcf, ped, map, bed, bim, fam |
| Dataset stucture | The dataset consists of five parts: Functional annotation information for 403,216 genes across 21 species. Genomic variation data for 10,835 individuals from 21 species, encompassing 877.59 million variations. Gene expression matrix data for 44,638 individuals from 21 species. Epigenetic signal matrix data for 5,718 individuals from 21 species, including 124 markers such as H3K27ac. The pre-labeled gene and phenotype data of 2794237 articles from 21 species. |
| Volume of dataset | 2.8 TB |
| Key index in dataset | Gene functional annotation, genomic variation information, gene expression matrices, epigenetic signal matrices, gene and phenotypic pre-labeled data |
| Data accessibility | https://cstr.cn/17058.11.sciencedb.agriculture.00024 https://doi.org/10.57760/sciencedb.agriculture.00024 PUBLIC, CC BY-NC 4.0 |
| Financial support | National Natural Science Foundation of China General Program (32272841); Hubei International Science and technology cooperation project (2022EHB055) |
LIU Hong, DOU JingWen, WANG Yue, LIAO Yong, LIU XiaoLei, LI XinYun, ZHAO ShuHong, FU YuHua . A Multi-Omics Dataset for Functional Gene Mining in Animals[J]. Journal of Agricultural Big Data, 2025 , 7(1) : 96 -106 . DOI: 10.19788/j.issn.2096-6369.100039
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