SoyOD: An Integrated Soybean Multi-omics Database for Mining Genes and Biological Research

文献类型： 外文期刊

第一作者： Li, Jie

作者： Li, Jie;Ni, Qingyang;He, Guangqi;Huang, Jiale;Chao, Haoyu;Li, Sida;Chen, Ming;Whelan, James;Shou, Huixia;Li, Jie;Chen, Ming;Whelan, James;Shou, Huixia;Hu, Guoyu

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关键词： Soybean; Database; Genome; Transcriptome; Phenome

期刊名称：GENOMICS PROTEOMICS & BIOINFORMATICS （ 影响因子：7.9； 五年影响因子：9.1 ）

ISSN： 1672-0229

年卷期： 2025 年 22 卷 6 期

页码：

收录情况： SCI

摘要： Soybean is a globally important crop for food, feed, oil, and nitrogen fixation. A variety of multi-omics studies have been carried out, generating datasets ranging from genotype to phenotype. In order to efficiently utilize these data for basic and applied research, a soybean multi-omics database with extensive data coverage and comprehensive data analysis tools was established. The Soybean Omics Database (SoyOD) integrates important new datasets with existing public datasets to form the most comprehensive collection of soybean multi-omics information. Compared to existing soybean databases, SoyOD incorporates an extensive collection of novel data derived from the deep-sequencing of 984 germplasms, 162 novel transcriptomic datasets from seeds at different developmental stages, 53 phenotypic datasets, and more than 2500 phenotypic images. In addition, SoyOD integrates existing data resources, including 59 assembled genomes, genetic variation data from 3904 soybean accessions, 225 sets of phenotypic data, and 1097 transcriptomic sequences covering 507 different tissues and treatment conditions. Moreover, SoyOD can be used to mine candidate genes for important agronomic traits, as shown in a case study on plant height. Additionally, powerful analytical and easy-to-use toolkits enable users to easily access the available multi-omics datasets, and to rapidly search genotypic and phenotypic data in a particular germplasm. The novelty, comprehensiveness, and user-friendly features of SoyOD make it a valuable resource for soybean molecular breeding and biological research. SoyOD is publicly accessible at https://bis.zju.edu.cn/soyod.

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