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bioRxiv · 10.1101/2024.09.19.613982

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

Li, J.·Ni, Q.·He, G.·Huang, J.·Chao, H.·Li, S.·Chen, M.·Hu, G.·Whelan, J.·Shou, H.
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Abstract

Soybean is a globally important crop for food, feed, oil, and nitrogen fixation. A variety of multi-omics research has been carried out generating datasets ranging from genotype to phenotype. To utilise this data, a soybean multi-omics database that has broad data coverage and comprehensive data analysis tools would be of value for basic and applied research. We present the soybean omics database (SoyOD), which integrates significant new datasets with existing public datasets for the most comprehensive collection of soybean multi-omics information. Compared to the existing soybean database, SoyOD incorporates an extensive collection of novel data derived from the deep-sequencing of 984 germplasms, 162 novel transcriptome datasets from seeds at different developmental stages, 53 phenotypic datasets, and over 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. SoyOD is a novel tool, as it can be used to mine and analyze 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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Li, J., Ni, Q., He, G., Huang, J., Chao, H., Li, S., Chen, M., Hu, G., Whelan, J., Shou, H.. 2024-09-20. SoyOD: An Integrated Soybean Multi-omics Database for Mining Genes and Biological Research. https://doi.org/10.1101/2024.09.19.613982

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