As one of the most important crops to supply the majority of plant oil and protein for the whole world, soybean is facing an increasing global demand. The reference genome of accession ‘‘Williams82’’opened the gate of genomics research in soybean (Schmutz et al., 2010). After that, vast multi-omics data were generated, thereby providing valuable resources for functional study and molecular breeding. Parts of these data have been collected in different soybean databases (see details in Supplemental Table 1), such as Soybase (Grant et al., 2010) and SoyKB (Joshi et al., 2012), which made valuable efforts to facilitate the wide utility of these data. Nevertheless, these existing databases poorly tackled multi-omics data integration and interactivity for soybean, provoking tremendous challenges for researchers to deal with these big omics data, particularly considering the unprecedented rate of data growth (Yang et al., 2021). Thus, constructing an integrated multi-omics database for soybean that provides a one-stop solution for big data mining with friendly interactivity is highly desired.

Here, we collect the reported high-quality omics data, including assembly genomes, graph pan-genome, resequencing, and phenotypic data of representative germplasms (Zhou et al., 2015; Fang et al., 2017; Liu et al., 2020); de-novo-assembled genomes of the species in the subgenus Glycine (Zhuang et al., 2022); transcriptomic and epigenomic data from different tissues, organs, and accessions (Shen et al., 2014, 2018, 2019); and knowledge of quantitative trait locus and genome-wide association study (GWAS)(Grant et al., 2010), and construct an integrated soybean multi-omics database, named SoyOmics (https://ngdc. cncb. ac. cn/soyomics). By equipping it with multiple analysis modules and toolkits, SoyOmics is of great utility to facilitate the global scientific community to fully use these big omics datasets for a wide range of soybean studies from fundamental functional investigation to molecular breeding.