Advanced multi-omics technologies offer much information that can uncover the regulatory mechanisms from genotype to phenotype. In soybean, numerous multi-omics databases have been published. Although they cover multiple omics, there are still limitations when it comes to the types and scales of omics datasets and analysis methods utilized. This study aims to address these limitations by collecting and integrating a comprehensive set of multi-omics datasets. This includes 38 genomes, transcriptomes from 435 tissue samples, 125 phenotypes from 6686 accessions, epigenome data involving histone modification, transcription factor binding, chromosomal accessibility and chromosomal interaction, as well as genetic variation data from 24 501 soybean accessions. Then, common analysis pipelines and statistical methods were applied to mine information from these multi-omics datasets, resulting in the successful establishment of a user-friendly multi-omics database called SoyMD (https://yanglab.hzau.edu.cn/SoyMD/#/). SoyMD provides researchers with efficient query options and analysis tools, allowing them to swiftly access relevant omics information and conduct comprehensive multi-omics data analyses. Another notable feature of SoyMD is its capability to facilitate the analysis of candidate genes, as demonstrated in the case study on seed oil content. This highlights the immense potential of SoyMD in soybean genetic breeding and functional genomics research.

Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. Addressing this biological complexity requires innovative and accurate tools for trait prediction. In response to this challenge, we have developed SoyDNGP, a deep learning-based model that offers significant advancements in the field of soybean trait prediction. Compared to existing methods, such as DeepGS and DNNGP, SoyDNGP boasts a distinct advantage due to its minimal increase in parameter volume and superior predictive accuracy. Through rigorous performance comparison, including prediction accuracy and model complexity, SoyDNGP represents improved performance to its counterparts. Furthermore, it effectively predicted complex traits with remarkable precision, demonstrating robust performance across different sample sizes and trait complexities. We also tested the versatility of SoyDNGP across multiple crop species, including cotton, maize, rice and tomato. Our results showed its consistent and comparable performance, emphasizing SoyDNGP's potential as a versatile tool for genomic prediction across a broad range of crops. To enhance its accessibility to users without extensive programming experience, we designed a user-friendly web server, available at http://xtlab.hzau.edu.cn/SoyDNGP. The server provides two features: 'Trait Lookup', offering users the ability to access pre-existing trait predictions for over 500 soybean accessions, and 'Trait Prediction', allowing for the upload of VCF files for trait estimation. By providing a high-performing, accessible tool for trait prediction, SoyDNGP opens up new possibilities in the quest for optimized soybean breeding.

In many plants, flowering time is influenced by daylength as an adaptive response. In soybean (Glycine max) cultivars, however, photoperiodic flowering reduces crop yield and quality in high-latitude regions. Understanding the genetic basis of wild soybean (Glycine soja) adaptation to high latitudes could aid breeding of improved cultivars. Here, we identify the Tof4 (Time of flowering 4) locus, which encodes by an E1-like protein, E1La, that represses flowering and enhances adaptation to high latitudes in wild soybean. Moreover, we found that Tof4 physically associates with the promoters of two important FLOWERING LOCUS T (FT2a and FT5a) and with Tof5 to inhibit their transcription under long photoperiods. The effect of Tof4 on flowering and maturity is mediated by FT2a and FT5a proteins. Intriguingly, Tof4 and the key flowering repressor E1 independently but additively regulate flowering time, maturity, and grain yield in soybean. We determined that weak alleles of Tof4 have undergone natural selection, facilitating adaptation to high latitudes in wild soybean. Notably, over 71.5% of wild soybean accessions harbor the mutated alleles of Tof4 or a previously reported gain-of-function allele Tof5^H2, suggesting that these two loci are the genetic basis of wild soybean adaptation to high latitudes. Almost no cultivated soybean carries the mutated tof4 allele. Introgression of the tof4-1 and Tof5^H2 alleles into modern soybean or editing E1 family genes thus represents promising avenues to obtain early-maturity soybean, thereby improving productivity in high latitudes.

Raffinose Family Oligosaccharides (RFOs) are a kind of polysaccharide containing D-galactose, and they widely exist in higher plants. Synthesis of RFOs begins with galactinol synthase (GolS; EC 2.4.1.123) to convert myo-inositol into galactinol. The subsequent formation of raffinose and stachyose are catalyzed by raffinose synthase (RS; EC 2.4.1.82) and stachyose synthase (STS; EC 2.4.1.67) using sucrose and galactinol as substrate, respectively. The hydrolysis of RFOs is finished by α-galactosidase (α-Gal; EC 3.2.1.22) to produce sucrose and galactose. Importance of RFOs metabolism have been summarized, e.g. In RFOs translocating plants, the phloem loading and unloading of RFOs are widely reported in mediating the plant development process. Interference function of RFOs synthesis or hydrolysis enzymes caused growth defect. In addition, the metabolism of RFOs involved in the biotic or abiotic stresses was discussed in this review. Overall, this literature summarizes our current understanding of RFOs metabolism and points out knowledge gaps that need to be filled in future.