Salt‐stress priming enhances the tolerance of plants against subsequent exposure to a similar stress. Priming‐induced transcriptomic reprogramming is mediated by multiple epigenetic mechanisms, the best known of which is histone modifications. However, not much is known about other epigenetic responses. In this study, salt‐stress priming resulted in global DNA hypomethylation in the leaves of soybean seedlings. The DNA methyltransferase activities in primed seedlings were reduced, contributing to the overall DNA hypomethylation. Genes associated with the hypomethylated DNA regions in primed seedlings also showed a higher mean level of the active histone mark, histone 3 lysine 4 trimethylation (H3K4me3), and a lower mean level of the repressive histone mark, H3K4me2. Transcriptomic analyses supported that DNA hypomethylation played a role in fine‐tuning the chromatin status in primed seedlings to potentiate gene expressions. Motif and transcriptional network analyses revealed that DNA hypomethylation may facilitate the responses mediated by key transcription factors in the abscisic acid (ABA)‐dependent pathway. A pre‐treatment using a DNA methyltransferase inhibitor, 5‐azacytidine, could enhance salt tolerance in non‐primed soybean seedlings, similar to the priming effect, suggesting the role of DNA hypomethylation in salt‐stress priming. Overall, this research furthers our understanding of the epigenetic mechanisms involved in salt‐stress priming in soybean.

Dear Editor, The rapid development of sequencing technology in the last decade has ushered soybean (Glycine max and Glycine soja) research into the genomic age. Mining information out of these datasets is challenging for biologists without intensive bioinformatic training, but web applications with intuitive data retrieval and visualization functions can empower general users to access both the genome sequences and genomics resources. Although there are some online platforms for soybean ( Machado et al., 2020; Yu et al., 2022), they are primarily focused on RNA-Seq data and lack the association to gene regulation information, like chromatin accessibility. While the leading platform Soybase (https://soybase.org/) has deposited multiomic data, most of their data are based on prior versions of the soybean reference genome (Wm82 a1v1.1, Wm82 a2v1) ( Brown et al., 2021). Furthermore, none of the aforementioned platforms allow biologists to easily retrieve intergenic sequences (e.g. promoters) or design primers. With the increasing amount of sequencing data generated from various cultivars, there is a growing demand for a curated web portal hosting multiple reference genomes with associated multiomic data. To address this gap, we created an integrated online platform Wildsoydb DataHub (https://datahub.wildsoydb.org/) hosting four high-quality genome assemblies, including two G. max cultivars: Williams 82 (Wm82 a2v1 and Wm82 a4v1) ( Schmutz et al., 2010; Valliyodan et al., 2019) and Zhonghuang 13 (v2) ( Liu et al., 2020) and a G. soja cultivar: W05 (v1) ( Xie et al., 2019). We aim to provide an easy-to-use web interface for biologists to fully benefit from the genomic resources. Aside from comprehensive functional annotations of all the genes, the sequencing data from 21 soybean genomics studies and 11 public SRA BioProjects of various data types were integrated. Furthermore, a variety of functional modules were developed to provide an intuitive user-centric interface. Modules currently available on this platform include Gene Search, BLAST, Jbrowse, Synteny, SeqExtractor, and Primer3. All functions are aggregated to the Gene Search result and can also be invoked from their respective pages, allowing users to (1) retrieve functional annotations, genome sequences, and gene expressions; (2) cross-compare sequences of interest with BLAST and synteny analysis; (3) visualize expression and methylation levels as well as other genomics information from a well-organized browser; (4) design primers for selected genes; and (5) retrieve sequence from unannotated regions.

In metabarcoding research, such as taxon identification, phylogenetic placement plays a critical role. However, many existing phylogenetic placement methods lack comprehensive features for downstream analysis and visualization. Visualization tools often ignore placement uncertainty, making it difficult to explore and interpret placement data effectively. To overcome these limitations, we introduce a scalable approach using treeio and ggtree for parsing and visualizing phylogenetic placement data. The treeio‐ggtree method supports placement filtration, uncertainty exploration, and customized visualization. It enhances scalability for large analyses by enabling users to extract subtrees from the full reference tree, focusing on specific samples within a clade. Additionally, this approach provides a clearer representation of phylogenetic placement uncertainty by visualizing associated placement information on the final placement tree.