Summary: Genomic selection (GS) is a new breeding strategy. Generally, traditional methods are used for predicting traits based on the whole genome. However, the prediction accuracy of these models remains limited because they cannot fully reflect the intricate nonlinear interactions between genotypes and traits. Here, a novel single nucleotide polymorphism (SNP) feature extraction technique based on the Pearson‐Collinearity Selection (PCS) is firstly presented and improves prediction accuracy across several known models. Furthermore, gene network prediction model (NetGP) is a novel deep learning approach designed for phenotypic prediction. It utilizes transcriptomic dataset (Trans), genomic dataset (SNP) and multi‐omics dataset (Trans + SNP). The NetGP model demonstrated better performance compared to other models in genomic predictions, transcriptomic predictions and multi‐omics predictions. NetGP multi‐omics model performed better than independent genomic or transcriptomic prediction models. Prediction performance evaluations using several other plants' data showed good generalizability for NetGP. Taken together, our study not only offers a novel and effective tool for plant genomic selection but also points to new avenues for future plant breeding research.

In eukaryotes, RNA N⁶‐methyladenosine (m⁶A) modification and microRNA (miRNA)‐mediated RNA silencing represent two critical epigenetic regulatory mechanisms. The m⁶A methyltransferase complex (MTC) and the microprocessor complex both undergo liquid–liquid phase separation to form nuclear membraneless organelles. Although m⁶A methyltransferase has been shown to positively regulate miRNA biogenesis, a mechanism of reciprocal regulation between the MTC and the microprocessor complex has remained elusive. Here, we demonstrate that the MTC and the microprocessor complex associate with each other through the METHYLTRANSFERASE B (MTB)–SERRATE (SE) interacting module. Knockdown of MTB impaired miRNA biogenesis by diminishing microprocessor complex binding to primary miRNAs (pri‐miRNAs) and their respective MIRNA loci. Additionally, loss of SE function led to disruptions in transcriptome‐wide m⁶A modification. Further biochemical assays and fluorescence recovery after photobleaching (FRAP) assay indicated that SE enhances the liquid–liquid phase separation and solubility of the MTC. Moreover, the MTC exhibited enhanced retention on chromatin and diminished binding to its RNA substrates in the se mutant background. Collectively, our results reveal the substantial regulatory interplay between RNA m⁶A modification and miRNA biogenesis.

The scope and function of RNA modifications in model plant systems have been extensively studied, resulting in the identification of an increasing number of novel RNA modifications in recent years. Researchers have gradually revealed that RNA modifications, especially N⁶-methyladenosine (m⁶A), which is one of the most abundant and commonly studied RNA modifications in plants, have important roles in physiological and pathological processes. These modifications alter the structure of RNA, which affects its molecular complementarity and binding to specific proteins, thereby resulting in various of physiological effects. The increasing interest in plant RNA modifications has necessitated research into RNA modifications and associated datasets. However, there is a lack of a convenient and integrated database with comprehensive annotations and intuitive visualization of plant RNA modifications. Here, we developed the Plant RNA Modification Database (PRMD; http://bioinformatics.sc.cn/PRMD and http://rnainformatics.org.cn/PRMD) to facilitate RNA modification research. This database contains information regarding 20 plant species and provides an intuitive interface for displaying information. Moreover, PRMD offers multiple tools, including RMlevelDiff, RMplantVar, RNAmodNet and Blast (for functional analyses), and mRNAbrowse, RNAlollipop, JBrowse and Integrative Genomics Viewer (for displaying data). Furthermore, PRMD is freely available, making it useful for the rapid development and promotion of research on plant RNA modifications.

Base editing enables precise nucleotide substitutions within a relatively broad editing window (5–6 nucleotides). However, considerable bystander editing significantly compromise its accuracy. Point mutagenesis, a powerful approach for gradient‐tuning protein function, facilitates the generation of diverse plant phenotypes to meet the demands of complex environments and consumer preferences. Here, a series of plant base editors is engineered by fusing three optimized TadA8e variants, TadA9, TadA‐LM, and TadA‐dual, with a PAM‐flexible SpRY nickase (SpRYn, with 5′‐NNN PAM recognition). These editors enable A‐to‐G, C‐to‐T, and dual‐base (simultaneous A‐to‐G and C‐to‐T) conversions within a highly condensed active window (1–3 nucleotides). Performance evaluations reveal that the TadDBE (TadA Dual‐Base Editor) achieves the most robust outcomes, delivering dual‐base editing efficiencies ranging from 2.3% to 61.4%, while maintaining minimal off‐target activity. Utilizing TadDBE, targeted point mutagenesis is performed on OsBadh2 , a gene encoding betaine aldehyde dehydrogenase that plays a critical role in the biosynthesis of 2‐acetyl‐1‐pyrroline (2‐AP), a key aromatic compound. This approach yields rice lines exhibiting gradient‐tuned aromatic profiles and optimized levels of 2‐AP and γ‐aminobutyric acid (GABA). These evolved TadA‐derived editors provide a precise, PAM‐flexible platform for base editing and represent a versatile strategy for generating genome‐edited plants with gradient‐tuned agronomic traits.