Summary: Integrative analysis of single-cell RNA-sequencing (scRNA-seq) data with spatial data for the same species and organ would provide each cell sample with a predictive spatial location, which would facilitate biological study. However, publicly available spatial sequencing datasets for specific species and organs are rare and are often displayed in different formats. In this study, we introduce a new web-based scRNA-seq analysis tool, webSCST, that integrates well-organized spatial transcriptome sequencing datasets categorized by species and organs, provides a user-friendly interface for raw single-cell processing with popular integration methods and allows users to submit their raw scRNA-seq data once to obtain predicted spatial locations for each cell type.Availability and implementation: webSCST implemented in shiny with all major browsers supported is available at http://www.webscst.com. webSCST is also freely available as an R package at https://github.com/swsoyee/webSCST.

As one of the widespread RNA post-transcriptional modifications (PTCMs), 5-Methylcytosine (m5C) plays vital roles in better understanding of basic biological mechanisms and major disease treatments. In experiments, traditional high-throughput approaches to find m5C sites are usually expensive and laborious. Additionally, facing with a large number of RNA sequences, developing accurate computational methods to distinguish m5C and non-m5C sites is an efficient solution. Here we introduced a novel predictor, called iRNA-m5C_NB, to identify m5C sites in Home sapiens using Naive Bayes (NB) algorithm. In this method, unbalanced dataset Met935 is firstly analyzed using efficient hybrid-sampling strategy SMOTEEEN. Then top 57 features are selected by the ANOVA F-value from four kinds of well-performance feature extraction techniques, including Bi-profile Bayes (BPB), enhanced Nucleic Acid Composition (ENAC), electron-ion interaction pseudopotentials (EIIP) and mMGap_1. Based on the jackknife test, the evaluated recall for the unbalanced training dataset Met935 is up to 82.81% with MCC of 0.63. And for the independent dataset Test1157, the predictor still shows high recall of 70.06% and MCC of 0.34. It is the first m5C predictor constructed using the unbalanced dataset, and the recall scores are increased by 19.82% and 59.23% for jackknife and independent tests compared with the latest tool RNAm5CPred, respectively. We demonstrate that the proposed predictor iRNA-m5C_NB outperforms other start-of-art models, which hopes to be an efficient and reliable method to identify m5C sites.

As one of the common post-transcriptional modifications in tRNAs, dihydrouridine (D) has prominent effects on regulating the flexibility of tRNA as well as cancerous diseases. Facing with the expensive and time-consuming sequencing techniques to detect D modification, precise computational tools can largely promote the progress of molecular mechanisms and medical developments. We proposed a novel predictor, called iRNAD_XGBoost, to identify potential D sites using multiple RNA sequence representations. In this method, by considering the imbalance problem using hybrid sampling method SMOTEEEN, the XGBoost-selected top 30 features are applied to construct model. The optimized model showed high Sn and Sp values of 97.13% and 97.38% over jackknife test, respectively. For the independent experiment, these two metrics separately achieved 91.67% and 94.74%. Compared with iRNAD method, this model illustrated high generalizability and consistent prediction efficiencies for positive and negative samples, which yielded satisfactory MCC scores of 0.94 and 0.86, respectively. It is inferred that the chemical property and nucleotide density features (CPND), electron-ion interaction pseudopotential (EIIP and PseEIIP) as well as dinucleotide composition (DNC) are crucial to the recognition of D modification. The proposed predictor is a promising tool to help experimental biologists investigate molecular functions.

Representations that capture latent regulatory and functional features. Compared with task-specific models, FMs offer enhanced generalization, cross-species transferability, and scalability, making them particularly attractive for plant systems characterized by genomic complexity and limited functional annotations. Providing a systematic overview of this paradigm, Xu et al. present a mini-review synthesizing recent advances in foundation models for plant molecular biology. The review traces the evolution from general DNA language models to plant-specific tools and highlights key challenges unique to plant systems, including polyploidy, repetitive genomes, and sparse experimental annotations. By outlining future directions such as multimodal integration and computational efficiency, this work establishes a conceptual framework for understanding how FMs are redefining computational plant biology and guiding next-generation model development.Building on this FM paradigm, several contributions demonstrate how representation learning can be applied to concrete biological problems. Zhang et al. applied a DNABERT-2-based framework combined with gradient boosting to identify DNA N6-methyladenine modifications in rice, illustrating how foundation models can enhance epigenetic marker detection while mitigating data sparsity. This work exemplifies a broader shift toward pretraining-based strategies in plant genomics, with implications for cross-species prediction and regulatory annotation. Taken together, the contributions in this Research Topic highlight the transformative role of machine learning and foundation models in plant functional genomics. By advancing representation learning, model architecture, interpretability, and multi-omics integration, these studies move the field beyond traditional sequence-based annotation toward predictive, mechanism-aware, and application-oriented frameworks. Continued synergy between computational innovation and experimental validation will be essential for translating these advances into resilient, high-yield crops capable of meeting future agricultural and environmental challenges.