Spatial multi-omics techniques generate extensive spot-level profiles without accompanying spot-type labels, forcing biologists into labor-intensive manual annotation. Although large language models (LLMs) promise automated annotation, they are poorly equipped to handle high-dimensional numeric inputs, struggle to convert complex spatial-omics structures into interpretable text, and lack the specialized biological knowledge needed to avoid hallucinations. Moreover, the intrinsic sparsity and heterogeneity of spatial omics data undermine robust feature extraction and accurate spot-level niche label assignment. To address these challenges, we propose SpotTypeLLM, a framework for annotating spatial multi-omics spot-level niche types using a Bi-view Retrieval-Augmented Generation (BiRAG) tailored for LLMs. Specifically, SpotTypeLLM encodes spatial multi-omics data through scLLM-based embeddings, graph convolutional networks (GCNs), and multi-view attention module, and then selects spot-specific genes to construct the prompts for LLMs. We validate the accuracy and practical utility of our framework by comparing it with ten state-of-the-art methods on labeled spatial multi-omics datasets. The results demonstrate the effectiveness of SpotTypeLLM in overcoming the limitations of current annotation methods and enhancing spatial multi-omics analysis.

RNA's diverse biological functions, including gene regulation, enzymatic catalysis, signal transduction, and even therapeutic activity, ultimately depend on its three-dimensional (3D) structure. Departing from the classical task of inferring structure directly from sequence, this study investigates how to efficiently encode and generate RNA 3D structures, thereby enabling systematic exploration of the molecules' vast folding landscape. We propose a generative framework, RNA-FrameEncoder (RNA-FE). First, a Vector-Quantised Variational AutoEncoder compresses continuous RNA backbones into sequences of discrete structural tokens, “verbalizing” the geometry while suppressing high-frequency noise. Next, a Transformer language model is trained on these token sequences to capture their joint distribution, and a decoder subsequently projects the tokens back into Cartesian coordinates. This design sidesteps the complexity of explicit equivariance modelling, lowers computational cost, and markedly enhances the representational capacity. Comprehensive benchmarks show that RNA-FE can unconditionally generate realistic, novel, and designable RNA structures without any sequence or structural priors. Overall, RNA-FE provides a scalable approach for efficiently charting RNA 3D structural space and opens a data-efficient avenue for a range of downstream applications. The code and pretrained models are publicly available at: https://github.com/users/Hanbo24-bit/RNA-FE.

MOTIVATION: Accurate prediction of RNA secondary structure remains challenging due to the presence of pseudoknots, long-range dependencies, and limited labeled data. RESULTS: We propose TVAE, a novel framework that integrates a Transformer encoder with a Variational Autoencoder (VAE). The Transformer captures global dependencies in the sequence, while the VAE models structural variability by learning a probabilistic latent space. Unlike deterministic models, TVAE generates diverse and biologically plausible secondary structures, enabling more comprehensive structure discovery. To obtain discrete predictions, we introduce GHA-Pairing, a fast and biologically constrained base-pairing algorithm. TVAE demonstrates strong generalization across different RNA families and achieves state-of-the-art performance on benchmark datasets, reaching an F1 score of 0.89 and 83% accuracy, surpassing existing methods by 10%. These results highlight the advantage of probabilistic modeling for RNA structure prediction and its potential to enhance biological insights. AVAILABILITY AND IMPLEMENTATION: Code and pretrained models are available at https://github.com/mei-rna/TVAE-RNA. The released version of the dataset and models can also be accessed via DOI: 10.5281/zenodo.16946114.