Biological sequences, including RNAs and proteins, share similarities with natural languages, enabling the application of advanced language models to various biological tasks. However, due to its flexibility and lack of experimental data, RNA is a particularly challenging biological ``language'' compared to other biological sequences like proteins. RNA multiple sequence alignments (MSAs), which align evolutionarily related RNA sequences, can greatly enhance RNA biology modeling, as evidenced by their significant roles in structure prediction and function annotation. This raises the question of whether RNA MSAs can also benefit RNA design, which remains unexplored. This paper introduces RMSAGen, a model comprising RMSA-Encoder and RMSA-Decoder, that leverages MSAs to design functional RNA sequences. RMSA-Encoder effectively extracts MSA features, enhancing performance in functional prediction and solvent accessibility prediction tasks and supporting RMSA-Decoder in accurate RNA generation. RMSAGen can design RNA sequences that effectively bind to target RNA-binding proteins, and the design performance improves with an increasing number of sequences. In addition, the ribozymes designed with structural features by RMSAGen show strong computational metrics and exhibit biological activity during gel electrophoresis. These results highlight the effectiveness of RMSAGen, establishing it as a powerful tool and a new direction for RNA design.

Biological foundation models (BioFMs), pretrained on large-scale biological sequences, have recently shown strong potential in providing meaningful representations for diverse downstream bioinformatics tasks. However, such models often rely on millions to billions of training sequences and billions of parameters, resulting in prohibitive computational costs and significant barriers to reproducibility and accessibility—particularly for academic labs. To address these challenges, we investigate the feasibility of data pruning for BioFM pretraining and propose a post-hoc influence-guided data pruning framework tailored to biological domains. Our approach first introduces a subset-based self-influence formulation that enables efficient estimation of sample importance at low computational cost. Built upon this, we propose two simple yet effective selection strategies: Top-k Influence (Top I) and Coverage-Centric Influence (CCI). Then, we empirically validate our method on two representative BioFMs: RNA-FM and ESM-C. For RNA, our framework consistently outperforms random selection baselines under an extreme pruning rate of over 99%, which displays our framework's effectiveness. Furthermore, we demonstrate the generalizability of our framework on protein-related tasks using ESM-C. Specifically, our coreset even outperforms random 10x subsets in both RNA and protein settings, revealing substantial redundancy in biological sequence datasets. These findings underscore the potential of influence-guided data pruning to substantially reduce the computational cost of BioFM pretraining, paving the way for more efficient, accessible, and sustainable biological AI research.

Jiyue Jiang, Yitao Xu, Zikang Wang, Yihan Ye, Yanruisheng Shao, Yuheng Shan, Jiuming Wang, Xiaodan Fan, Jiao Yuan, Yu Li. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

The widespread adoption of high-throughput sequencing technologies and multi-omics approaches has led to rapid accumulation of genomic, transcriptomic, proteomic, and even single-cell multimodal datasets, resulting in an exponential growth of biological data. The massive scale and inherent complexity of these datasets pose significant challenges for data management, analysis, and interpretation in the field of bioinformatics. Concurrently, artificial intelligence (AI) techniques, particularly deep learning and reinforcement learning, have achieved groundbreaking advances in medical diagnostics, drug discovery, and genomic analyses, providing novel theoretical tools and analytical paradigms for bioinformatics research. AI techniques are now extensively applied to DNA, RNA, and protein sequence prediction and design, 3D structural elucidation, functional annotation, integrative analysis of multi-omics data, and personalized drug design for precision medicine, significantly advancing biological research. This review systematically summarizes recent research progress and representative applications of AI techniques in bioinformatics, specifically discussing suitable scenarios and advantages of traditional machine learning algorithms, deep learning models, and reinforcement learning methods. We highlight AI's transformative impact with quantitative metrics from landmark achievements: accurate near-atomic protein structure prediction (median 0.96 Å on CASP14), robust single-cell modeling (AvgBIO $\approx $ 0.82), high protein design success rates (up to 92%), and sensitive cancer detection (Area Under Curve (AUC) $\approx $ 0.93). Furthermore, the paper provides an in-depth analysis of the latest advancements of AI in specific tasks, including biomedical text mining, multimodal omics integration, and single-cell analyses, while highlighting current challenges such as data noise and sparsity, difficulties in modeling long biological sequences, complexities in multimodal data integration, insufficient model interpretability, and ethical and privacy concerns. Finally, the paper outlines promising future research directions, emphasizing large-scale data mining, cross-domain model generalization, innovations in drug design and personalized medicine, and advocates for establishing an open and collaborative research ecosystem.

Advancements in high-throughput sequencing technologies and artificial intelligence (AI) offer unprecedented opportunities for groundbreaking discoveries in bioinformatics research. However, the challenges of exponential growth of omics data and the rapid development of AI technologies require automated big biological data analysis capability and interdisciplinary knowledge-driven scientific insight. Here, we propose a data-intelligence-intensive bioinformatics copilot (Bio-Copilot) system that synergizes AI capabilities with human researchers to facilitate hypothesis-free exploratory research and inspire novel scientific insights in large-scale omics studies. Bio-Copilot forms high-quality intensive intelligence through close collaboration between multiple agents, driven by large language models (LLMs), and human researchers. To augment the capabilities of Bio-Copilot, this study devises an agent group management strategy, an effective human-agent interaction mechanism, a shared interdisciplinary knowledge database, and continuous learning strategies for the agents. We comprehensively compare Bio-Copilot against GPT-4o and several leading AI agents across diverse bioinformatics tasks, using a broad range of evaluation metrics. Bio-Copilot achieves overall state-of-the-art performance across all tasks, while showcasing exceptional task completeness. Furthermore, on application to constructing a large-scale human lung cell atlas, Bio-Copilot not only reproduces the intricate data integration process detailed in a seminal study but also introduces a recursive, multilevel annotation strategy to capture the continuous nature of cellular states and uncovers the characteristics of rare cell types, highlighting its potential to unravel hidden complexities in biological systems. Beyond the technical achievements, this study also underscores the profound implications of integrating AI capabilities with expert knowledge in accelerating impactful biological discoveries and exploring uncharted territories.

Spatially resolved transcriptomics (SRT) technologies facilitate gene expression profiling with spatial resolution in a naïve state. Nevertheless, current SRT technologies exhibit limitations, manifesting as either low transcript detection sensitivity or restricted gene throughput. These constraints result in diminished precision and coverage in gene measurement. In response, we introduce SpaGDA, a sophisticated deep learning-based graph domain adaptation framework for both scenarios of gene expression imputation and cell type identification in spatially resolved transcriptomics data by impartially transferring knowledge from reference scRNA-seq data. Systematic benchmarking analyses across several SRT datasets generated from different technologies have demonstrated SpaGDA's superior effectiveness compared to state-of-the-art methods in both scenarios. Further applied to three SRT datasets of different biological contexts, SpaGDA not only better recovers the well-established knowledge sourced from public atlases and existing scientific literature but also yields a more informative spatial expression pattern of genes. Together, these results demonstrate that SpaGDA can be used to overcome the challenges of current SRT data and provide more accurate insights into biological processes or disease development. The SpaGDA is available in https://github.com/shenrb/SpaGDA.

MOTIVATION: PIWI-interacting RNAs (piRNAs) are a class of small non-coding RNAs that are highly abundant in the germline. One important role of piRNAs is to defend genome integrity by guiding PIWI proteins to silence transposable elements (TEs), which have a high potential to cause deleterious effects on their host. The mechanism of piRNA-mediated post-transcriptional silencing was also observed to affect mRNAs, suggesting that piRNAs might play a broad role in gene expression regulation. However, there has been no systematic report with regard to how many protein-coding genes might be targeted and regulated by piRNAs. RESULTS: We trained a support vector machine classifier based on a combination of Miwi CLIP-Seq-derived features and position-derived features to predict the potential targets of piRNAs on mRNAs in the mouse. Reanalysis of a published microarray dataset suggested that the expression level of the 2587 protein-coding genes predicted as piRNA targets showed significant upregulation as a whole after abolishing the slicer activity of Miwi, supporting the conclusion that they are subject to piRNA-mediated regulation. AVAILABILITY AND IMPLEMENTATION: A web version of the method called pirnaPre as well as our results for browse is available at http://www.regulatoryrna.org/software/piRNA/piRNA_target_mRNA/index.php CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.