cs.CVApr 24, 2025

A Comprehensive Review on RNA Subcellular Localization Prediction

Authors: Cece Zhang, Xuehuan Zhu, Nick Peterson, Jieqiong Wang, Shibiao Wan

Organizations: Department of Cell & Systems Biology, University of Toronto, ON, Canada · School of Engineering, University of California, Los Angeles, CA, United States · Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, United States · Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, United States

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

Abstract

The subcellular localization of RNAs, including long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), microRNAs (miRNAs) and other smaller RNAs, plays a critical role in determining their biological functions. For instance, lncRNAs are predominantly associated with chromatin and act as regulators of gene transcription and chromatin structure, while mRNAs are distributed across the nucleus and cytoplasm, facilitating the transport of genetic information for protein synthesis. Understanding RNA localization sheds light on processes like gene expression regulation with spatial and temporal precision. However, traditional wet lab methods for determining RNA localization, such as in situ hybridization, are often time-consuming, resource-demanding, and costly. To overcome these challenges, computational methods leveraging artificial intelligence (AI) and machine learning (ML) have emerged as powerful alternatives, enabling large-scale prediction of RNA subcellular localization. This paper provides a comprehensive review of the latest advancements in AI-based approaches for RNA subcellular localization prediction, covering various RNA types and focusing on sequence-based, image-based, and hybrid methodologies that combine both data types. We highlight the potential of these methods to accelerate RNA research, uncover molecular pathways, and guide targeted disease treatments. Furthermore, we critically discuss the challenges in AI/ML approaches for RNA subcellular localization, such as data scarcity and lack of benchmarks, and opportunities to address them. This review aims to serve as a valuable resource for researchers seeking to develop innovative solutions in the field of RNA subcellular localization and beyond.

Explore similar work

CardsList
Mar 19, 2026

CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization

Subcellular localization is a crucial biological task for drug target identification and function annotation. Although it has been biologically realized that subcellular localization is closely associated with protein structure, no existing dataset offers comprehensive 3D structural information with detailed subcellular localization annotations, thus severely hindering the application of promising structure-based models on this task. To address this gap, we introduce a new benchmark called CAPSUL\mathbf{CAPSUL}, a C\mathbf{C}omprehensive humA\mathbf{A}n P\mathbf{P}rotein benchmark for SU\mathbf{SU}bcellular L\mathbf{L}ocalization. It features a dataset that integrates diverse 3D structural representations with fine-grained subcellular localization annotations carefully curated by domain experts. We evaluate this benchmark using a variety of state-of-the-art sequence-based and structure-based models, showcasing the importance of involving structural features in this task. Furthermore, we explore reweighting and single-label classification strategies to facilitate future investigation on structure-based methods for this task. Lastly, we showcase the powerful interpretability of structure-based methods through a case study on the Golgi apparatus, where we discover a decisive localization pattern αα-helix from attention mechanisms, demonstrating the potential for bridging the gap with intuitive biological interpretability and paving the way for data-driven discoveries in cell biology.

Yicheng Hu, Xinyu Lin, Shulin Li +3

University of Science and Technology of China · National University of Singapore · Tsinghua University

Protein Representation Learning
AskPDF
Feb 23, 2026

CrossLLM-Mamba: Multimodal State Space Fusion of LLMs for RNA Interaction Prediction

Accurate prediction of RNA-associated interactions is essential for understanding cellular regulation and advancing drug discovery. While Biological Large Language Models (BioLLMs) such as ESM-2 and RiNALMo provide powerful sequence representations, existing methods rely on static fusion strategies that fail to capture the dynamic, context-dependent nature of molecular binding. We introduce CrossLLM-Mamba, a novel framework that reformulates interaction prediction as a state-space alignment problem. By leveraging bidirectional Mamba encoders, our approach enables deep ``crosstalk'' between modality-specific embeddings through hidden state propagation, modeling interactions as dynamic sequence transitions rather than static feature overlaps. The framework maintains linear computational complexity, making it scalable to high-dimensional BioLLM embeddings. We further incorporate Gaussian noise injection and Focal Loss to enhance robustness against hard-negative samples. Comprehensive experiments across three interaction categories, RNA-protein, RNA-small molecule, and RNA-RNA demonstrate that CrossLLM-Mamba achieves state-of-the-art performance. On the RPI1460 benchmark, our model attains an MCC of 0.892, surpassing the previous best by 5.2%. For binding affinity prediction, we achieve Pearson correlations exceeding 0.95 on riboswitch and repeat RNA subtypes. These results establish state-space modeling as a powerful paradigm for multi-modal biological interaction prediction.

Rabeya Tus Sadia, Qiang Ye, Qiang Cheng

Department of Computer Science, University of Kentucky, Lexington, KY, USA · Department of Mathematics, University of Kentucky, Lexington, KY, USA · Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA

Cross-Modal Representation LearningState Space Models
AskPDF
Mar 20, 2026

Fair splits flip the leaderboard: CHANRG reveals limited generalization in RNA secondary-structure prediction

Accurate prediction of RNA secondary structure underpins transcriptome annotation, mechanistic analysis of non-coding RNAs, and RNA therapeutic design. Recent gains from deep learning and RNA foundation models are difficult to interpret because current benchmarks may overestimate generalization across RNA families. We present the Comprehensive Hierarchical Annotation of Non-coding RNA Groups (CHANRG), a benchmark of 170{,}083 structurally non-redundant RNAs curated from more than 10 million sequences in Rfam~15.0 using structure-aware deduplication, genome-aware split design and multiscale structural evaluation. Across 29 predictors, foundation-model methods achieved the highest held-out accuracy but lost most of that advantage out of distribution, whereas structured decoders and direct neural predictors remained markedly more robust. This gap persisted after controlling for sequence length and reflected both loss of structural coverage and incorrect higher-order wiring. Together, CHANRG and a padding-free, symmetry-aware evaluation stack provide a stricter and batch-invariant framework for developing RNA structure predictors with demonstrable out-of-distribution robustness.

Zhiyuan Chen, Zhenfeng Deng, Pan Deng +4

Department of Electrical and Computer Engineering, The University of Hong Kong, Pok Fu Lam, Hong Kong Island, Hong Kong SAR, China. · AI4Science Center, Shanghai Artificial Intelligence Laboratory, 129 Longwen Road, Xuhui, 200232, Shanghai, China. · Department of Pathology, Stanford Medicine, 265 Campus Drive, Stanford, 94305, Califorina, US. +7

OOD Generalization
AskPDF