ArticleBriefings in bioinformatics2023
RNAlight: a machine learning model to identify nucleotide features determining RNA subcellular localization.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- APEX-seq maps transcriptome-wide subcellular RNA localization in living cells.Nature protocols · 2026Review
- A 2025 meeting report from the Venice lagoon: International graduate programRNA (New York, N.Y.) · 2026Article
- Investigation of myofiber composition changes and molecular mechanisms in rabbit meat quality development during growth.BMC genomics · 2026Article
- A comprehensive review on RNA subcellular localization prediction.Briefings in functional genomics · 2026Review
- CIRCpedia v3: an interactive database for circular RNA characterization and functional exploration.Nucleic acids research · 2026Article
- High-throughput Screening of Sequence Elements Associated with RNA Localization.Computational and structural biotechnology journal · 2026Article
- From nucleotides to numbers: a comprehensive review of RNA feature extraction methods for computational modelling.Briefings in bioinformatics · 2025Review
- LncRNA Subcellular Localization Across Diverse Cell Lines: An Exploration Using Deep Learning with InexactNon-coding RNA · 2025Article
- RNALoc-LM: RNA subcellular localization prediction using pre-trained RNA language model.Bioinformatics (Oxford, England) · 2025Article
- RNALoc-LM: RNA subcellular localization prediction using pre-trained RNA language model.Bioinformatics (Oxford, England) · 2025Article
- lncRNA localization and feature interpretability analysis.Molecular therapy. Nucleic acids · 2025Article
- An ensemble deep learning framework for multi-class LncRNA subcellular localization with innovative encoding strategy.BMC biology · 2025Article
- Omic AI reveals new autophagy regulators from the Atg1 interactome inFrontiers in cell and developmental biology · 2025Article
- The improved de Bruijn graph for multitask learning: predicting functions, subcellular localization, and interactions of noncoding RNAs.Briefings in bioinformatics · 2024Article
- Nmix: a hybrid deep learning model for precise prediction of 2'-O-methylation sites based on multi-feature fusion and ensemble learning.Briefings in bioinformatics · 2024Article
- MSlocPRED: deep transfer learning-based identification of multi-label mRNA subcellular localization.Briefings in bioinformatics · 2024Article
- Evaluation of machine learning models that predict lncRNA subcellular localization.NAR genomics and bioinformatics · 2024Article
- Challenges in LncRNA Biology: Views and Opinions.Non-coding RNA · 2024Article
- GPS-SUMO 2.0: an updated online service for the prediction of SUMOylation sites and SUMO-interacting motifs.Nucleic acids research · 2024Article
- mRCat: A Novel CatBoost Predictor for the Binary Classification of mRNA Subcellular Localization by Fusing Large Language Model Representation and Sequence Features.Biomolecules · 2024Article
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4 authors.
Funding
Abstract
Different RNAs have distinct subcellular localizations. However, nucleotide features that determine these distinct distributions of lncRNAs and mRNAs have yet to be fully addressed. Here, we develop RNAlight, a machine learning model based on LightGBM, to identify nucleotide k-mers contributing to the subcellular localizations of mRNAs and lncRNAs. With the Tree SHAP algorithm, RNAlight extracts nucleotide features for cytoplasmic or nuclear localization of RNAs, indicating the sequence basis for distinct RNA subcellular localizations. By assembling k-mers to sequence features and subsequently mapping to known RBP-associated motifs, different types of sequence features and their associated RBPs were additionally uncovered for lncRNAs and mRNAs with distinct subcellular localizations. Finally, we extended RNAlight to precisely predict the subcellular localizations of other types of RNAs, including snRNAs, snoRNAs and different circular RNA transcripts, suggesting the generality of using RNAlight for RNA subcellular localization prediction.
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