Oct 2026· Non-coding RNA Research· Vol 20, pp.
104-112
· 0 citations· 44 references
Medicine
Abstract
Accurate classification of long non-coding RNAs (lncRNAs) is essential for transcriptome annotation and understanding gene regulation. Existing computational methods predominantly rely on nucleotide sequence features, frequently overlooking biologically relevant peptide signals encoded within lncRNAs. To overcome this limitation, we developed LncPNdeep, an integrative deep learning framework that combines nucleotide and peptide embeddings extracted via masked language models, specifically utilizing contextual representations from BigBird, Longformer, and ProtTrans. By fusing both features in a concatenated neural architecture, LncPNdeep robustly captures complex sequence relationships and improves discrimination between lncRNAs and coding RNAs. Benchmarking on the human transcriptome achieved state-of-the-art performance with 97.1% accuracy, surpassing established lncRNA classification tools and baseline machine learning models. LncPNdeep also demonstrated superior generalization ability across cross-species datasets, maintaining consistently high accuracy and F1 scores. Permutation analysis highlighted the pivotal role of peptide embeddings, especially Average Peptide Embedding, in model performance, while t-SNE visualizations confirmed that integrating multiple embeddings markedly enhances the separation of lncRNAs from coding RNAs. These results position LncPNdeep as a versatile and powerful tool for transcriptomic research, facilitating lncRNA discovery, biomarker identification, and comparative genomics. The model and instructions are freely available at https://github.com/yatoka233/LncPNdeep.
A concatenated deep neural network that incorporates both peptide and nucleotide embedding from masked language modeling (MLM), being able to discover complex associations between sequence information and lncRNA classification.
Small non-coding RNAs (ncRNAs) are functionally diverse RNA molecules involved in various regulatory and cellular processes. Accurate family classification of ncRNAs is important for large-scale functional annotation. However, many existing computational approaches rely heavily on predicted secondary structural information, which may not always be available. In this work, we propose a purely sequence-based deep learning framework for small ncRNA family classification without using explicit secondary structure features. Our approach combines one-hot encoded sequences, sequence-level motifs, normalized length, GC content, and biologically motivated handcrafted descriptors within a DenseNet-inspired convolutional neural network architecture. The proposed model was evaluated on the benchmark nRC dataset containing 13 ncRNA families using 10fold cross-validation and an independent test set. Our method achieved an MCC of 0.951 and an F1-score of 0.954, outperforming existing sequence-based as well as several structure-aware methods. The model also demonstrated improved performance for challenging classes such as miRNA, CD-box, HACA-box, and riboswitches. In addition, we evaluated the generalization capability of our framework on the recently introduced NCY dataset, where it maintained strong and consistent classification performance. These results show that enriched sequence-derived representations can effectively capture discriminative functional signals for ncRNA family classification without relying on secondary structure prediction. The source code is available at https://github.com/sharma-nandita/small_RNA_classification.
Nandita Sharma, Pralay Mitra· Annual International Compute...· 0 citations
DFM-ac4C, a novel computational framework designed for the accurate prediction of ac4C modification sites, significantly outperforms existing models, achieving outstanding predictive metrics, and underscores DFM-ac4C's effectiveness as a robust and efficient tool for RNA ac4C site identification.
Yiming Wang, Fan Mo, Yun Sha et al.· Interdisciplinary Sciences C...· 0 citations
Tr-lncRNA-derived MPs represent a previously underexplored class of potentially functional molecules associated with cancer clinical annotation and may serve as biomarkers for disease progression.
Stav Zok, M. Linial· British Journal of Cancer· 1 citation
Long noncoding RNAs (lncRNAs) play roles in gene regulation across kingdoms of life. However, lncRNAs with related functions often lack linear sequence similarity, making it difficult to leverage studies of one lncRNA to inform the understanding of others. We describe a k-mer-based hidden Markov model, hmSEEKR, that enables the scanning of transcriptomes for regions of non-linear sequence similarity to a query domain, without prior knowledge of where within the transcriptome the similarities may be located. When individual lncRNA domains were used as search features, hmSEEKR successfully identified regions in other RNAs that harbor non-linear sequence similarity and bind similar sets of proteins. Applying hmSEEKR to transcriptome-wide searches, we found that certain domains within the lncRNAs XIST, NEAT1, and MALAT1 exhibited widespread regional similarity to both lncRNA and protein-coding genes, while others were more unique, exhibiting similarity to ∼100 genes or fewer. Combinatorial searches uncovered RNAs containing sequential matches to core functional domains of XIST and NEAT1, and eCLIP-inferred protein-interaction networks within these RNAs more closely resembled those of XIST and NEAT1, respectively, than would be expected by chance, suggesting the searches recovered RNAs with similar biological properties. Finally, within annotated sets of cis-activating and cis-repressive lncRNAs, we observed opposing enrichments for similarity to domains associated with transcription-promoting complexes and heterogeneous nuclear ribonucleoprotein (hnRNP) binding, respectively, suggesting the enriched sequences may contribute to regulatory functions. hmSEEKR can be applied with minimal training data and enables the a priori discovery of RNA domains that share nonlinear similarity, offering a sequence-informed approach to discover functional elements within noncoding transcriptomes.
Shuang Li, Daniel Sprague, Quinn E Eberhard et al.· bioRxiv· 0 citations
PLM-ArgMe is presented that is based on a symmetry-sensitive Transformer framework using context-aware ESM-2 residue embeddings, which is mapped through a novel Bio-Symmetric Mirrored Sinusoidal Encoding strategy to address the biological symmetry hypothesis of arginine methylation.
Nitika Bhatt, Kartik Joshi, R. Rout et al.· Biochemical and Biophysical...· 0 citations