LncPNdeep: A long non-coding RNA classifier based on large language model with peptide and nucleotide embedding

A Sequence Feature-based Deep Learning Framework for Automatic Family Classification of the Small Non-Coding RNA

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.

Identifying RNA ac4C Modification Sites via Pseudo-Nucleotide Fingerprint Encoding and Multi-Scale Feature Integration.

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.

Signatures of micropeptides encoded by lncRNAs in cancer progression and metastasis.

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.

ESM2-Kcr: a novel model combining protein language model and deep learning methods for predicting lysine crotonylation sites

Scanning transcriptomes for nonlinear, domain-level similarities using hmSEEKR

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.

AWTI-Net Enables Accurate and Interpretable Functional Assessment of Disease-Associated LncRNA Mutations.