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

Abstract

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.