VAERNAGen: A variational autoencoder-based framework for de novo generation of RNA family sequences.

Abstract

RNA plays a pivotal role in diverse cellular processes, and the rational design of functional RNA sequences is central to advancing RNA engineering. While deep generative models have shown significant promise, their generated sequences frequently lack the structural accuracy and evolutionary fidelity required for biological functionality. To address this challenge, we introduced VAERNAGen, a novel variational autoencoder-based framework for de novo generation of RNA family sequences. VAERNAGen's core innovation is a joint representation that encode aligned nucleotide sequences and their secondary structures into an 11-channel L × L two-dimensional matrix. This image-like format enables 2D convolutional neural networks to effectively learn the spatial interplay between sequence conservation and structural variation. Evaluated on two canonical Rfam families-RF00001 (5S ribosomal RNA) and RF00005 (tRNA), VAERNAGen outperformed the current state-of-the-art grammar-based method, achieving significantly higher median bit scores (105.25 vs. 89.34 for RF00001; 58.24 vs. 50.02 for RF00005). Generated sequences also exhibited greater nucleotide-level similarity to natural seed alignments and lower variance, reflecting enhanced biological plausibility and reproducibility. Together, these results establish VAERNAGen as a state-of-the-art method for de novo generation of RNA family sequences.