Multi-task adversarial autoencoder for functional genomic element generation with preserved biophysical properties

Abstract

Generative modeling of genomic sequences presents a stringent test for deep learning, requiring the capture of long-range dependencies and functional constraints beyond local nucleotide statistics. Existing architectures frequently collapse to limited modes or reproduce shallow nucleotide distributions without encoding functional semantics. We introduce the Multi-Task Adversarial Autoencoder (MT-AAE), a hybrid generative framework that integrates adversarial regularization with auxiliary functional and biophysical objectives to enforce structured latent representations. Evaluated on an empirical human gene corpus, MT-AAE achieved a Train-on-Synthetic-Test-on-Real (TRTS) accuracy of 74.7%, compared with 41.0% for a standard GAN baseline. Stratified analysis further showed that functional discriminability increased to 89.3% when sequence lengths aligned with the model’s architectural window. Importantly, the learned representations exhibited emergent biological structure: synthetic sequences spontaneously preserved cis -regulatory syntax, including canonical TATA-box motifs recovered across 100% of generated promoter sequences without explicit rule encoding, though positional placement relative to the TSS was not statistically significant (KS $$p=0.90$$ ), and the high occurrence rate is partly attributable to the AT-rich composition of the generated sequences. Representation-level validation using frozen DNABERT-2 and DNABERT-S embeddings confirmed that the generated sequences retained functional information beyond shallow k-mer statistics. Cross-species evaluation on Mus musculus sequences further demonstrated species-specific learning consistent with known human–mouse regulatory divergence. The framework also mitigated mode collapse, maintaining near-uniform generation across functional classes ( $$R_g \approx 1.0$$ ), including rare categories such as tRNAs ( $$ < 2\%$$ of the dataset). These findings position MT-AAE as an effective framework for biologically constrained genomic sequence generation.