High-Fidelity Transcriptome Reconstruction of Degraded RNA-Seq Samples Using Denoising Diffusion Models.

BACKGROUND: RNA degradation in clinically archived samples introduces systematic biases into RNA-seq data, limiting the accuracy of downstream analyses. Developing computational methods for high-fidelity transcriptome restoration is therefore of critical importance. METHODS: We introduce DiffRepairer, a deep learning model that combines a Transformer architecture with a conditional diffusion model framework to reverse the effects of RNA degradation. The model is trained on "degraded-original" paired data, generated via a comprehensive simulation pipeline, to learn a direct, one-step repair mapping. RESULTS: Across five diverse pseudo-degraded datasets, DiffRepairer demonstrated stable and superior performance, outperforming traditional statistical methods (e.g., CQN) and standard deep learning models (e.g., VAE) on key technical and biological metrics. CONCLUSION: DiffRepairer is a validated, high-precision tool for transcriptome repair that effectively restores biologically meaningful signals from degraded RNA-seq data, highlighting the potential of advanced generative models in bioinformatics.