This paper introduces a novel framework for DNA sequence generation,
comprising two key components: DiscDiff, a Latent Diffusion Model (LDM)
tailored for generating discrete DNA sequences, and Absorb-Escape, a
post-training algorithm designed to refine these sequences. Absorb-Escape
enhances the realism of the generated sequences by correcting `round errors'
inherent in the conversion process between latent and input spaces. Our
approach not only sets new standards in DNA sequence generation but also
demonstrates superior performance over existing diffusion models, in generating
both short and long DNA sequences. Additionally, we introduce EPD-GenDNA, the
first comprehensive, multi-species dataset for DNA generation, encompassing
160,000 unique sequences from 15 species. We hope this study will advance the
generative modelling of DNA, with potential implications for gene therapy and
protein production.

该论文引入了一种用于 DNA 序列生成的新型框架，包括 DiscDiff，一种专门用于生成离散 DNA 序列的潜在扩散模型（LDM），和 Absorb-Escape，一种后期训练算法，用于优化这些序列。Absorb-Escape 通过纠正潜在和输入空间之间转换过程中固有的 ' 舍入误差 ' 来增强生成序列的真实性。我们的方法不仅在 DNA 序列生成方面树立了新的标准，而且在生成短 DNA 序列和长 DNA 序列方面表现出了优越性能。此外，我们还推出了 EPD-GenDNA，这是第一个综合性的多物种 DNA 生成数据集，包含来自 15 种物种的 160,000 个独特序列。我们希望这项研究能推动 DNA 的生成建模，对基因治疗和蛋白质生产可能产生影响。