The harnessing of machine learning, especially deep generative models, has
opened up promising avenues in the field of synthetic DNA sequence generation.
Whilst Generative Adversarial Networks (GANs) have gained traction for this
application, they often face issues such as limited sample diversity and mode
collapse. On the other hand, Diffusion Models are a promising new class of
generative models that are not burdened with these problems, enabling them to
reach the state-of-the-art in domains such as image generation. In light of
this, we propose a novel latent diffusion model, DiscDiff, tailored for
discrete DNA sequence generation. By simply embedding discrete DNA sequences
into a continuous latent space using an autoencoder, we are able to leverage
the powerful generative abilities of continuous diffusion models for the
generation of discrete data. Additionally, we introduce Fr\'echet
Reconstruction Distance (FReD) as a new metric to measure the sample quality of
DNA sequence generations. Our DiscDiff model demonstrates an ability to
generate synthetic DNA sequences that align closely with real DNA in terms of
Motif Distribution, Latent Embedding Distribution (FReD), and Chromatin
Profiles. Additionally, we contribute a comprehensive cross-species dataset of
150K unique promoter-gene sequences from 15 species, enriching resources for
future generative modelling in genomics. We will make our code public upon
publication.

通过深度生成模型，特别是深度扩散模型，来合成 DNA 序列在合成生物学领域开辟了新的前景。我们提出了一种新颖的潜在扩散模型 (DiscDiff)，通过将离散 DNA 序列嵌入连续的潜在空间，使用自编码器来利用连续扩散模型强大的生成能力生成离散数据。此外，我们引入了一种新的度量标准 Frechet Reconstruction Distance (FReD)，用于衡量 DNA 序列生成结果的样本质量。我们的 DiscDiff 模型能够生成与真实 DNA 序列在 Motif 分布、潜在嵌入分布 (FReD) 和染色质剖面方面紧密相符的合成 DNA 序列。此外，我们还贡献了一个包含 15 个物种的 15 万个唯一启动子 - 基因序列的全面跨物种数据集，为未来基因组学中的生成建模工作提供丰富的资源。我们将在发表后公开我们的代码。