用语言模型生成可调控的DNA序列，提升设计精准度。
Language Models for Controllable DNA Sequence Design
- 基于Transformer架构，融合多源生物信号实现可控生成。
- 在启动子/增强子设计中生成序列流畅且功能相关性高。
- 适合基因工程与合成生物学研究者使用。
我们研究可调控的DNA序列设计，即根据特定生物特性生成序列。尽管语言模型（如GPT、BERT）在自然语言生成中表现卓越，但在DNA序列生成中的应用仍不充分。本文提出ATGC-Gen，一种用于可控生成的自动化Transformer生成器，通过跨模态编码整合多样生物信号。ATGC-Gen采用仅解码器和仅编码器两种Transformer结构，支持自回归或掩码恢复目标下的灵活训练与生成。我们在启动子与增强子序列设计等代表性任务上进行评估，并基于ChIP-Seq实验引入新数据集以建模蛋白质结合特异性。实验表明，ATGC-Gen能生成流畅、多样且具有生物学意义的序列，与目标属性高度一致。相比现有方法，在可控性与功能相关性上均有显著提升，凸显语言模型在可编程基因组设计中的潜力。源代码已公开于https://github.com/divelab/AIRS/blob/main/OpenBio/ATGC_Gen。
原文摘要 · Abstract (English)
We consider controllable DNA sequence design, where sequences are generated by conditioning on specific biological properties. While language models (LMs) such as GPT and BERT have achieved remarkable success in natural language generation, their application to DNA sequence generation remains largely underexplored. In this work, we introduce ATGC-Gen, an Automated Transformer Generator for Controllable Generation, which leverages cross-modal encoding to integrate diverse biological signals. ATGC-Gen is instantiated with both decoder-only and encoder-only transformer architectures, allowing flexible training and generation under either autoregressive or masked recovery objectives. We evaluate ATGC-Gen on representative tasks including promoter and enhancer sequence design, and further introduce a new dataset based on ChIP-Seq experiments for modeling protein binding specificity. Our experiments demonstrate that ATGC-Gen can generate fluent, diverse, and biologically relevant sequences aligned with the desired properties. Compared to prior methods, our model achieves notable improvements in controllability and functional relevance, highlighting the potential of language models in advancing programmable genomic design. The source code is released at (https://github.com/divelab/AIRS/blob/main/OpenBio/ATGC_Gen).
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