首个可分析超长环状DNA的预训练模型，突破传统注意力机制限制。
eccDNAMamba: A Pre-Trained Model for Ultra-Long eccDNA Sequence Analysis
- 采用双向状态空间编码器，线性复杂度建模环状序列
- 支持长达200 Kbp的完整环状DNA分析，分类性能优异
- 适合癌症基因组学研究者用于eccDNA功能解析
染色体外环状DNA（eccDNA）在癌症中通过高拷贝扩增和远距离相互作用调控基因表达，发挥关键作用。尽管已有建模进展，但尚无预训练模型能支持全长度环状eccDNA的下游分析。现有基因组模型或局限于单核苷酸分辨率，或受二次注意力机制效率低下的制约。本文提出eccDNAMamba，首个专为环状DNA序列设计的双向状态空间编码器。该模型通过前向与反向传递实现全上下文表征学习，具有线性时间复杂度，并采用新型增强策略保留环状结构特性。在两个真实数据集上测试，eccDNAMamba展现出优异的分类性能，可扩展至200 Kbp的序列长度，为环状基因组建模提供了强大且高效的框架。代码已开源：https://github.com/zzq1zh/GenAI-Lab。
原文摘要 · Abstract (English)
Extrachromosomal circular DNA (eccDNA) plays key regulatory roles and contributes to oncogene overexpression in cancer through high-copy amplification and long-range interactions. Despite advances in modeling, no pre-trained models currently support full-length circular eccDNA for downstream analysis. Existing genomic models are either limited to single-nucleotide resolution or hindered by the inefficiency of the quadratic attention mechanism. Here, we introduce eccDNAMamba, the first bidirectional state-space encoder tailored for circular DNA sequences. It combines forward and reverse passes for full-context representation learning with linear-time complexity, and preserves circular structure through a novel augmentation strategy. Tested on two real-world datasets, eccDNAMamba achieves strong classification performance and scales to sequences up to 200 Kbp, offering a robust and efficient framework for modeling circular genomes. Our codes are available at https://github.com/zzq1zh/GenAI-Lab.
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