用图神经网络动态优化纳米孔测序的种子链匹配，提升准确性与实时性。
AGNES: Adaptive Graph Neural Network and Dynamic Programming Hybrid Framework for Real-Time Nanopore Seed Chaining
- 融合图神经网络与动态规划，根据基因组上下文自适应调整匹配策略。
- 在1000条合成读段上实现99.94%精确率、40.07%召回率，显著优于基线。
- 适合需要高精度实时处理的基因组学分析场景，尤其对抗噪声能力强。
纳米孔测序可实现实时长读段测序，读段长度超过10千碱基，但其固有错误率高达12%-15%，给序列比对带来巨大计算挑战。关键的种子链匹配步骤需在读段与参考基因组间连接精确的k-mer匹配并过滤虚假匹配，但现有方法依赖固定间隙惩罚函数，无法适应串联重复和结构变异等复杂基因组环境。本文提出RawHash3，一种结合图神经网络与经典动态规划的混合框架，实现自适应种子链匹配，在保持实时性能的同时提供统计保障。我们将种子链匹配形式化为图学习任务：种子作为节点，携带12维特征向量；边编码8维空间关系，包括间隙一致性。架构采用三层EdgeConv GNN，结合置信度决策机制，动态切换学习引导与算法回退。在1,000条合成纳米孔读段、5,200个测试种子上的综合评估表明，RawHash3达到99.94%精确率和40.07%召回率，相较基线有25.0%相对提升（p < 0.001）。系统保持中位推理延迟1.59毫秒，满足实时要求，且在20%标签污染下仍达100%成功率，而基线降至30.3%。交叉验证证实稳定性，确立图神经网络在生产级基因组流程中的可行性。
原文摘要 · Abstract (English)
Nanopore sequencing enables real-time long-read DNA sequencing with reads exceeding 10 kilobases, but inherent error rates of 12-15 percent present significant computational challenges for read alignment. The critical seed chaining step must connect exact k-mer matches between reads and reference genomes while filtering spurious matches, yet state-of-the-art methods rely on fixed gap penalty functions unable to adapt to varying genomic contexts including tandem repeats and structural variants. This paper presents RawHash3, a hybrid framework combining graph neural networks with classical dynamic programming for adaptive seed chaining that maintains real-time performance while providing statistical guarantees. We formalize seed chaining as graph learning where seeds constitute nodes with 12-dimensional feature vectors and edges encode 8-dimensional spatial relationships including gap consistency. Our architecture employs three-layer EdgeConv GNN with confidence-based method selection that dynamically switches between learned guidance and algorithmic fallback. Comprehensive evaluation on 1,000 synthetic nanopore reads with 5,200 test seeds demonstrates RawHash3 achieves 99.94 percent precision and 40.07 percent recall, representing statistically significant 25.0 percent relative improvement over baseline with p less than 0.001. The system maintains median inference latency of 1.59ms meeting real-time constraints, while demonstrating superior robustness with 100 percent success rate under 20 percent label corruption versus baseline degradation to 30.3 percent. Cross-validation confirms stability establishing graph neural networks as viable approach for production genomics pipelines.
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