AGNES: Adaptive Graph Neural Network and Dynamic Programming Hybrid Framework for Real-Time Nanopore Seed Chaining

TL;DR

This work introduces AGNES, a real-time adaptive seed chaining framework for nanopore sequencing that unites a graph neural network with dynamic programming to overcome fixed-gap penalties in context-rich genomic regions. Seeds are modeled as nodes with 12-dimensional features and edges as 8-dimensional spatial relations, forming a graph on which a three-layer EdgeConv GNN classifies seeds and guides chaining, with a confidence-based selector (threshold $ au=0.7$) deciding when to rely on learned guidance versus pure DP. Empirically, AGNES achieves state-of-the-art per-seed classification ($>99.9\%$ F1 for XGBoost, $99.90\%$ for GNN) and substantial chain-level gains ($57.21\%$ F1 for Hybrid) while maintaining a median latency of $1.59$ ms, thereby satisfying real-time requirements. The framework demonstrates robust performance under simulated noise (100% success at 20% corruption) and provides detailed ablation and domain analyses, supporting deployment in production genomics pipelines and offering a blueprint for integrating graph learning with classical algorithms in latency-critical bioinformatics tasks.

Abstract

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.

Figures (12)

• Figure 1: Visual validation of seed classification across three representative reads with varying complexity levels. Each panel shows seed match graphs where nodes represent seeds (green: correct, red: false), edges encode spatial relationships, and position corresponds to read (x-axis) and genome (y-axis) coordinates. Statistics boxes report precision, recall, and F1 scores for each method. (a) Simple read with 15 seeds achieving 100% accuracy across all methods. (b) Moderate complexity read with 26 seeds where GNN outperforms baselines in repeat-rich regions. (c) Complex read with 42 seeds demonstrating GNN's ability to leverage graph structure for disambiguation. Yellow shading indicates homopolymer regions, blue shading indicates GC-extreme regions where errors concentrate.
• Figure 2: Comprehensive multi-faceted performance comparison across classification, chaining, runtime, and interpretability dimensions. (a) Classification metrics showing XGBoost and GNN achieving >99.9% F1 scores with Pure DP baseline estimated from prior studies. (b) Chaining metrics revealing classification-chaining gap where 99.9% per-seed accuracy translates to only 57.2% chain F1, with Hybrid achieving 99.94% precision and 40.07% recall. (c) Runtime performance demonstrating sub-2ms latency across all methods with Hybrid at 1.59ms meeting real-time requirements. (d) XGBoost feature importance analysis identifying genome position, hash quality, and signal quality as top predictors, providing interpretability into learned decision boundaries.
• Figure 3: Performance versus computational cost trade-off analysis. (a) Chaining F1 score versus inference time per read showing all methods achieve sub-2ms latency (vertical dashed line) with Pareto frontier connecting dominant methods. Annotations highlight XGBoost achieving best classification F1 (99.99%) and Hybrid achieving best balance (99.94% precision, 40.07% recall, 1.59ms latency). (b) Chaining F1 score versus memory consumption with efficiency zones (green: low 0-100MB, yellow: medium 100-200MB, red: high 200-400MB) showing diminishing returns where Hybrid at 190MB represents optimal efficiency point before Ensemble's 350MB requirement yields marginal gains.
• Figure 4: Inference time distribution across methods showing median, quartiles, and full distribution via violin plots with embedded box plots. All methods except Ensemble achieve sub-2ms latency (horizontal dashed line) required for real-time nanopore sequencing. Pure DP exhibits tightest distribution (0.72ms median, IQR [0.68,0.78]ms), while Hybrid shows widest spread (1.59ms median, IQR [1.48,1.73]ms) due to adaptive selection overhead. Statistical annotations indicate significance levels from Kruskal-Wallis test with post-hoc Dunn comparisons.
• Figure 5: Robustness analysis under controlled noise injection from 0-20% label corruption. (a) Success rate (percentage of reads achieving F1>0.5) versus noise level showing Hybrid maintains 100% across all levels while Pure DP degrades to 30.3% at 20% noise. XGBoost, GNN, and Ensemble show intermediate degradation. (b) Relative improvement bars at 20% noise showing Hybrid achieves 230% improvement over Pure DP baseline. Statistical annotations indicate significance from chi-square tests (***p<0.001, **p<0.01, *p<0.05). Chi-square test across methods yields $\chi^2=42.1$, p<0.001.