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

The AGNES framework integrates adaptive graph neural networks with dynamic programming to improve real-time nanopore DNA sequencing. This hybrid approach specifically addresses challenges such as high error rates inherent in nanopore sequencing and enhances the alignment of sequencing reads. By effectively connecting k-mer matches between reads and reference genomes, AGNES facilitates more accurate and efficient seed chaining during the sequencing process. The framework’s design aims to overcome limitations in existing methods, offering a promising solution for advancing nanopore sequencing technology. Recent connected studies reinforce the significance of tackling these alignment and error-related issues, highlighting AGNES’s potential impact. Overall, AGNES represents a notable development in applying machine learning techniques to genomic data analysis, with implications for improving sequencing accuracy and speed.