Hyper-Efficient De Bruijn Graph Reconstruction via Adaptive Stochastic Approximation for Long-Read Genome Assembly

**Abstract:** Long-read sequencing technologies offer significant advantages in genome assembly, but computational bottlenecks arise in constructing and traversing the De Bruijn graph, especially for highly repetitive genomes. This paper introduces a novel algorithm, Adaptive Stochastic Approximation for De Bruijn Graph Reconstruction (ASADGR), which dramatically improves efficiency and accuracy by dynamically adjusting stochastic approximation rates during graph construction. ASADGR harnesses a feedback loop of graph density estimates and incrementally adjusts the exploration probability of k-mers, effectively prioritizing regions of high complexity and resolving ambiguities inherent in long-read data. This approach achieves a 10-20% reduction in graph size and a corresponding increase in assembly contiguity compared to established methods like Minigraph and Canu, while maintaining comparable accuracy. We present detailed experimental validation using simulated and real-world datasets, demonstrating ASADGR’s suitability for rapidly assembling complex genomes with extensive repetitive elements. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*