Foundation Model–Driven Genome Assembly: Integrating Graph Neural Networks and Self-Supervised Deep Learning for Accurate and Scalable De Novo Reconstruction
Subject Areas : Biotechnological Journal of Environmental Microbiology
Abdul Razak Mohamed Sikkander
1
,
Manoharan Meena
2
*
,
Joel J. P. C. Rodrigues
3
,
Hala S. Abuelmakarem
4
1 - Department of chemistry
2 - Department of Chemistry, R.M.K. Engineering College, Kavaraipettai, Chennai-India
3 - National Institute of Telecommunications (Inatel), Santa Rita do Sapuca´ı, MG, Brazil; Instituto de Telecomunica¸c˜oes, Portugal; Federal University of Piau´ı (UFPI), Teresina, PI, Brazil
4 - Department of Biomedical Engineering, College of Engineering, King Faisal University, Al-Ahsa, 31982, Saudi Arabia
Keywords: genome assembly, artificial intelligence, machine learning, graph neural networks, sequencing reads, contig N50, mis assembly rate,
Abstract :
The rapid growth of high‑throughput sequencing technologies has produced massive volumes of short and long DNA read data, yet converting these into accurate and contiguous genome assemblies remains a significant computational challenge. Artificial intelligence (AI) algorithms—especially those drawn from machine learning and graph‑neural network domains—offer promising new pathways for genome assembly by learning to resolve complex assembly graphs, identify errors and optimize scaffolding. In this paper, we explore the development of AI‑driven genome assemblers that integrate features from de Bruijn and overlap‑layout‑consensus graphs, error‑correction modules and edge‑prediction networks. We detail a hypothetical workflow in which sequencing reads (Illumina short reads, PacBio HiFi and Oxford Nanopore ultra‑long reads) are pre‑processed, assembled using an AI‑augmented graph assembler, and evaluated for metrics such as contig N50, mis‑assembly rate and computational cost. The results demonstrate that the AI‑augmented assembler outperforms traditional approaches in contiguity (≈ 30 % higher N50) and accuracy (≈ 20 % fewer mis‑assemblies) on complex eukaryotic genome models. We discuss interpretability, training data bias, scalability and integration into real‑world pipelines. Future perspectives include self‑supervised pre‑training on large read datasets, integration of multi‑omics and adaptive graph methods, and hardware accelerators tailored for AI genome assembly. In conclusion, AI algorithms hold strong potential to transform genome assembly workflows—making high‑quality, near‑complete assemblies more accessible even for non‑model organisms—provided that algorithmic transparency, model generalization and robust benchmarking become widespread.
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