Abdelwahab Omar, Torkamaneh Davoud
Département de Phytologie, Université Laval, Québec City, QC, Canada.
Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada.
Front Bioinform. 2025 Apr 23;5:1574359. doi: 10.3389/fbinf.2025.1574359. eCollection 2025.
Artificial intelligence (AI) has revolutionized numerous fields, including genomics, where it has significantly impacted variant calling, a crucial process in genomic analysis. Variant calling involves the detection of genetic variants such as single nucleotide polymorphisms (SNPs), insertions/deletions (InDels), and structural variants from high-throughput sequencing data. Traditionally, statistical approaches have dominated this task, but the advent of AI led to the development of sophisticated tools that promise higher accuracy, efficiency, and scalability. This review explores the state-of-the-art AI-based variant calling tools, including DeepVariant, DNAscope, DeepTrio, Clair, Clairvoyante, Medaka, and HELLO. We discuss their underlying methodologies, strengths, limitations, and performance metrics across different sequencing technologies, alongside their computational requirements, focusing primarily on SNP and InDel detection. By comparing these AI-driven techniques with conventional methods, we highlight the transformative advancements AI has introduced and its potential to further enhance genomic research.
人工智能（AI）已经彻底改变了众多领域，包括基因组学，在该领域它对变异检测产生了重大影响，变异检测是基因组分析中的一个关键过程。变异检测涉及从高通量测序数据中检测遗传变异，如单核苷酸多态性（SNP）、插入/缺失（InDel）和结构变异。传统上，统计方法主导了这项任务，但人工智能的出现促使了先进工具的开发，这些工具有望实现更高的准确性、效率和可扩展性。本综述探讨了基于人工智能的最新变异检测工具，包括DeepVariant、DNAscope、DeepTrio、Clair、Clairvoyante、Medaka和HELLO。我们讨论了它们的基本方法、优势、局限性以及在不同测序技术中的性能指标，以及它们的计算要求，主要关注SNP和InDel检测。通过将这些人工智能驱动的技术与传统方法进行比较，我们突出了人工智能带来的变革性进展及其进一步提升基因组研究的潜力。
