数字农科院2.0

Advances and applications in sequencing-based pathogen surveillance

文献类型： 外文期刊

作者： Luo, Hao;Wang, Yao;Hou, Huiyu;Yang, Junbo;Liu, Yong-Xin

作者机构：

关键词： Pathogen detection;High-throughput sequencing;Artificial intelligence;Diagnostic methodologies

期刊名称： ABIOTECH

ISSN： 2096-6326

年卷期： 2025 年 7 卷 1 期

页码：

收录情况： EI（2025版） ; ; CSCD（2025-2026年度） ; ; 农林核心（2024版） ; ; ESCI（2025版）

摘要： The ongoing emergence of infectious diseases necessitates cutting-edge diagnostic methodologies. Traditional diagnostic methods are constrained by limited range, lengthy processing times, and inadequate sensitivity. Highthroughput sequencing technologies, particularly multiplex polymerase chain reaction (PCR)-based targeted sequencing, have emerged as transformative tools for pathogen detection, offering enhanced sensitivity, specificity, and cost efficiency. However, challenges in primer design, such as dimerization and bias, limit the effectiveness of these approaches. This review explores advances in sequencing technologies, emphasizing the roles of culturomics, metagenomics, and metatranscriptomics in pathogen discovery. We spotlight innovative strategies for error-tolerant primer design that address existing limitations by balancing coverage and specificity, thereby optimizing the multiplex PCR process. Furthermore, integration of artificial intelligence enhances the precision and scalability of sequencing, enabling real-time diagnostics. Collectively, these advances offer promising pathways to bolster global health, food security, and ecological resilience through robust and sustainable pathogen-detection systems.

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