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在精准医学中增加致病菌变异诊断率:当前的最佳实践和未来的机会

Sonam Dukda¹, Manoharan Kumar¹, Andrew Calcino¹

¹Centre for Tropical Bioinformatics and Molecular Biology, College Science and Engineering, James Cook University, Cairns, QLD, Australia.

|August 23, 2025

概括

精确诊断致病变体对于精确医学至关重要. 这篇评论探讨了改善遗传疾病遗传诊断率的新技术和计算方法.

科学领域:

基因组学
生物信息学
计算生物学

背景情况:

精确诊断致病变体对于精确医学至关重要,但目前遗传性疾病的诊断率仍然不足.
使用已确定的指导方针来解释遗传变异面临着纳入新数据类型和方法的挑战.
测序和计算方法的进步提供了潜力,但需要仔细整合和验证.

研究的目的:

审查目前用于提高精准医学基因诊断率的技术和计算策略.
探索将新型数据类型和先进的方法纳入遗传变体的解释.
概述下一代多组精准医学框架的设计原则.

主要方法:

审查当前的测序技术,包括单细胞和长读数测序.
分析变量注释和优先级的软件方法.
在基因诊断中探索基于血统的策略和机器学习应用.
一个强大的,模块化的,安全的,灵活的,可扩展的多个OMC框架的设计原则.

主要成果:

新的测序技术和计算方法有望提高基因诊断率.
整合多种经济数据和人工智能带来了机遇和挑战.
已建立的指导方针需要适应新兴数据类型和分析方法.
确定了下一代精准医学框架的关键设计原则.

结论:

测序和计算分析的进步对于增加遗传诊断率至关重要.
为了充分发挥精准医学的潜力,开发综合的多学科框架至关重要.
人工智能应用的临床实用性需要仔细考虑和比较.

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