快速ST:一个有效的工具来推断微生物群落的分解和方向性
Joung Min Choi1, Xiaowei Wu2, Liqing Zhang1
1Department of Computer Science, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, United States of America.
PeerJ
|November 3, 2025
概括
一个新的计算框架,FastST,准确地识别微生物来源及其传播方向性. 与现有方法相比,该工具在微生物组研究和环境监测方面提供了更高的准确性和效率.
科学领域:
- 微生物生态学 微生物生态学
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
背景情况:
- 微生物组对人类健康和生态系统至关重要.
- 微生物源追踪 (MST) 方法如SourceTracker2和FEAST有助于识别微生物来源,但面临计算限制,无法推断传播方向性.
研究的目的:
- 介绍FastST,一个用于微生物源追踪的新型计算框架.
- 为了能够推断微生物群落的源贡献和方向性.
主要方法:
- 开发了FastST,这是一个用于微生物源追踪的新计算框架.
- 评估了FastST与FEAST和SourceTracker2的性能,使用不同复杂度的广泛模拟研究.
主要成果:
- 与现有的MST方法相比,FastST表现出卓越的准确性和计算效率.
- 随着越来越多的源环境,FastST保持了一致的性能.
- 在推断方向性方面取得了超过90%的准确性,即使使用多个主要来源.
结论:
- 在微生物源追踪方面,FastST提供了显著的进步,为源贡献和方向性提供了准确和高效的推断.
- 该框架扩大了微生物组研究,宿主病原体研究和环境监测中的应用.
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