使用Pfam注释预测辐射耐受性的模型TolRad,从参考基因组和MAG中识别出新的放射敏感细菌物种
Philip Sweet1, Matthew Burroughs1, Sungyeon Jang1
1McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, USA.
Microbiology spectrum
|September 5, 2024
概括
一个新的计算模型,TolRad,使用蛋白质域频率准确识别放射敏感细菌. 该工具有助于发现具有低电离辐射耐受性的新细菌物种,用于进一步的研究和生物标志物开发.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 细菌对电离辐射 (IR) 的耐受性有很大差异,从敏感物种到极端动物.
- 缺乏高通量方法来表征细菌的红外线敏感性,这阻碍了研究.
- 了解IR敏感性的多样性和遗传因素至关重要.
研究的目的:
- 开发一种计算模型,用于将细菌分类为放射敏感或耐辐射的细菌.
- 为了快速选细菌群落对红外线敏感物种.
- 为了促进机理学研究,并确定IR暴露的生物标志物.
主要方法:
- 训练了一种随机森林分类器 (TolRad),使用蛋白质域频率 (Pfam) 和实验确定的D10值.
- 在未经训练的细菌物种上验证了TolRad,并将其应用于人类微生物组蛋白质组和元基因组组装基因组 (MAGs).
- 实验证实了人类微生物组细菌预测的辐射敏感性.
主要成果:
- 在未经训练的物种中,TolRad获得了0.900的准确性,在蛋-NOG组装蛋白质上达到0.965.
- 在人类微生物组中确定了34种辐射敏感物种,扩展到蛋白质细菌之外.
- 使用MAG和建议的环境影响,在新的科目中发现了放射敏感物种.
结论:
- 托尔拉德是一个准确而通用的工具,用于分类细菌的红外线敏感性.
- 该模型扩大了已知的放射敏感细菌的多样性,包括在人类微生物群中.
- 这项工作为进一步研究细菌放射生物学和生物标志物发现提供了基础.
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