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对ESKAPE细菌的泛基组分析:代表性不足可能会影响机器学习模型

Jeesu Lee¹, Branden Hunter², Hyunjin Shim^1,2

¹Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, Republic of Korea.

Frontiers in molecular biosciences

|July 8, 2024

概括

公共可用的菌体基因组,特别是那些感染ESKAPE病原体的基因组,显著不足. 这种遗传多样性的差距限制了对菌体作用的理解,并偏差了计算模型,可能阻碍了菌体治疗的发展.

科学领域:

微生物学和病毒学
生物信息学和计算生物学
基因组学和遗传多样性

背景情况:

菌体 (菌体) 是最丰富的生物实体,但它们的遗传多样性被低估了.
测序技术和医学相关性研究的局限性导致了这种代表性不足.
低估的菌体多样性在生态角色和偏差计算模型中创造了知识差距.

研究的目的:

在公共数据库中量化感染高优先级ESKAPE病原体的菌体基因组的不足.
分析这些ESKAPE菌体之间的遗传多样性和关系.
评估这种代表性不足对菌体治疗和计算建模的影响.

主要方法:

对公开可用的菌体基因组进行分析,以ESKAPE病原体为目标.
泛基因组分析以确定相关菌体之间共享的核心基因.
全基因组的比较和聚类,以评估核酸水平的相关性.
对独特的菌体和抗菌素蛋白质稀缺性的评估.

主要成果:

证实了ESKAPE菌体基因组和蛋白质在公共数据库中的显著不足.
泛基因组分析显示,感染同一ESKAPE宿主的菌体之间广泛共享核心基因.
基因组聚类表明在研究的ESKAPE菌体中,核酸水平的多样性有限.
观察到独特的菌体和蛋白质稀缺,它们对ESKAPE病原体具有抗菌活性.

结论:

感染ESKAPE病原体的菌体的遗传多样性在公共存储库中被严重低估.
这种缺乏多样性可能会阻碍菌体治疗的发展和应用.
由于代表性不足,偏差的生物数据集可能导致数据驱动的计算模型的结果不准确.

关键词:

逃离病原体的可能细菌菌体是一种细菌体.潘格诺姆 (pangenome) 是一个名字.蛋白质预测预测蛋白质预测不平衡的数据集.

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