突发性,可重复性和可预测性在一个 prokaryotic pangenome 的进化过程中
Alan J S Beavan1, Maria Rosa Domingo-Sananes1,2, James O McInerney1
1School of Life Sciences, The University of Nottingham, Nottingham NG7 2UH, United Kingdom.
概括
机器学习预测了Escherichia coli pangenomes中的基因存在. 基因共发生模式揭示了决定性选择,这表明基因内基因适应性驱动 prokaryotic 进化.
科学领域:
- 微生物基因组学 微生物基因组学
- 进化生物学是进化的生物学.
- 生物信息学是一种生物信息学.
背景情况:
- Prokaryotic pangenomes 由于横向基因转移和基因丢失而表现出高度的变异性.
- 重复的基因获取引发了关于平行进化轨迹与宿主遗传背景影响的疑问.
研究的目的:
- 开发一种机器学习方法,用于预测大肠杆菌基因组中的基因存在/不存在.
- 为了调查是否选择保持决定性基因-基因共发生和避免关系.
主要方法:
- 利用机器学习方法分析基因存在/缺席模式.
- 在大肠杆菌泛体中利用重复的基因转移事件来观察进化模式.
主要成果:
- 基因的存在/缺失是高度可预测的,基于其他辅助基因.
- 选择决定性地保持了基因与基因之间的关系,强大地承载了进化历史.
结论:
- 庞基因组的很大一部分可以被视为相互作用的基因组,类似于生态社区.
- 基因内基因适应效应可能是 prokaryotic 进化和基因关系出现的关键驱动因素.
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