通过神经后部估计,从RNA病毒单元类型推断出突变率,选择和表观症
Itamar Caspi1, Moran Meir1, Nadav Ben Nun2,3
1Shmunis School of Biomedicine and Cancer Research, Faculty of Life Sciences, Tel Aviv University, P.O. Box 39040, Tel Aviv 6997801, Israel.
Virus evolution
|June 12, 2023
概括
这项研究引入了一种新的计算方法,可以从基因组数据中准确估计RNA病毒突变率和选择参数. 该方法成功分析了MS2菌的进化,揭示了对病毒遗传多样性的洞察力.
科学领域:
- 病毒学 病毒学
- 计算生物学 计算生物学
- 进化遗传学 进化遗传学
背景情况:
- 由于突变和自然选择,RNA病毒具有很高的遗传多样性.
- 从选择区分突变是具有挑战性的,影响突变率估计和适应性效应推断.
- 精确推断进化参数对于理解病毒进化至关重要.
研究的目的:
- 开发和验证一种计算方法,从病毒单元型序列中共同推断突变率和选择参数.
- 应用这种方法来研究MS2菌的进化.
主要方法:
- 采用神经后部估计,使用神经网络的基于模拟的推断技术.
- 在已知突变和选择参数的合成数据上测试了该方法,包括测序错误.
- 将该方法应用于MS2菌体序列传递实验中的全基因组单元型测序数据.
主要成果:
- 开发的方法准确而公正地从合成数据中推断出突变和选择参数.
- 估计MS2细菌突变率大约为每个复制周期每基因组0.2个突变.
- 在调节 lysis 蛋白表达的RNA干环中的四种有益突变中确定了相互的信号表达.
结论:
- 这种新的计算方法使得突变率和从哈普洛型数据中进行选择的强有力的联合推断成为可能,并考虑了测序错误.
- 这项研究为MS2菌提供了精确的突变率估计,并揭示了复杂的表皮性相互作用.
- 研究结果表明,溶解蛋白表达的微妙平衡影响病毒进化和健康.
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