通过隐藏的马尔科夫模型揭示SARS-CoV-2进化中的表观相互作用
Ayotomiwa Ezekiel Adeniyi1, Akshay Juyal1, Pavel Skums2
1Department of Computer Science, Georgia State University, Atlanta, Georgia, USA.
概括
这项研究引入了一种新的隐藏马尔科夫模型 (HMM),用于跟踪SARS-CoV-2演变中的动态表观相互作用,改善病毒适应性的预测,并使早期变种检测成为可能. 该框架揭示了突变链接的独特时间模式,跨阿尔法,三角形和Omicron等变体.
科学领域:
- 病毒学和进化生物学
- 计算生物学和生物信息学
- 基因组监测 基因组监测
背景情况:
- 突变集体影响病毒健康的表观相互作用对于理解病原体进化至关重要,但通常使用静态方法进行分析.
- 以前的方法缺乏捕捉这些复杂的遗传关系的动态和时间性质的能力,这些复杂的遗传关系在像SARS-CoV-2这样的不断发展的病毒种群中缺乏.
- 预测病原体进化需要能够解释突变如何随着时间的推移相互作用的方法.
研究的目的:
- 开发和验证隐藏的马尔科夫模型 (HMM) 框架,以捕捉SARS-CoV-2中表观关系的时间动态.
- 通过引入对突变相互作用的时间意识分析来解决静态,基于网络的方法的局限性.
- 提供用于早期检测病毒变体和理解它们的进化轨迹的计算工具.
主要方法:
- 开发了一种隐藏的马尔科夫模型 (HMM),以模拟单氨基酸变异对作为两种状态系统 (连接/不连接).
- 排放概率来自链接不平衡理论,并使用姆-韦尔赫算法优化过渡概率.
- 基于变的验证与时间顺序降低噪声用于确保生物信号的准确性,应用于超过200万个SARS-CoV-2尖端蛋白序列.
主要成果:
- 该HMM确定了三种类型的表观动态:永久的 (0.3%),短暂的 (0.3%) 和振荡的 (0.7%) 联系.
- 对阿尔法变异的分析显示,与一般的尖端蛋白对 (1.3%) 相比,显著更高的表皮性链接 (78%),具有许多振荡模式.
- 该框架检测到已知和新的表观相互作用,确定了早期的阿尔法变种网络,并揭示了三角形和Omicron变种的独特动态模式.
结论:
- 时间HMM框架有效地捕捉了SARS-CoV-2中的表观相互作用的动态性质,优于静态方法.
- 这种方法为变异演变提供了有价值的见解,识别了特定的链接模式 (例如,振荡),表明了依赖频率的选择.
- 计算工具使实时基因组监测成为可能,促进早期变种检测和更深入地了解病毒演变.
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