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使用matOptimize的在线植物遗传学产生了相当的树木,并且对于大型SARS-CoV-2植物遗传学比de novo和最大概率实现更有效
Alexander M Kramer1,2, Bryan Thornlow1,2, Cheng Ye3
1Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA.
Systematic biology
|May 26, 2023
概括
对SARS-CoV-2的家族遗传学分析可以使用"在线"方法和最大储蓄 (MP) 方法高效地进行. 这些方法,如UShER和matOptimize,比大型,密集的病毒基因组数据集的传统方法更快,更准确.
科学领域:
- 计算生物学 计算生物学
- 病毒学 病毒学
- 基因组学就是基因组学.
背景情况:
- 遗传学对SARS-CoV-2研究至关重要,有助于基因组监测和变种跟踪.
- 传统的 de novo 遗传学推断是计算密集的,不适合大规模,不断增长的 SARS-CoV-2 数据集.
- 对SARS-CoV-2基因组的密集采样促使对不同的基因推断方法进行评估.
研究的目的:
- 为了比较新的 de novo 与在线的 SARS-CoV-2 类遗传学方法的性能.
- 评价最大概率 (ML),伪ML和最大 (MP) 框架,以推断大而密集的SARS-CoV-2基因.
- 评估基于MP的方法 (UShER, matOptimize) 与ML方法的准确性和计算效率.
主要方法:
- 评估了新的和在线的基因推断方法.
- 使用大型SARS-CoV-2基因组数据集比较ML,伪ML和MP的遗传学框架.
- 利用UShER和matOptimize进行MP优化,并将它们的速度和准确性与ML实现进行比较.
主要成果:
- 在线遗传学产生了与SARS-CoV-2的新型分析相比较的遗传学树.
- 使用UShER和matOptimize的MP优化产生了与流行的ML和伪ML工具相当的SARS-CoV-2基因.
- 使用UShER和matOptimize的MP优化速度是ML实现的数千倍;在线族遗传学比de novo推断更快.
结论:
- 基于的方法 (UShER, matOptimize) 为大型SARS-CoV-2基因组提供了对ML的准确和实用的替代方案.
- 在线遗传学是对SARS-CoV-2基因组数据的动态性质更合适的方法.
- 对于数据集具有密集采样和短分支长度的数据集,MP方法非常有效,适用于超出SARS-CoV-2的范围.
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