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免费午餐还没有结束 - - 系统地探索数值值,以最大概率的基因推理推理
Julia Haag1, Lukas Hübner1,2, Alexey M Kozlov1
1Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany.
Bioinformatics advances
|September 26, 2023
概括
调整最大概率 (ML) 的数值值,像RAxML-NG和IQ-TREE这样的家族遗传推理工具可以显著加快树计算. 这些变化加快了ML树推断和引导分析,而不会影响得到的基系树的准确性.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 人类遗传学 是一个学科.
背景情况:
- 最大概率 (ML) 是一个标准的方法,用于遗传学推断.
- 机器学习软件依赖于数值优化与趋同值.
- 这些值对推断速度和准确性的影响尚未完全理解.
研究的目的:
- 系统地分析数值值设置对ML树推断的影响.
- 评估对日志概率值和计算运行时间的影响.
- 为了确定是否调整值可以在不牺牲树质量的情况下加速推断.
主要方法:
- 在RAxML-NG,IQ-TREE和FastTree中对数值值设置的系统分析.
- 在经验数据集上进行测试,以测量日志概率和运行时间.
- 结果与默认值和修改的值进行比较.
主要成果:
- 通过修改两个数值值,在RAxML-NG和IQ-TREE中大幅加快树推理.
- 没有对推断的家族遗传树质量产生重大影响.
- 加快RAxML-NG启动分析,而不影响支持值.
- 报告了RAxML-NG (高达1.9倍) 和IQ-TREE (高达1.3倍) 的特定加速度,并修改了值.
结论:
- 优化数值值提供了一个实用的策略,以提高ML基因推理的效率.
- 这些发现为使用RAxML-NG和IQ-TREE进行更快,更有效的遗传学分析提供了经验证据.
- 调整值是加速计算遗传学工作流程的可行方法.
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