代表和扩展节的进化历史的合奏与指向的非循环图
Will Dumm1,2, Mary Barker1,2, William Howard-Snyder3
1Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
Journal of mathematical biology
|October 25, 2023
概括
本研究引入了历史分区指向非循环图 (历史sDAG) 来表示高质量的遗传树的集合. 这种新的方法有效地处理大型数据集,并发现比传统方法更平等的节树.
科学领域:
- 计算生物学 计算生物学
- 人类遗传学 是一个学科.
- 生物信息学是一种生物信息学.
背景情况:
- 确定最好的遗传树是至关重要的,但识别一系列高质量的树木往往是必要的.
- 目前用于树组合分析的贝叶斯方法在大型数据集的可扩展性方面存在困难.
- 存储所有潜在的良好分叉树,用于具有低信号的大数据集是计算不可行的.
研究的目的:
- 开发一种可扩展的方法来表示和分析家族遗传树的集合.
- 引入一种新的数据结构,用于紧地存储高质量的树木集合.
- 为了能够发现更广泛的一组同样节的树木.
主要方法:
- 历史分区的发展指向非循环图 (历史 sDAG) 数据结构.
- 有效的构建和修剪算法用于历史 sDAG.
- 展示历史 sDAG 能够用标记的内部节点来表示树组合的能力.
主要成果:
- 历史sDAG紧地代表了一组家族遗传树,包括那些有标记祖先序列的树.
- 有效的算法允许历史sDAG的构建和修剪,以专注于最大限度的节树.
- 历史sDAG方便识别了许多额外的同样节的树木,超越了最初的组合.
结论:
- 历史的sDAG提供了一个计算高效的解决方案,用于代表树组合在遗传学.
- 这种新型对象为大型数据集提供了传统贝叶斯方法的可扩展替代方案.
- 历史sDAG作为一个基础结构,用于先进的不确定性量化在家族遗传学分析.
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