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地测学是用来描述植物遗传风景的
Marzieh Khodaei1, Megan Owen2, Peter Beerli1
1Department of Scientific Computing, Florida State University, Tallahassee, FL, United States of America.
PloS one
|June 23, 2023
概括
我们介绍了Pathtrees,这是一种通过生成中间树来探索遗传树空间的新方法. 这种方法有助于可视化进化关系,并发现新的,极有可能的家族遗传树.
科学领域:
- 进化生物学是进化的生物学.
- 计算型的遗传学学.
- 生物信息学是一种生物信息学.
背景情况:
- 遗传树对进化历史至关重要,但鉴定最大概率树是计算密集的,因为复杂的概率景观和巨大的树木空间.
- 现有的树搜索算法经常探索树空间的有限区域,可能缺少最佳或新的家族遗传假设.
研究的目的:
- 开发一种方法来生成和可视化使用Billera-Holmes-Vogtmann (BHV) 距离在任何两棵树之间的最短路径上生成和可视化中间的家族遗传树 (pathtrees).
- 探索遗传树空间的研究不足的区域,确定本地最佳树木,并发现当前算法可能错过的高概率树木.
- 将路径树方法的性能与Paup*,RAxML和RevBayes等已有的树搜索工具进行比较.
主要方法:
- 比勒拉-霍尔姆斯-沃格特曼 (BHV) 距离被用来定义家族遗传树之间的最短路径.
- 在这些最短的路径上产生了中间树,称为pathtrees.
- 该方法应用于两个数据集:灵长类动物 (23种) 和牛奶蛇 (182个个体) 的线粒体DNA序列.
- 用日志概率作为健身函数可视化了家族遗传树空间.
- 通过比较发现的最高概率树和对Paup*,RAxML和RevBayes的拓学的新性,包括MCMC方法,来评估性能.
- 树空间映射的准确性使用缩小维度技术进行了验证.
主要成果:
- 基因树方法成功生成了中间树,提供了一种结构化的方法来探索和可视化基因树空间的部分.
- 该方法识别了具有与启发式搜索方法发现的概率相似的树,并揭示了新的拓.
- 在不同的尺度上实现了概率景观的可视化,突出了潜在的概率露台.
- 该方法发现了马尔科夫链蒙特卡洛 (MCMC) 方法未识别的相关的家族遗传树.
- 验证指标证实了该方法在将树空间映射到较低维度中的有效性.
结论:
- 遗传树提供了一个补充的方法,以启发式搜索分析在家族遗传学.
- 这种方法的可视化功能使得可能的景观可以进行详细的检查,并探索以前未曾访问过的树木空间区域.
- 这种方法通过系统地探索树木空间来增强最佳和新的遗传学假设的发现.
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