凸型ML:在不可逆转突变模型下快速准确地估计分支长度,通过应用CRISPR/Cas9基谱系追踪来说明
Sebastian Prillo1, Akshay Ravoor1, Nir Yosef1,2
1Computer Science Division, University of California, Berkeley, USA.
Systematic biology
|August 12, 2025
概括
我们介绍了ConvexML,这是一种使用不可逆转突变模型估计家族遗传树枝长度的新方法,对于CRISPR/Cas9谱系追踪至关重要. 这种方法将细胞谱系树精细化为时间解析的时间表,为细胞群体动态提供了洞察力.
科学领域:
- 统计类遗传学 统计类遗传学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 树枝长度估计是基因树重建的关键.
- 为了追踪CRISPR/Cas9的血统,需要对不可逆转的突变进行模型.
- 当前的方法经常重建树的拓,但缺乏时间分辨率.
研究的目的:
- 开发第一个可扩展和准确的方法,将单细胞树拓精制成时间解析的时间图.
- 在一般不可逆转突变模型下估计分支长度,特别是对于CRISPR/Cas9谱系追踪数据.
- 为了使细胞群进化动态的详细研究.
主要方法:
- 在一般不可逆转突变模型下规范化的最大概率估计.
- 一种保守的最大节的方法来重建祖先状态.
- 一个数据编码方案来处理CRISPR/Cas9数据的特殊性,如双重切除事件.
- 惩罚最大概率估计与最小分支长度约束和伪计数以稳定估计.
主要成果:
- 开发了"ConvexML",一种方法,通过估计分支长度,将单细胞树拓转化为时间表.
- ConvexML有效地解决了一个凸的最大概率估计问题,可以在几秒钟内解决.
- 基准测试显示,ConvexML在准确性方面与现有方法 (例如,TiDeTree,LAML) 相匹配或优于现有方法,同时速度明显更快 (10100x).
结论:
- 在不可逆转的突变模型下,ConvexML提供了一种快速,稳健和通用的分支长度估计方法.
- 该方法广泛适用于具有不可逆转突变和不可忽视的缺失数据的进化模型.
- 作为一个开源的Python包,可用的ConvexML促进了对CRISPR/Cas9谱系追踪数据的分析,以了解细胞群动态.
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