Deriving confidence intervals for mutation rates across a wide range of evolutionary distances using FracMinHash.
Deriving confidence intervals for mutation rates across a wide range of evolutionary distances using FracMinHash.
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DOI：
10.1101/gr.277651.123
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发表时间：
2023-07
期刊：
影响因子：
7
通讯作者：
Koslicki, David
中科院分区：
文献类型：
--
作者：
Rahman Hera, Mahmudur;Pierce-Ward, N Tessa;Koslicki, David
草图方法为计算生物学家提供了可扩展的技术来分析不断增长的数据集。MinHash就是一种这样的技术来估计集合相似性，最近得到了广泛的应用。然而，传统的MinHash在应用于非常不同大小的集合时表现不佳。FracMinHash最近被引入作为MinHash的修改，以弥补集合大小不同时的性能不足。该方法已成功应用于广泛使用的工具sourmash gather中的宏基因组分类分析。尽管实验证据令人鼓舞，但FracMinHash尚未从理论角度进行分析。在本文中，我们进行这样的分析，以获得FracMinHash的各种统计数据，并证明，虽然FracMinHash不是无偏的（在这个意义上，它的期望值不等于它试图估计的数量），这种偏见是很容易纠正的遏制和Jaccard索引版本。接下来，我们将展示如何通过假设一个简单的突变模型，使用FracMinHash来计算一对序列之间进化突变距离的点估计值和置信区间。我们还调查了边缘情况，在这些情况下，这些分析可能无法有效地警告FracMinHash的用户，表明这种情况的可能性。我们的分析表明，FracMinHash估计一个基因组在一个大的宏基因组中的包容性更准确，更精确地与传统的MinHash相比，点估计和置信区间在估计突变距离方面表现得更好。
Sketching methods offer computational biologists scalable techniques to analyze data sets that continue to grow in size. MinHash is one such technique to estimate set similarity that has enjoyed recent broad application. However, traditional MinHash has previously been shown to perform poorly when applied to sets of very dissimilar sizes. FracMinHash was recently introduced as a modification of MinHash to compensate for this lack of performance when set sizes differ. This approach has been successfully applied to metagenomic taxonomic profiling in the widely used tool sourmash gather. Although experimental evidence has been encouraging, FracMinHash has not yet been analyzed from a theoretical perspective. In this paper, we perform such an analysis to derive various statistics of FracMinHash, and prove that although FracMinHash is not unbiased (in the sense that its expected value is not equal to the quantity it attempts to estimate), this bias is easily corrected for both the containment and Jaccard index versions. Next, we show how FracMinHash can be used to compute point estimates as well as confidence intervals for evolutionary mutation distance between a pair of sequences by assuming a simple mutation model. We also investigate edge cases in which these analyses may fail to effectively warn the users of FracMinHash indicating the likelihood of such cases. Our analyses show that FracMinHash estimates the containment of a genome in a large metagenome more accurately and more precisely compared with traditional MinHash, and the point estimates and confidence intervals perform significantly better in estimating mutation distances.