Quantifying the uncertainty of assembly-free genome-wide distance estimates and phylogenetic relationships using subsampling.
Quantifying the uncertainty of assembly-free genome-wide distance estimates and phylogenetic relationships using subsampling.
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DOI：
10.1016/j.cels.2022.06.007
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发表时间：
2022-10-19
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影响因子：
9.3
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中科院分区：
文献类型：
--
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在不进行比对甚至不获取组装信息的情况下计算两个基因组之间的距离有许多下游分析。然而，无比对方法，包括在快速发展的基因组略读领域，受到重大方法学差距的阻碍。虽然存在用于无组装距离计算的准确方法（许多基于k -mer的方法），但对估计距离的不确定性的测量尚未得到充分研究。在本文中，我们表明，自举法（用于测量估计量不确定性的标准非参数方法）对于依赖k -mer频率分布的基于k -mer的方法并不准确。相反，我们建议使用无放回抽样并结合一个校正步骤来降低推断分布的方差。我们表明，使用我们的方法得到的距离分布与估计量的真实不确定性相匹配。由此产生的系统发育支持值能有效区分正确和错误的分支，并识别出在文献中报道的无比对和基于比对的系统发育树中不同的有争议的分支。
由于缺乏可靠的不确定性估计方法，使用无比对和无组装方法构建的进化树的可用性和可解释性受到限制。标准的自举法（有放回抽样）不能使用，因为它违反了估计量的假设。作为一种替代方法，拉克特曼等人建议使用无放回抽样并结合校正来考虑抽样数据增加的方差，在多个实验中评估所提出的方法以证明其准确性，并将其应用于一系列低覆盖度的基因组略读数据。
Computing distance between two genomes without alignments or even access to assemblies has many downstream analyses. However, alignment-free methods, including in the fast-growing field of genome skimming, are hampered by a significant methodological gap. While accurate methods (many k-mer-based) for assembly-free distance calculation exist, measuring the uncertainty of estimated distances has not been sufficiently studied. In this paper, we show that bootstrapping, the standard non-parametric method of measuring estimator uncertainty, is not accurate for k-mer-based methods that rely on k-mer frequency profiles. Instead, we propose using subsampling (with no replacement) in combination with a correction step to reduce the variance of the inferred distribution. We show that the distribution of distances using our procedure matches the true uncertainty of the estimator. The resulting phylogenetic support values effectively differentiate between correct and incorrect branches and identify controversial branches that change across alignment-free and alignment-based phylogenies reported in the literature. Usability and interpretability of evolutionary trees constructed using alignment-free and assembly-free methods is limited due to a lack of robust approaches for uncertainty estimation. The standard bootstrapping method (sampling with replacement) cannot be used because it violates the assumptions of estimators. As an alternative, Rachtman et al. propose to use subsampling (without replacement) in combination with a correction to account for the increased variance of subsampled data, evaluate the proposed procedure in multiple experiments that demonstrate its accuracy, and apply it to a range of low coverage genome skimming data.
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影响因子：
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