Here, we describe Assemble Species by Automatic Partitioning (ASAP), a new method to build species partitions from single locus sequence alignments (i.e., barcode data sets). ASAP is efficient enough to split data sets as large 10⁴ sequences into putative species in several minutes. Although grounded in evolutionary theory, ASAP is the implementation of a hierarchical clustering algorithm that only uses pairwise genetic distances, avoiding the computational burden of phylogenetic reconstruction. Importantly, ASAP proposes species partitions ranked by a new scoring system that uses no biological prior insight of intraspecific diversity. ASAP is a stand‐alone program that can be used either through a graphical web‐interface or that can be downloaded and compiled for local usage. We have assessed its power along with three others programs (ABGD, PTP and GMYC) on 10 real COI barcode data sets representing various degrees of challenge (from small and easy cases to large and complicated data sets). We also used Monte‐Carlo simulations of a multispecies coalescent framework to assess the strengths and weaknesses of ASAP and the other programs. Through these analyses, we demonstrate that ASAP has the potential to become a major tool for taxonomists as it proposes rapidly in a full graphical exploratory interface relevant species hypothesis as a first step of the integrative taxonomy process.

中文翻译：

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尽快：通过自动分区组装物种

在这里，我们描述了通过自动分区（ASAP）组装物种，这是一种从单基因座序列比对（即条形码数据集）构建物种分区的新方法。ASAP 的效率足以将数据集拆分为大 10 ⁴在几分钟内将序列转化为推定的物种。尽管基于进化理论，ASAP 是层次聚类算法的实现，该算法仅使用成对遗传距离，避免了系统发育重建的计算负担。重要的是，ASAP 提出了一种新的评分系统对物种分区进行排序，该系统不使用对种内多样性的生物学先验见解。ASAP 是一个独立程序，可以通过图形 Web 界面使用，也可以下载和编译以供本地使用。我们与其他三个程序（ABGD、PTP 和 GMYC）在 10 个真实 COI 条码数据集上评估了它的能力，这些数据集代表了不同程度的挑战（从小而简单的案例到大而复杂的数据集）。我们还使用了多物种聚结框架的蒙特卡罗模拟来评估 ASAP 和其他程序的优势和劣势。通过这些分析，我们证明 ASAP 有潜力成为分类学家的主要工具，因为它在完整的图形探索界面相关物种假设中迅速提出作为综合分类法过程的第一步。