Placing new sequences onto reference phylogenies is increasingly used for analyzing environmental samples, especially microbiomes. Existing placement methods assume that query sequences have evolved under specific models directly on the reference phylogeny. For example, they assume single-gene data (e.g., 16S rRNA amplicons) have evolved under the GTR model on a gene tree. Placement, however, often has a more ambitious goal: extending a (genome-wide) species tree given data from individual genes without knowing the evolutionary model. Addressing this challenging problem requires new directions. Here, we introduce Deep-learning Enabled Phylogenetic Placement (DEPP), an algorithm that learns to extend species trees using single genes without pre-specified models. In simulations and on real data, we show that DEPP can match the accuracy of model-based methods without any prior knowledge of the model. We also show that DEPP can update the multi-locus microbial tree-of-life with single genes with high accuracy. We further demonstrate that DEPP can combine 16S and metagenomic data onto a single tree, enabling community structure analyses that take advantage of both sources of data.

中文翻译：

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DEPP：深度学习可以使用单基因扩展物种树

将新序列置于参考系统发育中越来越多地用于分析环境样品，尤其是微生物组。现有的放置方法假设查询序列已经在特定模型下直接在参考系统发育上进化。例如，他们假设单基因数据（例如，16S rRNA 扩增子）已经在基因树上的 GTR 模型下进化。然而，放置通常有一个更雄心勃勃的目标：在不知道进化模型的情况下，根据来自单个基因的数据扩展（全基因组）物种树。解决这个具有挑战性的问题需要新的方向。在这里，我们介绍了启用深度学习的系统发育布局 (DEPP)，这是一种学习使用单个基因扩展物种树的算法，无需预先指定的模型。在模拟和真实数据中，我们表明 DEPP 可以在没有任何模型先验知识的情况下匹配基于模型的方法的准确性。我们还表明，DEPP 可以高精度地用单基因更新多位点微生物生命树。我们进一步证明 DEPP 可以将 16S 和宏基因组数据组合到一棵树上，从而实现利用两种数据源的社区结构分析。