DEPP: Deep Learning Enables Extending Species Trees using Single Genes
DEPP: Deep Learning Enables Extending Species Trees using Single Genes
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DOI：
10.1093/sysbio/syac031
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发表时间：
2022-04-29
影响因子：
6.5
通讯作者：
Mirarab, Siavash
中科院分区：
文献类型：
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作者：
Jiang, Yueyu;Balaban, Metin;Mirarab, Siavash
将新序列置于参考系统发育树上越来越多地用于分析环境样本，尤其是微生物群落。现有的放置方法假定查询序列是在参考系统发育树上根据特定模型直接进化的。例如，它们假定单基因数据（如16S rRNA扩增子）是在基因树上根据GTR模型进化的。然而，放置通常有一个更宏大的目标：在不知道进化模型的情况下，根据单个基因的数据扩展（全基因组范围的）物种树。解决这个具有挑战性的问题需要新的方向。在此，我们介绍深度学习使能的系统发育放置（DEPP），这是一种无需预先指定模型就能学习使用单个基因扩展物种树的算法。在模拟和实际数据中，我们表明DEPP能够在对模型没有任何先验知识的情况下达到基于模型的方法的准确性。我们还表明DEPP能够以高精度用单个基因更新多位点微生物生命之树。我们进一步证明DEPP能够将16S和宏基因组数据合并到一棵树上，从而能够进行利用两种数据源的群落结构分析。[深度学习；基因树不一致；宏基因组学；微生物群落分析；神经网络；系统发育放置]
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 prespecified 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 multilocus 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. [Deep learning; gene tree discordance; metagenomics; microbiome analyses; neural networks; phylogenetic placement.]