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DEPP: Deep learning enables extending species trees using single genes

Cite this dataset

Jiang, Yueyu; Balaban, Metin; Zhu, Qiyun; Mirarab, Siavash (2022). DEPP: Deep learning enables extending species trees using single genes [Dataset]. Dryad.


Placing new sequences onto reference phylogenies is increasingly used for analyzing environmental samples, especially microbiomes. However, existing placement methods have a fundamental limitation: they assume that query sequences have evolved using specific models directly on the reference phylogeny. Thus, they can place single-gene data (e.g., 16S rRNA amplicons) onto their own gene tree. This practice is a proxy for a more ambitious goal: extending a (genome-wide) species tree given data from individual genes. No algorithm currently addresses this challenging problem. Here, we introduce Deep-learning Enabled Phylogenetic Placement (DEPP), an algorithm that learns to extend species trees using single genes without pre-specified models. We show that DEPP updates the multi-locus microbial tree-of-life with single genes with high accuracy. We further demonstrate that DEPP can achieve the long-standing goal of combining 16S and metagenomic data onto a single tree, enabling community structure analyses that were previously impossible and producing robust patterns.

Usage notes

Please note, this dataset is the most recent version of a duplicate dataset available via this link: (published February 4, 2022).