DEPP: Deep learning enables extending species trees using single genes

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.