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DEPP: Deep learning enables extending species trees using single genes

Citation

Jiang, Yueyu; Balaban, Metin; Zhu, Qiyun; Mirarab, Siavash (2022), DEPP: Deep learning enables extending species trees using single genes, Dryad, Dataset, https://doi.org/10.6076/D14G68

Abstract

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: https://doi.org/10.6076/D1JS3Z (published February 4, 2022).

Funding