ArticleSystematic biology2023
DEPP: Deep Learning Enables Extending Species Trees using Single Genes.
Article in Systematic biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Performance and Limitations of Out-Of-Distribution Detection for Insect DNA Barcoding.Ecology and evolution · 2026Article
- Deconvolving Phylogenetic Distance Mixtures.bioRxiv : the preprint server for biology · 2026Article
- Phylogenetic Placement Using SCAMPP and Batch-SCAMPP.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Machine Learning Enables Alignment-Free Distance Calculation and Phylogenetic Placement Using k-Mer Frequencies.Molecular ecology resources · 2025Article
- Evolutionary Reasoning Does Not Arise in Standard Usage of Protein Language Models.bioRxiv : the preprint server for biology · 2025Article
- Phylogenetic Methods Meet Deep Learning.Genome biology and evolution · 2025Article
- PhyloTune: An efficient method to accelerate phylogenetic updates using a pretrained DNA language model.Nature communications · 2025Article
- Phyloformer: Fast, Accurate, and Versatile Phylogenetic Reconstruction with Deep Neural Networks.Molecular biology and evolution · 2025Article
- Evaluating the method reproducibility of deep learning models in biodiversity research.PeerJ. Computer science · 2025Article
- Constructing phylogenetic trees for microbiome data analysis: A mini-review.Computational and structural biotechnology journal · 2024Review
- Toward a Semi-Supervised Learning Approach to Phylogenetic Estimation.Systematic biology · 2024Article
- Reliable estimation of tree branch lengths using deep neural networks.PLoS computational biology · 2024Article
- Scaling DEPP phylogenetic placement to ultra-large reference trees: a tree-aware ensemble approach.Bioinformatics (Oxford, England) · 2024Article
- Generation of accurate, expandable phylogenomic trees with uDance.Nature biotechnology · 2024Article
- Median quartet tree search algorithms using optimal subtree prune and regraft.Algorithms for molecular biology : AMB · 2024Article
- Quantifying the uncertainty of assembly-free genome-wide distance estimates and phylogenetic relationships using subsampling.Cell systems · 2022Article
- Recent progress on methods for estimating and updating large phylogenies.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
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- Disentangling host-microbiota complexity through hologenomics.Nature reviews. Genetics · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.].
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.