ArticleSystematic biology2020
APPLES: Scalable Distance-Based Phylogenetic Placement with or without Alignments.
Article in Systematic biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- metaWEPP: leveraging biobank-scale intra-species phylogenies for near-haplotype resolution in metagenomic analysis.NAR genomics and bioinformatics · 2026Article
- WASTER: Practical de novo Phylogenomics from Low-coverage Short Reads.Molecular biology and evolution · 2026Article
- krepp: a k-mer-based maximum pseudo-likelihood method for estimating read distances and genome-wide phylogenetic placement.Genome biology · 2026Article
- Deconvolving Phylogenetic Distance Mixtures.bioRxiv : the preprint server for biology · 2026Article
- PlaceMyFossils: An Integrative Approach to Analyze and Visualize the Phylogenetic Placement of Fossils Using Backbone Trees.Systematic biology · 2025Article
- Machine Learning Enables Alignment-Free Distance Calculation and Phylogenetic Placement Using k-Mer Frequencies.Molecular ecology resources · 2025Article
- TIPP3 and TIPP3-fast: Improved abundance profiling in metagenomics.PLoS computational biology · 2025Article
- Phyloformer: Fast, Accurate, and Versatile Phylogenetic Reconstruction with Deep Neural Networks.Molecular biology and evolution · 2025Article
- Read Length Dominates Phylogenetic Placement Accuracy of Ancient DNA Reads.Molecular biology and evolution · 2025Article
- Testing Phylogenetic Placement Accuracy of DNA Barcode Sequences on a Fish Backbone Tree: Implications of Backbone Tree Completeness and Species Representation.Ecology and evolution · 2025Article
- Skmer approach improves species discrimination in taxonomically problematic genusPlant diversity · 2024Article
- A rapid phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets.eLife · 2024Article
- Scaling DEPP phylogenetic placement to ultra-large reference trees: a tree-aware ensemble approach.Bioinformatics (Oxford, England) · 2024Article
- Analyses of Nuclear Reads Obtained Using Genome Skimming.Methods in molecular biology (Clifton, N.J.) · 2024Article
- EPIK: precise and scalable evolutionary placement with informative k-mers.Bioinformatics (Oxford, England) · 2023Article
- Online tree expansion could help solve the problem of scalability in Bayesian phylogenetics.Systematic biology · 2023Article
- DEPP: Deep Learning Enables Extending Species Trees using Single Genes.Systematic biology · 2023Article
- SCAMPP+FastTree: improving scalability for likelihood-based phylogenetic placement.Bioinformatics advances · 2023Article
- 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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Authors and funding
3 authors.
Funding
Abstract
Placing a new species on an existing phylogeny has increasing relevance to several applications. Placement can be used to update phylogenies in a scalable fashion and can help identify unknown query samples using (meta-)barcoding, skimming, or metagenomic data. Maximum likelihood (ML) methods of phylogenetic placement exist, but these methods are not scalable to reference trees with many thousands of leaves, limiting their ability to enjoy benefits of dense taxon sampling in modern reference libraries. They also rely on assembled sequences for the reference set and aligned sequences for the query. Thus, ML methods cannot analyze data sets where the reference consists of unassembled reads, a scenario relevant to emerging applications of genome skimming for sample identification. We introduce APPLES, a distance-based method for phylogenetic placement. Compared to ML, APPLES is an order of magnitude faster and more memory efficient, and unlike ML, it is able to place on large backbone trees (tested for up to 200,000 leaves). We show that using dense references improves accuracy substantially so that APPLES on dense trees is more accurate than ML on sparser trees, where it can run. Finally, APPLES can accurately identify samples without assembled reference or aligned queries using kmer-based distances, a scenario that ML cannot handle. APPLES is available publically at github.com/balabanmetin/apples.
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