ArticleeLife2024
A rapid phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Bycatch in a bottle: what taxa are recoverable from metabarcoding DNA in historical invertebrate collection preservative fluid?The Journal of heredity · 2026Article
- Molecular Clock Dating of Ancient Environmental DNA Reveals Damage Beyond Deamination.bioRxiv : the preprint server for biology · 2026Article
- Field strains of the unicellular alga Chlamydomonas reinhardtii exhibit multicellular characteristics that shape their interactions.The ISME journal · 2026Article
- Zero-shot deep learning for the annotation of unknown eDNA sequences using co-occurrences and phylogenetic embeddings.PLoS computational biology · 2025Article
- Limited genomic signatures of population collapse in the critically endangered black abalone (Haliotis cracherodii).Molecular ecology · 2025Article
- A rapid phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets.eLife · 2024Article
- Limited genomic signatures of population collapse in the critically endangered black abalone (bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Environmental DNA (eDNA) is becoming an increasingly important tool in diverse scientific fields from ecological biomonitoring to wastewater surveillance of viruses. The fundamental challenge in eDNA analyses has been the bioinformatical assignment of reads to taxonomic groups. It has long been known that full probabilistic methods for phylogenetic assignment are preferable, but unfortunately, such methods are computationally intensive and are typically inapplicable to modern next-generation sequencing data. We present a fast approximate likelihood method for phylogenetic assignment of DNA sequences. Applying the new method to several mock communities and simulated datasets, we show that it identifies more reads at both high and low taxonomic levels more accurately than other leading methods. The advantage of the method is particularly apparent in the presence of polymorphisms and/or sequencing errors and when the true species is not represented in the reference database.
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Registered trials
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