ArticlePLoS computational biology2025
HAPP: High-accuracy pipeline for processing deep metabarcoding data.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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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
6 citing papers in PubMed.
- Molecular markers for the study of arbuscular mycorrhizal fungi.Archives of microbiology · 2026Review
- Molecular markers for the study of arbuscular mycorrhizal fungi.Archives of microbiology · 2026Review
- Spatial overlap and temporal synchrony between guilds of insect hosts and parasitoids.The Journal of animal ecology · 2026Article
- Accuracy of occurrence and abundance estimates from insect metabarcoding.bioRxiv : the preprint server for biology · 2026Article
- Small Bugs, Big Data: Metagenomics for Arthropod Biodiversity Monitoring.Ecology and evolution · 2025Article
- Data of the Insect Biome Atlas: a metabarcoding survey of the terrestrial arthropods of Sweden and Madagascar.Scientific data · 2025Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Deep metabarcoding offers an efficient and reproducible approach to biodiversity monitoring, but noisy data and incomplete reference databases challenge accurate diversity estimation and taxonomic annotation. Here, we introduce a novel algorithm, NEEAT, for removing spurious operational taxonomic units (OTUs) originating from nuclear-embedded mitochondrial DNA sequences (NUMTs) or sequencing errors. It integrates 'echo' signals across samples with the identification of unusual evolutionary patterns among similar DNA sequences. We also extensively benchmark current tools for chimera removal, taxonomic annotation and OTU clustering of deep metabarcoding data. The best performing tools/parameter settings are integrated into HAPP, a high-accuracy pipeline for processing deep metabarcoding data. Tests using CO1 data from BOLD and large-scale metabarcoding data on insects demonstrate that HAPP significantly outperforms existing methods, while enabling efficient analysis of extensive datasets by parallelizing computations across taxonomic groups.
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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.