2 December 2025
New paper: Benchmarking metabarcode sequence denoisers for improved invertebrate community diversity estimates
A new BGE paper has been published! The paper called ‘Benchmarking metabarcode sequence denoisers for improved invertebrate community diversity estimates’ was written by Marius Hannes Eisele, Brendan Furneaux, Tomas Roslin, Otso Ovaskainen, Brent C. Emerson and Carmelo Andújar.
The paper evaluates how denoising pipelines for metabarcoding perform and discusses how they can be improved.
Abstract
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- DNA metabarcoding is fast becoming a method of choice for the inventory and monitoring of community-level arthropod biodiversity, with growing interest in the recovery of haplotype-level variation within species from metabarcoding reads. Denoising tools have been established as the standard for metabarcode processing, with the promise to provide accurate sequence data at the haplotype level.
- Despite their widespread use, the accuracy of denoising methods relative to the elimination of non-target sequences and retention of target sequences has received limited attention. Recent studies based on whole organism community DNA (wocDNA) metabarcoding suggest that misleading estimates of species richness and diversity may be obtained after denoising. Understanding and evaluating how denoising pipelines perform and how they can be improved is crucial for broadening the applicability of metabarcoding to community ecology, metaphylogeography and biodiversity inventory and monitoring.
- To evaluate the performance of the most popularly used denoising tools, we use (i) complex communities from malaise traps subjected to wocDNA metabarcoding (representing the information yielded by metabarcoding) and (ii) additional sequencing of each individual specimen (representing the ground truth of sample contents). To these data, we apply the metabarcoding evaluation rationale described in Andújar et al. (2021), complemented by an additional layer of clustering and filtering with LULU and metaMATE.
- This study confirms that denoising tools alone, but also in combination with clustering, retain a high number of non-target amplicon sequence variants (ASVs) and operational taxonomic units (OTUs). Both LULU and metaMATE removed large amounts of non-target sequences and noise. This, however, came at the cost of the collateral removal of target sequences. We recommend a workflow including one of the better performing denoising tools: DADA2 or UNOISE3, in combination with LULU or metaMATE as a customizable option for evaluating pipeline performance.
