A Systematic Review of Sources of Variability and Uncertainty in eDNA Data for Environmental Monitoring

Mathieu, Chloé; Hermans, Syrie M.; Lear, Gavin; Buckley, Thomas R.; Lee, Kevin C.; Buckley, Hannah

doi:10.3389/fevo.2020.00135

Cited by 55 publications

(50 citation statements)

References 99 publications

(101 reference statements)

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“…The field of eukaryotic metabarcoding is witnessing an exponential growth, both in the number of communities and substrates studied and the applications reported (reviewed in [ 1 – 4 ]). In parallel, technical and conceptual issues are being discussed (e.g., [ 5 , 6 ]) and new methods and pipelines generated. In some cases, however, new practices are established after a paper reporting a technique is published and followed uncritically, sometimes pushing its application outside the context in which it was first developed.…”

Section: Introductionmentioning

confidence: 99%

To denoise or to cluster, that is not the question: optimizing pipelines for COI metabarcoding and metaphylogeography

et al. 2021

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Background The recent blooming of metabarcoding applications to biodiversity studies comes with some relevant methodological debates. One such issue concerns the treatment of reads by denoising or by clustering methods, which have been wrongly presented as alternatives. It has also been suggested that denoised sequence variants should replace clusters as the basic unit of metabarcoding analyses, missing the fact that sequence clusters are a proxy for species-level entities, the basic unit in biodiversity studies. We argue here that methods developed and tested for ribosomal markers have been uncritically applied to highly variable markers such as cytochrome oxidase I (COI) without conceptual or operational (e.g., parameter setting) adjustment. COI has a naturally high intraspecies variability that should be assessed and reported, as it is a source of highly valuable information. We contend that denoising and clustering are not alternatives. Rather, they are complementary and both should be used together in COI metabarcoding pipelines. Results Using a COI dataset from benthic marine communities, we compared two denoising procedures (based on the UNOISE3 and the DADA2 algorithms), set suitable parameters for denoising and clustering, and applied these steps in different orders. Our results indicated that the UNOISE3 algorithm preserved a higher intra-cluster variability. We introduce the program DnoisE to implement the UNOISE3 algorithm taking into account the natural variability (measured as entropy) of each codon position in protein-coding genes. This correction increased the number of sequences retained by 88%. The order of the steps (denoising and clustering) had little influence on the final outcome. Conclusions We highlight the need for combining denoising and clustering, with adequate choice of stringency parameters, in COI metabarcoding. We present a program that uses the coding properties of this marker to improve the denoising step. We recommend researchers to report their results in terms of both denoised sequences (a proxy for haplotypes) and clusters formed (a proxy for species), and to avoid collapsing the sequences of the latter into a single representative. This will allow studies at the cluster (ideally equating species-level diversity) and at the intra-cluster level, and will ease additivity and comparability between studies.

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Section: Introductionmentioning

confidence: 99%

To denoise or to cluster, that is not the question: optimizing pipelines for COI metabarcoding and metaphylogeography

et al. 2021

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“…Many factors can influence the results of metabarcoding of eDNA from water samples. Some examples are low DNA quantity or quality, cross-contamination during field sampling, cross-contamination in the laboratory, primer competition among the DNA of different species during PCR or sequence mistakes introduced by TAQ-polymerase inconsistencies (Taberlet et al 2018;Mathieu et al 2020). All these phenomena can lead to the false negative or false positive identification of species in a sample or to some species having many sequencing reads, while other species show only a few reads.…”

Section: Challenges Met During the Development Of The Workflowsmentioning

confidence: 99%

Ready-to-use workflows for the implementation of genetic tools in conservation management

Holderegger

Schmidt

Grünig

et al. 2020

Conservation Genet Resour

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We present a conservation genetics tool kit, which offers two ready-to-use workflows for the routine application of genetic methods in conservation management. The workflows were optimized for work load and costs and are accompanied by an easy-to-read and richly illustrated manual with guidelines regarding sampling design, sampling of genetic material, necessary permits, laboratory methods, statistical analyses and documentation of results in a practice-oriented way. The manual also provides a detailed interpretation help for the implementation of the results in conservation management. One workflow deals with the identification of pond-breeding amphibians based on metabarcoding and environmental DNA (eDNA) from water samples. This workflow also discriminates the morphologically similar water frogs (Pelophylax sp.) and other closely related species (e.g. Triturus cristatus and T. carnifex). The second workflow studies connectivity among populations using microsatellite markers. Its statistical analyses encompass the detection of genetic groups and historical, recent and current dispersal and gene flow. Using the two workflows does not involve academic research institutes; they can be applied by environmental consultancies, laboratories from the private sector, governmental agencies or non-governmental organisations. These and additional conservation genetic workflows will hopefully foster the routine use of genetic methods in conservation management.

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“…The field of eukaryotic metabarcoding is witnessing an exponential growth, both in the number of communities and substrates studied and the applications reported (reviewed in Deiner et al 2017, Aylagas et al 2018, Bani et al 2020, Compson et al 2020. In parallel, technical and conceptual issues are being discussed (e.g., Mathieu et al 2020, Rodriguez-Ezpeleta et al 2020 and new methods and pipelines generated. In some cases, however, new practices are established after a paper reporting a technique is published and followed, sometimes pushing its application outside the context in which it was first developed.…”

Section: Introductionmentioning

confidence: 99%

To denoise or to cluster? That is not the question. Optimizing pipelines for COI metabarcoding and metaphylogeography

Antich

Palacín

Wangensteen

et al. 2021

Preprint

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The recent blooming of metabarcoding applications to biodiversity studies comes with some relevant methodological debates. One such issue concerns the treatment of reads by denoising or by clustering methods, which have been wrongly presented as alternatives. It has also been suggested that denoised sequence variants should replace clusters as the basic unit of metabarcoding analyses, missing the fact that sequence clusters are a proxy for species-level entities, the basic unit in biodiversity studies. We argue here that methods developed and tested for ribosomal markers have been uncritically applied to highly variable markers such as cytochrome oxidase I (COI) without conceptual or operational (e.g., parameter setting) adjustment. COI has a naturally high intraspecies variability that should be assessed and reported, as it is a source of highly valuable information. We contend that denoising and clustering are not alternatives. Rather, they are complementary and both should be used together in COI metabarcoding pipelines. Using a typical dataset from benthic marine communities, we compared two denoising procedures (based on the UNOISE3 and the DADA2 algorithms), set suitable parameters for denoising and clustering COI datasets, and compared the outcome of applying these processes in different orders. Our results indicate that denoising based on the UNOISE3 algorithm preserves a higher intra-cluster variability. We suggest and test ways to improve this algorithm taking into account the natural variability of each codon position in coding genes. The order of the steps (denoising and clustering) has little influence on the final outcome. We recommend researchers to consider reporting their results in terms of both denoised sequences (a proxy for haplotypes) and clusters formed (a proxy for species), and to avoid collapsing the sequences of the latter into a single representative. This will allow studies at the cluster (ideally equating species-level diversity) and at the intra-cluster level, and will ease additivity and comparability between studies.

show abstract

A Systematic Review of Sources of Variability and Uncertainty in eDNA Data for Environmental Monitoring

Cited by 55 publications

References 99 publications

To denoise or to cluster, that is not the question: optimizing pipelines for COI metabarcoding and metaphylogeography

To denoise or to cluster, that is not the question: optimizing pipelines for COI metabarcoding and metaphylogeography

Ready-to-use workflows for the implementation of genetic tools in conservation management

To denoise or to cluster? That is not the question. Optimizing pipelines for COI metabarcoding and metaphylogeography

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