Evaluating bioinformatics pipelines for population‐level inference using environmental DNA

Macé, Bastien; Hocdé, Régis; Marques, Virginie; Guérin, Pierre‐Édouard; Valentini, Alice; Arnal, Véronique; Pellissier, Loïc; Manel, Stéphanie

doi:10.1002/edn3.269

Cited by 14 publications

(21 citation statements)

References 79 publications

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“…Metabarcoding data consist of counts of unique DNA sequences detected by a DNA sequencing platform (e.g., for a given sample we might observe three copies of sequence A, 1001 copies of sequence B, etc.). Important bioinformatic decisions bear on how multiple sequences are combined to represent species or genera or higher taxonomic groups (Macé et al, 2022), but the resulting data themselves are counts of reads associated with particular taxa for each sample. Many uses of these data in an ecological setting share an (often implicit) assumption that the reads emerging from DNA sequences are an accurate depiction of the sample composition prior to amplification (e.g., Laporte et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Toward quantitative metabarcoding

Shelton

Gold

Jensen

et al. 2022

Ecology

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Amplicon-sequence data from environmental DNA (eDNA) and microbiome studies provide important information for ecology, conservation, management, and health. At present, amplicon-sequencing studies-known also as metabarcoding studies, in which the primary data consist of targeted, amplified fragments of DNA sequenced from many taxa in a mixture-struggle to link genetic observations to the underlying biology in a quantitative way, but many applications require quantitative information about the taxa or systems under scrutiny. As metabarcoding studies proliferate in ecology, it becomes more important to develop ways to make them quantitative to ensure that their conclusions are adequately supported. Here we link previously disparate sets of techniques for making such data quantitative, showing that the underlying polymerase chain reaction mechanism explains the observed patterns of amplicon data in a general way. By modeling the process through which amplicon-sequence data arise, rather than transforming the data post hoc, we show how to estimate the starting DNA proportions from a mixture of many taxa. We illustrate how to calibrate the model using mock communities and apply the approach to simulated data and a series of empirical examples. Our approach opens the door to improve the use of metabarcoding data in a wide range of applications in ecology, public health, and related fields.

show abstract

Section: Introductionmentioning

confidence: 99%

Toward quantitative metabarcoding

Shelton

Gold

Jensen

et al. 2022

Ecology

View full text Add to dashboard Cite

show abstract

“…for a given sample we might observe 3 copies of sequence A, 1001 copies of sequence B, etc.). Important bioinformatic decisions bear on how multiple sequences are combined to represent species or genera or higher taxonomic groups (Macé et al 2022), but the resulting data themselves are straightforward counts of reads associated with particular taxa for each sample. Many uses of these data in an ecological setting share an (often implicit) assumption that the reads emerging from DNA sequencers are an accurate depiction of the sample composition prior to amplification (e.g., Laporte et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Toward quantitative metabarcoding

Shelton

Gold

Jensen

et al. 2022

Preprint

View full text Add to dashboard Cite

Amplicon-sequence data from environmental DNA (eDNA) and microbiome studies provides important information for ecology, conservation, management, and health. At present, amplicon-sequencing studies – known also as metabarcoding studies, in which the primary data consist of targeted, amplified fragments of DNA sequenced from many taxa in a mixture – struggle to link genetic observations to underlying biology in a quantitative way, but many applications require quantitative information about the taxa or systems under scrutiny. As metabarcoding studies proliferate in ecology following decades of microbial and microbiome work using similar techniques, it becomes more important to develop ways ot make them quantitative to ensure that their conclusions are adequately supported. Here we link previously disparate sets of techniques for making such data quantitative, showing that the underlying PCR mechanism explains observed patterns of amplicon data in a general way. By modeling the process through which amplicon-sequence data arises, rather than transforming the data post-hoc, we show how to estimate the starting DNA proportions from a mixture of many taxa. We illustrate how to calibrate the model using mock communities and apply the approach to simulated data and a series of empirical examples. Our approach opens the door to improve the use of metabarcoding data in a wide range of applications in ecology, public health, and related fields.

show abstract

“…The emergent field of eDNA‐based population genetics could have a great potential to study fish population connectivity across depths (Adams et al, 2019). For instance, eDNA has been recently used to investigate diversity within populations of Striped red mullet ( Mullus surmuletus ; e.g., Macé et al, 2022), or Blackfoot pāua ( Haliotis iris ; Adams et al, 2022), and to successfully estimate population genetic differentiation like for the whale shark (Sigsgaard et al, 2016). Only through standard, sustained and multi‐objectives observations at all depths we could get the insights needed to understand the fundamental ecological processes that govern the dynamics of marine ecosystems, and design depth‐specific strategies to effectively protect them in the future.…”

Section: Discussionmentioning

confidence: 99%