Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification

Mathon, Laëtitia; Valentini, Alice; Guérin, Pierre‐Édouard; Normandeau, Éric; Noël, Cyril; Lionnet, Clément; Boulanger, Emilie; Thuiller, Wilfried; Bernatchez, Louis; Mouillot, David; Déjean, Tony; Manel, Stéphanie

doi:10.1111/1755-0998.13430

Cited by 56 publications

(58 citation statements)

References 93 publications

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“…Dully et al (2021a; showed that MLbased pipelines are sufficiently robust even for rarefied samples. Other authors reached similar conclusions (Cordier et al, 2019a;Apothéloz-Perret-Gentil et al, 2021;Frühe et al, 2021;He et al, 2021) and Mathon et al (2021) reviewed literature on "eDNA and Machine Learning." In ML analysis, data is first pre-processed with common bioinformatic pipelines as for general metabarcoding analysis (Mathon et al, 2021) and subsequently processed through an automated DNA-Barcode Classifier (taxonomy assignment).…”

Section: Artificial Intelligence For Environmental Dna Analyses and Integration With Imaging Datasupporting

confidence: 60%

Framing Cutting-Edge Integrative Deep-Sea Biodiversity Monitoring via Environmental DNA and Optoacoustic Augmented Infrastructures

et al. 2022

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Deep-sea ecosystems are reservoirs of biodiversity that are largely unexplored, but their exploration and biodiscovery are becoming a reality thanks to biotechnological advances (e.g., omics technologies) and their integration in an expanding network of marine infrastructures for the exploration of the seas, such as cabled observatories. While still in its infancy, the application of environmental DNA (eDNA) metabarcoding approaches is revolutionizing marine biodiversity monitoring capability. Indeed, the analysis of eDNA in conjunction with the collection of multidisciplinary optoacoustic and environmental data, can provide a more comprehensive monitoring of deep-sea biodiversity. Here, we describe the potential for acquiring eDNA as a core component for the expanding ecological monitoring capabilities through cabled observatories and their docked Internet Operated Vehicles (IOVs), such as crawlers. Furthermore, we provide a critical overview of four areas of development: (i) Integrating eDNA with optoacoustic imaging; (ii) Development of eDNA repositories and cross-linking with other biodiversity databases; (iii) Artificial Intelligence for eDNA analyses and integration with imaging data; and (iv) Benefits of eDNA augmented observatories for the conservation and sustainable management of deep-sea biodiversity. Finally, we discuss the technical limitations and recommendations for future eDNA monitoring of the deep-sea. It is hoped that this review will frame the future direction of an exciting journey of biodiscovery in remote and yet vulnerable areas of our planet, with the overall aim to understand deep-sea biodiversity and hence manage and protect vital marine resources.

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Section: Artificial Intelligence For Environmental Dna Analyses and Integration With Imaging Datasupporting

confidence: 60%

Framing Cutting-Edge Integrative Deep-Sea Biodiversity Monitoring via Environmental DNA and Optoacoustic Augmented Infrastructures

et al. 2022

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“…Raw forward and reverse reads were trimmed, merged, and classified using the Barque v1.7.0, an eDNA metabarcoding pipeline (Mathon et al, 2021;www.github.com/enorm andea u/barque).…”

Section: Bioinformatics Analyses and Data Cleaningmentioning

confidence: 99%

Proper environmental DNA metabarcoding data transformation reveals temporal stability of fish communities in a dendritic river system

Laporte

Reny-Nolin

Chouinard³

et al. 2021

Environmental DNA

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“…The trimmed paired‐end reads were then merged using flash v. 1.2.11 (options: − t = 1, − z , − m = 30, − M = 280). The merged reads were kept if they possessed both forward and reverse amplicon primer sequences using the 03_split_amplicons.sh script from Barque 1.5.2 (https://github.com/enormandeau/barque), an eDNA analysis pipeline that has been shown to be more accurate and efficient alternative to some highly used pipelines for analysing fish eDNA metabarcoding data (Mathon et al, 2021). Chimeras were removed with vsearch v. 2.5.1 (options: ‐‐uchime_denovo and ‐‐nonchimeras).…”

Section: Methodsmentioning

confidence: 99%

eDNA metabarcoding as a means to assess distribution of subterranean fish communities: Iranian blind cave fishes as a case study

et al. 2021

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One of the most important steps in conservation of the subterranean life forms is to decipher their distribution and ecology, which is challenging using traditional approaches. Development of an environmental DNA (eDNA) assay provides an efficient means for discovering and monitoring subterranean life forms. In this study, the distribution of three Iranian blind cave fish species (blind Iran cave barb Garra typhlops, blind Lorestan cave barb Garra lorestanensis, and blind cave loach Eidinemacheilus smithi) was assessed using 12S rRNA eDNA metabarcoding performed using MiFish‐U PCR primers and preliminary species distribution modeling (SDM) using bioclimatic data. The majority of sampling localities with positive detection of cave barb eDNA fall within suitable habitats in the Zagros Mountains of Iran. Our results revealed that Lorestan and Iran cave barbs have differential distribution patterns, with some extent of habitat overlap in the vicinity of the originally discovered cave barb locality. According to the observed distribution patterns, the blind Lorestan cave barb and cave loach are mostly distributed in habitats close to the Sezar River (Dez River drainage, Iran), and the blind Iran cave barb is distributed towards the west and probably in a few springs in the Karkheh River drainage. Our data support the previously proposed distribution pattern for the cave barbs, in which the species show partial niche separation and reproductive isolation, with the Lorestan cave barb being a water flow‐dependent species and the Iran cave barb being a generalist species preferring variable flow rates. We showed eDNA metabarcoding to be a useful approach for ecological surveys of subterranean fish biodiversity with implications for conservation.

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Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification

Cited by 56 publications

References 93 publications

Framing Cutting-Edge Integrative Deep-Sea Biodiversity Monitoring via Environmental DNA and Optoacoustic Augmented Infrastructures

Framing Cutting-Edge Integrative Deep-Sea Biodiversity Monitoring via Environmental DNA and Optoacoustic Augmented Infrastructures

Proper environmental DNA metabarcoding data transformation reveals temporal stability of fish communities in a dendritic river system

eDNA metabarcoding as a means to assess distribution of subterranean fish communities: Iranian blind cave fishes as a case study

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