collaborateurs

Tristan Cordier

Post-doctorant chez Molecular Systematics & Environmental Genomics

  • T: +41 22 379 30 77
  • office 4078b (Sciences III)
  • SLIM: a flexible web application for the reproducible processing of environmental DNA metabarcoding data. BMC Bioinformatics 2019 Feb;20(1):88. 10.1186/s12859-019-2663-2. 10.1186/s12859-019-2663-2.

    résumé

    High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) has become a routine tool for biodiversity survey and ecological studies. By including sample-specific tags in the primers prior PCR amplification, it is possible to multiplex hundreds of samples in a single sequencing run. The analysis of millions of sequences spread into hundreds to thousands of samples prompts for efficient, automated yet flexible analysis pipelines. Various algorithms and software have been developed to perform one or multiple processing steps, such as paired-end reads assembly, chimera filtering, Operational Taxonomic Unit (OTU) clustering and taxonomic assignment. Some of these software are now well established and widely used by scientists as part of their workflow. Wrappers that are capable to process metabarcoding data from raw sequencing data to annotated OTU-to-sample matrix were also developed to facilitate the analysis for non-specialist users. Yet, most of them require basic bioinformatic or command-line knowledge, which can limit the accessibility to such integrative toolkits. Furthermore, for flexibility reasons, these tools have adopted a step-by-step approach, which can prevent an easy automation of the workflow, and hence hamper the analysis reproducibility.

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  • Embracing Environmental Genomics and Machine Learning for Routine Biomonitoring. Trends Microbiol. 2018 Nov;():. S0966-842X(18)30238-5. 10.1016/j.tim.2018.10.012.

    résumé

    Genomics is fast becoming a routine tool in medical diagnostics and cutting-edge biotechnologies. Yet, its use for environmental biomonitoring is still considered a futuristic ideal. Until now, environmental genomics was mainly used as a replacement of the burdensome morphological identification, to screen known morphologically distinguishable bioindicator taxa. While prokaryotic and eukaryotic microbial diversity is of key importance in ecosystem functioning, its implementation in biomonitoring programs is still largely unappreciated, mainly because of difficulties in identifying microbes and limited knowledge of their ecological functions. Here, we argue that the combination of massive environmental genomics microbial data with machine learning algorithms can be extremely powerful for biomonitoring programs and pave the way to fill important gaps in our understanding of microbial ecology.

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  • A Comparison of Different Ciliate Metabarcode Genes as Bioindicators for Environmental Impact Assessments of Salmon Aquaculture. J. Eukaryot. Microbiol. 2018 Jul;():. 10.1111/jeu.12670.

    résumé

    Ciliates are powerful indicators for monitoring the impact of aquaculture and other industrial activities in the marine environment. Here we tested the efficiency of four different genetic markers (V4 and V9 regions of the SSU rRNA gene, D1 and D2 regions of the LSU rRNA gene, obtained from environmental (e)DNA and environmental (e)RNA) of benthic ciliate communities for environmental monitoring. We obtained these genetic metabarcodes from sediment samples collected along a transect extending from below salmon cages towards the open sea. These data were compared to benchmark data from traditional macrofauna surveys of the same samples. In beta-diversity analyses of ciliate community structures, the V4 and V9 markers had a higher resolution power for sampling sites with different degrees of organic enrichment compared to the D1 and D2 markers. The eDNA and eRNA V4 markers had a higher discriminatory power than the V9 markers. However, results obtained with the eDNA V9 marker corroborated better with the traditional macrofauna monitoring. This allows for a more direct comparison of ciliate metabarcoding with the traditional monitoring. We conclude that the ciliate eDNA V9 marker is the best choice for implementation in routine monitoring programs in marine aquaculture. This article is protected by copyright. All rights reserved.

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  • Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring. Mol Ecol Resour 2018 Jul;():. 10.1111/1755-0998.12926.

    résumé

    Biodiversity monitoring is the standard for environmental impact assessment of anthropogenic activities. Several recent studies showed that high-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) could overcome many limitations of the traditional morphotaxonomy-based bioassessment. Recently, we demonstrated that supervised machine learning (SML) can be used to predict accurate biotic indices values from eDNA metabarcoding data, regardless of the taxonomic affiliation of the sequences. However, it is unknown to which extent the accuracy of such models depends on taxonomic resolution of molecular markers or how SML compares with metabarcoding approaches targeting well-established bioindicator species. In this study, we address these issues by training predictive models upon five different ribosomal bacterial and eukaryotic markers and measuring their performance to assess the environmental impact of marine aquaculture on independent datasets. Our results show that all tested markers are yielding accurate predictive models, and that they all outperform the assessment relying solely on taxonomically assigned sequences. Remarkably, we did not find any significant difference in the performance of the models built using universal eukaryotic or prokaryotic markers. Using any molecular marker with a taxonomic range broad enough to comprise different potential bioindicator taxa, SML approach can overcome the limits of taxonomy-based eDNA bioassessment. This article is protected by copyright. All rights reserved.

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  • The future of biotic indices in the ecogenomic era: Integrating (e)DNA metabarcoding in biological assessment of aquatic ecosystems. Sci. Total Environ. 2018 May;637-638():1295-1310. S0048-9697(18)31632-2. 10.1016/j.scitotenv.2018.05.002.

    résumé

    The bioassessment of aquatic ecosystems is currently based on various biotic indices that use the occurrence and/or abundance of selected taxonomic groups to define ecological status. These conventional indices have some limitations, often related to difficulties in morphological identification of bioindicator taxa. Recent development of DNA barcoding and metabarcoding could potentially alleviate some of these limitations, by using DNA sequences instead of morphology to identify organisms and to characterize a given ecosystem. In this paper, we review the structure of conventional biotic indices, and we present the results of pilot metabarcoding studies using environmental DNA to infer biotic indices. We discuss the main advantages and pitfalls of metabarcoding approaches to assess parameters such as richness, abundance, taxonomic composition and species ecological values, to be used for calculation of biotic indices. We present some future developments to fully exploit the potential of metabarcoding data and improve the accuracy and precision of their analysis. We also propose some recommendations for the future integration of DNA metabarcoding to routine biomonitoring programs.

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  • Development and implementation of eco-genomic tools for aquatic ecosystem biomonitoring: the SYNAQUA French-Swiss program. Environ Sci Pollut Res Int 2018 May;():. 10.1007/s11356-018-2172-2. 10.1007/s11356-018-2172-2.

    résumé

    The effectiveness of environmental protection measures is based on the early identification and diagnosis of anthropogenic pressures. Similarly, restoration actions require precise monitoring of changes in the ecological quality of ecosystems, in order to highlight their effectiveness. Monitoring the ecological quality relies on bioindicators, which are organisms revealing the pressures exerted on the environment through the composition of their communities. Their implementation, based on the morphological identification of species, is expensive because it requires time and experts in taxonomy. Recent genomic tools should provide access to reliable and high-throughput environmental monitoring by directly inferring the composition of bioindicators' communities from their DNA (metabarcoding). The French-Swiss program SYNAQUA (INTERREG France-Switzerland 2017-2019) proposes to use and validate the tools of environmental genomic for biomonitoring and aims ultimately at their implementation in the regulatory bio-surveillance. SYNAQUA will test the metabarcoding approach focusing on two bioindicators, diatoms, and aquatic oligochaetes, which are used in freshwater biomonitoring in France and Switzerland. To go towards the renewal of current biomonitoring practices, SYNAQUA will (1) bring together different actors: scientists, environmental managers, consulting firms, and biotechnological companies, (2) apply this approach on a large scale to demonstrate its relevance, (3) propose robust and reliable tools, and (4) raise public awareness and train the various actors likely to use these new tools. Biomonitoring approaches based on such environmental genomic tools should address the European need for reliable, higher-throughput monitoring to improve the protection of aquatic environments under multiple pressures, guide their restoration, and follow their evolution.

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  • Environmental DNA metabarcoding of benthic bacterial communities indicates the benthic footprint of salmon aquaculture. Mar. Pollut. Bull. 2018 Feb;127():139-149. S0025-326X(17)31022-6. 10.1016/j.marpolbul.2017.11.065.

    résumé

    We evaluated benthic bacterial communities as bioindicators in environmental impact assessments of salmon aquaculture, a rapidly growing sector of seafood industry. Sediment samples (n=72) were collected from below salmon cages towards distant reference sites. Bacterial community profiles inferred from DNA metabarcodes were compared to reference data from standard macrofauna biomonitoring surveys of the same samples. Deltaproteobacteria were predominant in immediate vicinity of the salmon cages. Along the transect, significant shifts in bacterial community structures were observed with Gammaproteobacteria dominating the less-impacted sites. Alpha- and beta-diversity measures of bacterial communities correlated significantly with macrofauna diversity metrics and with five ecological status indices. Benthic bacterial communities mirror the reaction of macrofauna bioindicators to environmental disturbances caused by salmon farming. The implementation of bacterial eDNA metabarcoding in future Strategic Framework Directives is an alternative cost-effective high-throughput biomonitoring solution, providing a basis for management strategies in a matter of days rather than months.

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  • Predicting the ecological quality status of marine environments from eDNA metabarcoding data using supervised machine learning. Environ. Sci. Technol. 2017 Jun;():. 10.1021/acs.est.7b01518.

    résumé

    Monitoring biodiversity is essential to assess the impacts of increasing anthropogenic activities in marine environments. Traditionally, marine biomonitoring involves the sorting and morphological identification of benthic macro-invertebrates, which is time-consuming and taxonomic-expertise demanding. High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) represents a promising alternative for benthic monitoring. However, an important fraction of eDNA sequences remains unassigned or belong to taxa of unknown ecology, which prevent their use for assessing the ecological quality status. Here, we show that supervised machine learning (SML) can be used to build robust predictive models for benthic monitoring, regardless of the taxonomic assignment of eDNA sequences. We tested three SML approaches to assess the environmental impact of marine aquaculture using benthic foraminifera eDNA, a group of unicellular eukaryotes known to be good bioindicators, as features to infer macro-invertebrates based biotic indices. We found similar ecological status as obtained from macro-invertebrates inventories. We argue that SML approaches could overcome and even bypass the cost and time-demanding morpho-taxonomic approaches in future biomonitoring.

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  • Benthic monitoring of salmon farms in Norway using foraminiferal metabarcoding AEI 8:371-386 (2016) - doi:10.3354/aei00182

    résumé

    The rapid growth of the salmon industry necessitates the development of fast and accurate tools to assess its environmental impact. Macrobenthic monitoring is commonly used to measure the impact of organic enrichment associated with salmon farm activities. However, classical benthic monitoring can hardly answer the rapidly growing demand because the morphological identification of macro-invertebrates is time-consuming, expensive and requires taxonomic expertise. Environmental DNA (eDNA) metabarcoding of meiofauna-sized organisms, such as Foraminifera, was proposed to overcome the drawbacks of macrofauna-based benthic monitoring. Here, we tested the application of foraminiferal metabarcoding to benthic monitoring of salmon farms in Norway. We analysed 140 samples of eDNA and environmental RNA (eRNA) extracted from surface sediment samples collected at 4 salmon farming sites in Norway. We sequenced the variable region 37f of the 18S rRNA gene specific to Foraminifera. We compared our data to the results of macrofaunal surveys of the same sites and tested the congruence between various diversity indices inferred from metabarcoding and morphological data. The results of our study confirm the usefulness of Foraminifera as bioindicators of organic enrichment associated with salmon farming. The foraminiferal diversity increased with the distance to fish cages, and metabarcoding provides an assessment of the ecological quality comparable to the morphological analyses. The foraminiferal metabarcoding approach appears to be a promising alternative to classical benthic monitoring, providing a solution to the morpho-taxonomic bottleneck of macrofaunal surveys.

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