DNA metabarcoding—Need for robust experimental designs to draw sound ecological conclusions
2019; Wiley; Volume: 28; Issue: 8 Linguagem: Inglês
10.1111/mec.15060
ISSN1365-294X
AutoresLucie Zinger, Aurélie Bonin, Inger Greve Alsos, Miklós Bálint, Holly M. Bik, Frédéric Boyer, Anthony A. Chariton, Simon Creer, Éric Coissac, Bruce E. Deagle, Marta De Barba, Ian A. Dickie, Alex J. Dumbrell, Gentile Francesco Ficetola, Noah Fierer, Luca Fumagalli, M. Thomas P. Gilbert, Simon Jarman, Ari Jumpponen, Håvard Kauserud, Ludovic Orlando, Johan Pansu, Jan Pawłowski, Leho Tedersoo, Philip Francis Thomsen, Eske Willerslev, Pierre Taberlet,
Tópico(s)Microbial Community Ecology and Physiology
ResumoMolecular EcologyVolume 28, Issue 8 p. 1857-1862 EDITORIALFree Access DNA metabarcoding—Need for robust experimental designs to draw sound ecological conclusions Lucie Zinger, Corresponding Author Lucie Zinger lucie@zinger.fr orcid.org/0000-0002-3400-5825 Institut de Biologie de l'ENS (IBENS), Département de biologie, Ecole normale supérieure, CNRS, INSERM, Université PSL, Paris, France Correspondence Lucie Zinger, Ecole Normale Supérieure, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), PSL Research University, Paris, France. Email: lucie@zinger.frSearch for more papers by this authorAurélie Bonin, Aurélie Bonin orcid.org/0000-0001-7800-8609 Laboratoire d'Ecologie Alpine (LECA), CNRS, Université Grenoble Alpes, Grenoble, FranceSearch for more papers by this authorInger G. Alsos, Inger G. Alsos orcid.org/0000-0002-8610-1085 UiT – The Arctic University of Norway, Tromsø Museum, Tromsø, NorwaySearch for more papers by this authorMiklós Bálint, Miklós Bálint orcid.org/0000-0003-0499-8536 Senckenberg Biodiversity and Climate Research Centre, Frankfurt am Main, Germany LOEWE Centre for Translational Biodiversity Genomics (LOEWE-TBG), Frankfurt, GermanySearch for more papers by this authorHolly Bik, Holly Bik orcid.org/0000-0002-4356-3837 Department of Nematology, University of California, Riverside, CaliforniaSearch for more papers by this authorFrédéric Boyer, Frédéric Boyer orcid.org/0000-0003-0021-9590 Laboratoire d'Ecologie Alpine (LECA), CNRS, Université Grenoble Alpes, Grenoble, FranceSearch for more papers by this authorAnthony A. Chariton, Anthony A. Chariton orcid.org/0000-0002-5809-3372 Department of Biological Sciences, Macquarie University, Sydney, New South Wales, AustraliaSearch for more papers by this authorSimon Creer, Simon Creer orcid.org/0000-0003-3124-3550 School of Natural Sciences, Bangor University, Gwynedd, UKSearch for more papers by this authorEric Coissac, Eric Coissac orcid.org/0000-0001-7507-6729 Laboratoire d'Ecologie Alpine (LECA), CNRS, Université Grenoble Alpes, Grenoble, FranceSearch for more papers by this authorBruce E. Deagle, Bruce E. Deagle orcid.org/0000-0001-7651-3687 Australian Antarctic Division, Kingston, Tasmania, AustraliaSearch for more papers by this authorMarta De Barba, Marta De Barba orcid.org/0000-0002-2979-3716 Laboratoire d'Ecologie Alpine (LECA), CNRS, Université Grenoble Alpes, Grenoble, FranceSearch for more papers by this authorIan A. Dickie, Ian A. Dickie orcid.org/0000-0002-2740-2128 School of Biological Sciences, BioProtection Research Centre, University of Canterbury, Christchurch, New ZealandSearch for more papers by this authorAlex J. Dumbrell, Alex J. Dumbrell orcid.org/0000-0001-6282-3043 School of Biological Sciences, University of Essex, Essex, UKSearch for more papers by this authorGentile Francesco Ficetola, Gentile Francesco Ficetola orcid.org/0000-0003-3414-5155 Laboratoire d'Ecologie Alpine (LECA), CNRS, Université Grenoble Alpes, Grenoble, France Department of Environmental Science and Policy, Università degli Studi di Milano, Milano, ItalySearch for more papers by this authorNoah Fierer, Noah Fierer orcid.org/0000-0002-6432-4261 Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, ColoradoSearch for more papers by this authorLuca Fumagalli, Luca Fumagalli orcid.org/0000-0002-6648-2570 Laboratory for Conservation Biology, Department of Ecology and Evolution, University of Lausanne, Lausanne, SwitzerlandSearch for more papers by this authorM. Thomas P. Gilbert, M. Thomas P. Gilbert orcid.org/0000-0002-5805-7195 Section for Evolutionary Genomics, Biological Institute, University of Copenhagen, Copenhagen, Denmark Norwegian University of Science and Technology, University Museum, Trondheim, NorwaySearch for more papers by this authorSimon Jarman, Simon Jarman orcid.org/0000-0002-0792-9686 Trace and Environmental DNA Laboratory, Department of Environment and Agriculture, Curtin University, Perth, Western Australia, Australia Environomics Future Science Platform, CSIRO National Collections and Marine Infrastructure, Crawley, Western Australia, AustraliaSearch for more papers by this authorAri Jumpponen, Ari Jumpponen orcid.org/0000-0002-6770-2563 Division of Biology, Kansas State University, Manhattan, KansasSearch for more papers by this authorHåvard Kauserud, Håvard Kauserud orcid.org/0000-0003-2780-6090 Section for Genetics and Evolutionary Biology (EVOGENE), University of Oslo, Oslo, NorwaySearch for more papers by this authorLudovic Orlando, Ludovic Orlando orcid.org/0000-0003-3936-1850 Laboratoire d'Anthropobiologie Moléculaire et d'Imagerie de Synthèse, CNRS UMR 5288, Université de Toulouse, Université Paul Sabatier, Toulouse, France Lundbeck Foundation GeoGenetics Center, University of Copenhagen, Copenhagen, DenmarkSearch for more papers by this authorJohan Pansu, Johan Pansu orcid.org/0000-0003-0256-0258 Department of Biological Sciences, Macquarie University, Sydney, New South Wales, Australia Station Biologique de Roscoff, CNRS UMR 7144, Sorbonne Université, Roscoff, France CSIRO Ocean & Atmosphere, Lucas Heights, New South Wales, AustraliaSearch for more papers by this authorJan Pawlowski, Jan Pawlowski orcid.org/0000-0003-2421-388X ID-Gene Ecodiagnostics, Geneva, Switzerland Department of Genetics and Evolution, University of Geneva, Geneva, SwitzerlandSearch for more papers by this authorLeho Tedersoo, Leho Tedersoo orcid.org/0000-0002-1635-1249 Institute of Ecology and Earth Sciences, University of Tartu, Tartu, EstoniaSearch for more papers by this authorPhilip Francis Thomsen, Philip Francis Thomsen orcid.org/0000-0002-9867-4366 Department of Bioscience, University of Aarhus, Aarhus C, DenmarkSearch for more papers by this authorEske Willerslev, Eske Willerslev orcid.org/0000-0002-7081-6748 Lundbeck Foundation GeoGenetics Center, University of Copenhagen, Copenhagen, Denmark Department of Zoology, University of Cambridge, Cambridge, UK Wellcome Trust Sanger Institute, Cambridge, UKSearch for more papers by this authorPierre Taberlet, Pierre Taberlet orcid.org/0000-0002-3554-5954 Laboratoire d'Ecologie Alpine (LECA), CNRS, Université Grenoble Alpes, Grenoble, France UiT – The Arctic University of Norway, Tromsø Museum, Tromsø, NorwaySearch for more papers by this author Lucie Zinger, Corresponding Author Lucie Zinger lucie@zinger.fr orcid.org/0000-0002-3400-5825 Institut de Biologie de l'ENS (IBENS), Département de biologie, Ecole normale supérieure, CNRS, INSERM, Université PSL, Paris, France Correspondence Lucie Zinger, Ecole Normale Supérieure, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), PSL Research University, Paris, France. Email: lucie@zinger.frSearch for more papers by this authorAurélie Bonin, Aurélie Bonin orcid.org/0000-0001-7800-8609 Laboratoire d'Ecologie Alpine (LECA), CNRS, Université Grenoble Alpes, Grenoble, FranceSearch for more papers by this authorInger G. Alsos, Inger G. Alsos orcid.org/0000-0002-8610-1085 UiT – The Arctic University of Norway, Tromsø Museum, Tromsø, NorwaySearch for more papers by this authorMiklós Bálint, Miklós Bálint orcid.org/0000-0003-0499-8536 Senckenberg Biodiversity and Climate Research Centre, Frankfurt am Main, Germany LOEWE Centre for Translational Biodiversity Genomics (LOEWE-TBG), Frankfurt, GermanySearch for more papers by this authorHolly Bik, Holly Bik orcid.org/0000-0002-4356-3837 Department of Nematology, University of California, Riverside, CaliforniaSearch for more papers by this authorFrédéric Boyer, Frédéric Boyer orcid.org/0000-0003-0021-9590 Laboratoire d'Ecologie Alpine (LECA), CNRS, Université Grenoble Alpes, Grenoble, FranceSearch for more papers by this authorAnthony A. Chariton, Anthony A. Chariton orcid.org/0000-0002-5809-3372 Department of Biological Sciences, Macquarie University, Sydney, New South Wales, AustraliaSearch for more papers by this authorSimon Creer, Simon Creer orcid.org/0000-0003-3124-3550 School of Natural Sciences, Bangor University, Gwynedd, UKSearch for more papers by this authorEric Coissac, Eric Coissac orcid.org/0000-0001-7507-6729 Laboratoire d'Ecologie Alpine (LECA), CNRS, Université Grenoble Alpes, Grenoble, FranceSearch for more papers by this authorBruce E. Deagle, Bruce E. Deagle orcid.org/0000-0001-7651-3687 Australian Antarctic Division, Kingston, Tasmania, AustraliaSearch for more papers by this authorMarta De Barba, Marta De Barba orcid.org/0000-0002-2979-3716 Laboratoire d'Ecologie Alpine (LECA), CNRS, Université Grenoble Alpes, Grenoble, FranceSearch for more papers by this authorIan A. Dickie, Ian A. Dickie orcid.org/0000-0002-2740-2128 School of Biological Sciences, BioProtection Research Centre, University of Canterbury, Christchurch, New ZealandSearch for more papers by this authorAlex J. Dumbrell, Alex J. Dumbrell orcid.org/0000-0001-6282-3043 School of Biological Sciences, University of Essex, Essex, UKSearch for more papers by this authorGentile Francesco Ficetola, Gentile Francesco Ficetola orcid.org/0000-0003-3414-5155 Laboratoire d'Ecologie Alpine (LECA), CNRS, Université Grenoble Alpes, Grenoble, France Department of Environmental Science and Policy, Università degli Studi di Milano, Milano, ItalySearch for more papers by this authorNoah Fierer, Noah Fierer orcid.org/0000-0002-6432-4261 Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, ColoradoSearch for more papers by this authorLuca Fumagalli, Luca Fumagalli orcid.org/0000-0002-6648-2570 Laboratory for Conservation Biology, Department of Ecology and Evolution, University of Lausanne, Lausanne, SwitzerlandSearch for more papers by this authorM. Thomas P. Gilbert, M. Thomas P. Gilbert orcid.org/0000-0002-5805-7195 Section for Evolutionary Genomics, Biological Institute, University of Copenhagen, Copenhagen, Denmark Norwegian University of Science and Technology, University Museum, Trondheim, NorwaySearch for more papers by this authorSimon Jarman, Simon Jarman orcid.org/0000-0002-0792-9686 Trace and Environmental DNA Laboratory, Department of Environment and Agriculture, Curtin University, Perth, Western Australia, Australia Environomics Future Science Platform, CSIRO National Collections and Marine Infrastructure, Crawley, Western Australia, AustraliaSearch for more papers by this authorAri Jumpponen, Ari Jumpponen orcid.org/0000-0002-6770-2563 Division of Biology, Kansas State University, Manhattan, KansasSearch for more papers by this authorHåvard Kauserud, Håvard Kauserud orcid.org/0000-0003-2780-6090 Section for Genetics and Evolutionary Biology (EVOGENE), University of Oslo, Oslo, NorwaySearch for more papers by this authorLudovic Orlando, Ludovic Orlando orcid.org/0000-0003-3936-1850 Laboratoire d'Anthropobiologie Moléculaire et d'Imagerie de Synthèse, CNRS UMR 5288, Université de Toulouse, Université Paul Sabatier, Toulouse, France Lundbeck Foundation GeoGenetics Center, University of Copenhagen, Copenhagen, DenmarkSearch for more papers by this authorJohan Pansu, Johan Pansu orcid.org/0000-0003-0256-0258 Department of Biological Sciences, Macquarie University, Sydney, New South Wales, Australia Station Biologique de Roscoff, CNRS UMR 7144, Sorbonne Université, Roscoff, France CSIRO Ocean & Atmosphere, Lucas Heights, New South Wales, AustraliaSearch for more papers by this authorJan Pawlowski, Jan Pawlowski orcid.org/0000-0003-2421-388X ID-Gene Ecodiagnostics, Geneva, Switzerland Department of Genetics and Evolution, University of Geneva, Geneva, SwitzerlandSearch for more papers by this authorLeho Tedersoo, Leho Tedersoo orcid.org/0000-0002-1635-1249 Institute of Ecology and Earth Sciences, University of Tartu, Tartu, EstoniaSearch for more papers by this authorPhilip Francis Thomsen, Philip Francis Thomsen orcid.org/0000-0002-9867-4366 Department of Bioscience, University of Aarhus, Aarhus C, DenmarkSearch for more papers by this authorEske Willerslev, Eske Willerslev orcid.org/0000-0002-7081-6748 Lundbeck Foundation GeoGenetics Center, University of Copenhagen, Copenhagen, Denmark Department of Zoology, University of Cambridge, Cambridge, UK Wellcome Trust Sanger Institute, Cambridge, UKSearch for more papers by this authorPierre Taberlet, Pierre Taberlet orcid.org/0000-0002-3554-5954 Laboratoire d'Ecologie Alpine (LECA), CNRS, Université Grenoble Alpes, Grenoble, France UiT – The Arctic University of Norway, Tromsø Museum, Tromsø, NorwaySearch for more papers by this author First published: 29 April 2019 https://doi.org/10.1111/mec.15060Citations: 189AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat DNA metabarcoding, especially when coupled with high-throughput DNA sequencing, is currently revolutionizing our capacity to assess biodiversity across a full range of taxa and habitats, from soil microbes (e.g., Thompson et al., 2017) to large marine fish (e.g., Thomsen et al., 2016), and from contemporary to tens of thousands year-old biological communities (e.g., Willerslev et al., 2003). The breadth of potential applications is immense and spans surveys on the diversity or diet of species native to specific ecosystems to bioindication (Pawlowski et al., 2018). The approach is also especially cost-effective and easy to implement, which makes DNA metabarcoding one of the tools of choice of the 21st century for fundamental research and the future of large-scale biodiversity monitoring programs (reviewed in Bohan et al., 2017; Creer et al., 2016; Taberlet, Bonin, Zinger, & Coissac, 2018; Thomsen & Willerslev, 2015). However, as is often the case with any emerging technology, we feel that the rise of DNA metabarcoding is occurring at a pace and in a manner that often loses sight of the challenges in producing high-quality and reproducible data (Baker, 2016). DNA metabarcoding is by essence a multidisciplinary approach building upon many complementary expertises, including field and theoretical knowledge, taxonomic expertise, molecular biology, bioinformatics, and computational statistics. Combining all these within single studies is necessary, not so much for producing and analyzing the data per se, but rather for minimizing and controlling the possible biases that can be introduced at any step of the experimental workflow—i.e., from the sampling to data analysis—and that can lead to spurious ecological conclusions (reviewed in Bálint et al., 2016; Nilsson et al., 2019; Dickie et al., 2018; Taberlet et al., 2018). Whether the starting material consists of DNA from bulk samples (community DNA) and/or from environmental DNA (eDNA), all DNA metabarcoding studies rely on a deceptively simple succession of core experimental steps: (a) sampling and preservation of the starting material, (b), DNA extraction, (c) PCR amplification of a taxonomically-informative genomic region, (d) high-throughput DNA sequencing of the amplicons, and (e) sequence analysis using bioinformatic pipelines. Despite this apparent simplicity, each step can potentially introduce its own sources of artifacts and biases (Figure 1). For example, the sampling design might not be effective for capturing the full taxonomic diversity or the ecological processes under study, an undesired bias for studies based on species detection. The availability of DNA in the samples is governed by its production rate, transport and persistence, processes which are all largely dependent on the targeted organisms, their biomass, and the ecosystem considered. A correct assessment of an ecological phenomenon based on DNA metabarcoding require not only implementation of standardized standardized, randomized and repeatable sampling designs and procedures (Dickie et al., 2018), but also consideration of DNA dynamics in the underlying matrix (i.e., in gut, faeces, water or soil matrices from tropical or boreal organisms/ecosystems; Barnes & Turner, 2016). Likewise, the community under study can be enriched on purpose or not with specific taxa depending on how the sample is collected (e.g., filter size for water samples, removal of roots or not for soils), how it is transported/preserved, and how DNA is extracted (differential extraction efficiencies). PCR amplification is also well known to be an important source of biases, that are now fully revealed with high-throughput DNA sequencing techniques. The preferential amplification of certain taxa over other ones due to inappropriate primers provides one such example of potential bias (Clarke, Soubrier, Weyrich, & Cooper, 2014; Deagle, Jarman, Coissac, Pompanon, & Taberlet, 2014). Primer biases can both skew abundance profiles and lead to false negatives. PCR amplification can produce false negatives too through the presence of e.g., PCR inhibitors, but also many false positives through the introduction of replication errors by the DNA polymerase or the formation of chimeric fragments (reviewed in Taberlet et al., 2018). False positives can also be introduced at any step of the experimental workflow through the presence of reagent contaminants (Salter et al., 2014), or through samples, extractions or PCR cross-contaminations. An even more insidious source of false positives pertains to the occurrence of “tag jumps”, sometimes referred to as “mistagging”, “tag-switching”, or “cross-talks” (Carlsen et al., 2012; Edgar, 2018; Esling, Lejzerowicz, & Pawlowski, 2015; Schnell, Bohmann, & Gilbert, 2015). PCR amplicons are indeed often tagged with unique short nucleotide sequences added on the 5’-end of the primers (i.e., “tags”), which allow pooling all PCRs within a single sequencing run and reducing sequencing costs. Each sequence obtained can then be bioinformatically assigned back to its sample of origin on the basis of its tags (Schnell et al., 2015). However, the procedures underlying the preparation of DNA libraries and/or the sequencing can introduce these “tag jumps”, when the tag assigned to one particular sample is in fact recombined to the sequences belonging to another sample (Taberlet et al., 2018). This introduces additional, non-negligible levels of sample cross-contaminations, which primarily involve the most abundant taxa and can have a disproportionate impact on samples with low DNA concentrations (Esling et al., 2015; Murray, Coghlan, & Bunce, 2015; Schnell et al., 2015). Similarly, the Illumina index located on the P5 sequencing adaptor can be subjected to “index jumps”, resulting in apparent cross-contaminations (Taberlet et al., 2018). This bias happens when several individual Illumina sequencing libraries are pooled and loaded on the same sequencing lane (Kircher, Sawyer, & Meyer, 2012). Finally, high-throughput DNA sequencing instruments have their own error rates (Schirmer et al., 2015). The above list of problems is clearly not exhaustive, and the interested reader will find more complete reviews elsewhere (e.g. Bálint et al., 2016; Nilsson et al., 2019; Taberlet et al., 2018). Still, it illustrates that any potential bias must be considered carefully when designing an experimental protocol and when interpreting the results. This is crucial to limit their impact on downstream analyses, and to ensure that the conclusion drawn from such data are authentic. Figure 1Open in figure viewerPowerPoint Summarized workflow of DNA metabarcoding and biases in the data production process, with the potential associated controls to assess data quality. Expectations on the local community, either from a priori knowledge on the site or organisms targeted, or obtained through e.g., visual census, specimen collection, or building of a local reference database, constitute a first assessment of the DNA metabarcoding experiment success. Pilot experiments are essential for optimizing the whole experimental design, from the sampling strategy (e.g., number of biological replicates) to the entire technical approach. Field, extraction, PCR, and tagging-system negative and positive controls should be sequenced along with biological samples. They all aim at identifying (i) potential contaminants that could be introduced at any experimental step, and (ii) potential experimental artifacts due to the DNA extraction, PCR, and sequencing steps. Field negative controls consists of extracting DNA from storage/extraction buffers brought to the field or used to clean sampling instruments. Tagging-system negative controls can only be implemented when amplicons are identified by a unique combination of tags attached to the 5′ end of each amplification primer, and where one or several tag combinations remain unused in the experimental design. In such conditions, tagging-system controls can be performed at the bioinformatics analysis step, by monitoring the number of sequences harbouring unexpected tag combinations. This number is actually a direct measurement of the tag-jump rate. “Index jumps” are more difficult to evaluate, and can be controlled either by indexing both library adapters (P5 and P7) or when the libraries sequenced together have identifiable sequences that could indicate their origin. The positive controls (constructed using either synthetic DNA with the primer target sequences on both sides, DNA extracted from a mock community, or known environmental samples), as well as prior expectations on the taxa that should occur in the system can be used to evaluate the effectiveness of the data production process, the impact of contaminants on the retrieved ecological signal and the adequacy of bioinformatics filtering procedures [Colour figure can be viewed at wileyonlinelibrary.com] There is now an increasingly diverse range of field, laboratory (e.g., Caporaso et al., 2011; Taberlet et al., 2018; Valentini et al., 2009) and bioinformatics (e.g., Boyer et al., 2016; Caporaso et al., 2010; Dumbrell, Ferguson, & Clark, 2016) procedures aiming at reducing the amount of both false negatives (i.e., due to partial sampling, extraction, amplification or sequencing bias) and false positives (i.e., due to contaminations, “tag/index jumps”, or PCR and sequencing errors) in DNA metabarcoding experiments. However, using these protocols does not necessarily guarantee that the problem of false positives or negatives is completely under control. These protocols must continuously be reconsidered, especially alongside the emergence of novel DNA sequencing technologies that provide new opportunities, but also new challenges. Additionally, each individual study and each genomic marker comes with its own specificities, and this often requires customization of the above protocols. The sequence clustering threshold to be used to form Molecular Operational Taxonomic Units relevant to the question addressed (e.g., removing intraspecific marker variability when the species level is desired) provides such an example, and will critically depend on both the marker specificities and PCR/sequencing error rates. Bioinformatics tools can further fail to exclude molecular artifacts when the filtering thresholds are relaxed, which inflates sample diversity estimates. Likewise, they can also generate false negatives, for example when a genuine metabarcode is falsely flagged as an error or chimera, or when it is assigned to an incorrect taxon due to incomplete or inappropriate reference databases (Alsos et al., 2018; Coissac, Riaz, & Puillandre, 2012). This can be especially problematic when the question investigated strongly relies on species detection. It is therefore crucial to include several types of experimental controls so as to facilitate the exclusion of spurious signal and support the reliability of the biological conclusions (Figure 1). Amongst these controls, conducting pilot experiments is particularly helpful to assess how appropriate the sampling design is (Dickie et al., 2018). We also recommend that both biological replicates (i.e., multiple independent samples) and technical replicates (i.e., multiple extractions/PCR of the same sample and/or extract) are included in the experimental workflow to disentangle the effect of both the biological and technical variances (Ficetola et al., 2015). These replications are necessary because both sampling and PCR can introduce biases in a stochastic manner, especially when the concentration of the target DNA is low. It is also essential to analyze a sufficient number of negative controls at the field sampling, DNA extraction, PCR, and sequencing steps, as well as positive controls consisting of mock communities, known DNA samples, or even synthetic sequences reflecting the attributes of the targeted products (Figure 1). All these controls must be sequenced along the biological samples, as they facilitate the detection of sporadic contaminations and tag or index jumps while helping adjusting filtering and clustering thresholds. Ultimately, they will be a token of the reliability of the whole data curation process (De Barba et al., 2014). We also encourage careful consideration of the bioinformatics workflow itself, since the filtering steps necessary to curate the data will critically depend on the experimental design and the ecological question under study. Typically, sequences of low abundance in a given sample may be genuine or artifacts deriving from PCR/sequencing errors or tag/index jumps. The retained filtering threshold for taxon presence is thus dependent on the underlying rates of artifacts, as well as on the sequencing depth. As the different experimental controls provide direct measurements of these artifacts, they will therefore allow better tuning of the filtering thresholds. All of these technical considerations should be precisely reported within publications together with relevant illustrations and statistics characterizing the workflow, as they are necessary to assess the relevance and quality of the data underpinning specific conclusions. A last, a most obvious example of control consists in assessing the plausibility of the taxonomic composition based on a priori knowledge of the system or taxa studied. Such knowledge can be derived from data obtained with complementary sensing approaches such as visual observations. In this case, building exhaustive local reference databases of the genomic marker used from local specimens will secure the taxonomic assignment step (e.g., Alsos et al., 2018). When local information is unavailable, typically when studying microorganisms, it remains possible to assess whether the community is composed of clades that are expected to occur in the system surveyed or not, as for example soils, sediments, and gut environments harbour highly different bacterial phyla (e.g., Thompson et al., 2017). As users, readers, referees or editors, we realize that the above mentioned issues remain too often overlooked. This problematic stance can lead to unsubstantiated claims and undermine scientific advances if not resolved. Inappropriate practices such as estimating species richness from fingerprint profiles (Bent et al., 2007), the absence of biological replicates (Prosser, 2010), or that of contaminant controls (Perez-Muñoz, Arrieta, Ramer-Tait, & Walter, 2017) have been repeatedly criticized in the field of microbial ecology, and in the latter case, they contribute to the rising debate about the existence or not of a womb microbiota. Ancient DNA research has also developed rigorous standards to tackle issues related to contamination, sequencing errors, and data reproducibility (Poinar & Cooper, 2000). We believe that the community of DNA metabarcoding users has now come of age and learnt from its past errors. At a time when more and more exhaustive guides of best practices on the subject are emerging (Knight et al., 2018; Pollock, Glendinning, Wisedchanwet, & Watson, 2018; Taberlet et al., 2018), and where DNA sequencing costs are rapidly decreasing, we should be always mindful of the adage “better safe than sorry”. This note does not mean to imply that the systematic use of the highest technical and analytical standards is reasonable nor the universal remedy for all the challenges associated with DNA metabarcoding. Rather, we strongly encourage researchers and end-users to adopt reflective decision-making when designing their experiment and to critically appraise their results, with the ultimate aim to prove the robustness and reproducibility of their conclusions. REFERENCES Alsos, I. G., Lammers, Y., Yoccoz, N. G., Jørgensen, T., Sjögren, P., Gielly, L., & Edwards, M. E. (2018). Plant DNA metabarcoding of lake sediments: How does it represent the contemporary vegetation. PLoS ONE, 13(4), e0195403. https://doi.org/10.1371/journal.pone.0195403 Baker, M. (2016). 1,500 scientists lift the lid on reproducibility. Nature News, 533(7604), 452. https://doi.org/10.1038/533452a Bálint, M., Bahram, M., Eren, A. M., Faust, K., Fuhrman, J. A., Lindahl, B., … Tedersoo, L. (2016). Millions of reads, thousands of taxa: Microbial community structure and associations analyzed via marker genes. FEMS Microbiology Reviews, 40(5), 686– 700. https://doi.org/10.1093/femsre/fuw017 Barnes, M. A., & Turner, C. R. (2016). The ecology of environmental DNA and implications for conservation genetics. Conservation Genetics, 17(1), 1– 17. https://doi.org/10.1007/s10592-015-0775-4 Bent, S. J., Pierson, J. D., Forney, L. J., Danovaro, R., Luna, G. M., Dell'Anno, A., & Pietrangeli, B. (2007). Measuring species richness based on microbial community fingerprints: The emperor has no clothes. Applied and Environmental Microbiology, 73(7), 2399. https://doi.org/10.1128/AEM.02383-06 Bohan, D. A., Vacher, C., Tamaddoni-Nezhad, A., Raybould, A., Dumbrell, A. J., & Woodward, G. (2017). Next-generation global biomonitoring: Large-scale, automated reconstruction of ecological networks. Trends in Ecology & Evolution, 32(7), 477– 487. https://doi.org/10.1016/j.tree.2017.03.001 Boyer, F., Mercier, C., Bonin, A., Le Bras, Y., Taberlet, P., & Coissac, E. (2016). obitools: A unix-inspired software package for DNA metabarcoding. Molecular Ecology Resources, 16(1), 176– 182. Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., … Knight, R. (2010). QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7(5), 335– 336. https://doi.org/10.1038/nmeth.f.303 Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Lozupone, C. A., Turnbaugh, P. J., … Knight, R. (2011). Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proceedings of the National Academy of Sciences of the United States of America, 108(Suppl 1), 4516– 4522. https://doi.org/10.1073/pnas.1000080107 Carlsen, T., Aas, A. B., Lindner, D., Vrålstad, T., Schumacher, T., & Kauserud, H. (2012). Don't make a mista(g)ke: Is tag switching an overlooked source of error in amplicon pyrosequencing studies? Fungal Ecology, 5(6), 747– 749. Clarke, L. J., Soubrier, J., Weyrich, L. S., & Cooper, A. (2014). Environmental metabarcodes for insects: In silico PCR reveals potential for taxonomic bias. Molecular Ecology Resources, 14(6), 1160– 1170. Coissac, E., Riaz, T., & Puillandre, N. (2012). Bioinformatic challenges for DNA metabarcoding of plants and animals. Molecular Ecology, 21, 1834– 1847. https://doi.org/10.1111/j.1365-294X.2012.05550.x Creer, S., Deiner, K., Frey, S., Porazinska, D., Taberlet, P., Thomas, W. K., … Bik, H. M. (2016). The ecologist's field guide to sequence-based identification of biodiversity. Methods in Ecology and Evolution, 7(9), 1008– 1018. https://doi.org/10.1111/2041-210X.12574 De Barba, M., Miquel, C., Boyer, F., Rioux, D., Coissac, E., & Taberlet, P. (2014). DNA metabarcoding multiplexing for omnivorous diet analysis and validation of data accuracy. Molecular Ecology Resources, 14(2), 306– 323. Deagle, B. E., Jarman, S. N., Coissac, E., Pompanon, F., & Taberlet, P. (2014). DNA metabarcoding and the cytochrome c oxidase subunit I marker: Not a perfect match. Biology Letters, 10(9), 20140562. https://doi.org/10.1098/rsbl.2014.0562 Dickie, I. A., Boyer, S., Buckley, H. L., Duncan, R. P., Gardner, P. P., Hogg, I. D., … Weaver, L. (2018). Towards robust and repeatable sampling methods in eDNA-based studies. Molecular Ecology Resources, 18(5), 940– 952. https://doi.org/10.1111/1755-0998.12907 Dumbrell, A. J., Ferguson, R. M. W., & Clark, D. R. (2016). Microbial community analysis by single-amplicon high-throughput next generation sequencing: Data analysis – from raw output to ecology. In T. J. McGenity, K. N. Timmis, & B. Nogales (Eds.), Hydrocarbon and lipid microbiology protocols. New York, NY: Humana Press. Edgar, R. (2018). UNCROSS2: Identification of cross-talk in 16S rRNA OTU tables. bioRxiv, 400762. https://doi.org/10.1101/400762 Esling, P., Lejzerowicz, F., & Pawlowski, J. (2015). Accurate multiplexing and filtering for high-throughput amplicon-sequencing. Nucleic Acids Research, 43(5), 2513– 2524. https://doi.org/10.1093/nar/gkv107 Ficetola, G. F., Pansu, J., Bonin, A., Coissac, E., Giguet-Covex, C., De Barba, M., … Taberlet, P. (2015). Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data. Molecular Ecology Resources, 15(3), 543– 556. https://doi.org/10.1111/1755-0998.12338 Kircher, M., Sawyer, S., & Meyer, M. (2012). Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Research, 40, https://doi.org/10.1093/nar/gkr771 Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., … Dorrestein, P. C. (2018). Best practices for analysing microbiomes. Nature Reviews. Microbiology, 16(7), 410– 422. https://doi.org/10.1038/s41579-018-0029-9 Murray, D. C., Coghlan, M. L., & Bunce, M. (2015). From benchtop to desktop: Important considerations when designing amplicon sequencing workflows. PLoS ONE, 10(4), e0124671. https://doi.org/10.1371/journal.pone.0124671 Nilsson, R. H., Anslan, S., Bahram, M., Wurzbacher, C., Baldrian, P., & Tedersoo, L. (2019). Mycobiome diversity: High-throughput sequencing and identification of fungi. Nature Reviews. Microbiology, 17(2), 95– 109. https://doi.org/10.1038/s41579-018-0116-y Pawlowski, J., Kelly-Quinn, M., Altermatt, F., Apothéloz-Perret-Gentil, L., Beja, P., Boggero, A., … Kahlert, M. (2018). The future of biotic indices in the ecogenomic era: Integrating (e)DNA metabarcoding in biological assessment of aquatic ecosystems. The Science of the Total Environment, 637–638, 1295– 1310. https://doi.org/10.1016/j.scitotenv.2018.05.002 Perez-Muñoz, M. E., Arrieta, M.-C., Ramer-Tait, A. E., & Walter, J. (2017). A critical assessment of the “sterile womb” and “in utero colonization” hypotheses: Implications for research on the pioneer infant microbiome. Microbiome, 5(1), 48. https://doi.org/10.1186/s40168-017-0268-4 Poinar, H. N., & Cooper, A. (2000). Ancient DNA: Do it right or not at all. Science, 5482, 1139. Pollock, J., Glendinning, L., Wisedchanwet, T., & Watson, M. (2018). The madness of microbiome: Attempting to find consensus “best practice” for 16S microbiome studies. Applied and Environmental Microbiology, 84(7), e02627– e2717. https://doi.org/10.1128/AEM.02627-17 Prosser, J. I. (2010). Replicate or lie. Environmental Microbiology, 12(7), 1806– 1810. https://doi.org/10.1111/j.1462-2920.2010.02201.x Salter, S. J., Cox, M. J., Turek, E. M., Calus, S. T., Cookson, W. O., Moffatt, M. F., … Walker, A. W. (2014). Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biology, 12, 87. https://doi.org/10.1186/s12915-014-0087-z Schirmer, M., Ijaz, U. Z., D'Amore, R., Hall, N., Sloan, W. T., & Quince, C. (2015). Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Research, 43(6), e37. https://doi.org/10.1093/nar/gku1341 Schnell, I. B., Bohmann, K., & Gilbert, M. T. P. (2015). Tag jumps illuminated – reducing sequence-to-sample misidentifications in metabarcoding studies. Molecular Ecology Resources, 15(6), 1289– 1303. https://doi.org/10.1111/1755-0998.12402 Taberlet, P., Bonin, A., Zinger, L., & Coissac, E. (2018). Environmental DNA for biodiversity research and monitoring. Oxford, UK: Oxford University Press. Thompson, L. R., Sanders, J. G., McDonald, D., Amir, A., Ladau, J., & Locey, K. J. … Earth Microbiome Project Consortium (2017). A communal catalogue reveals Earth's multiscale microbial diversity. Nature, 551(7681), 457– 463. Thomsen, P. F., Møller, P. R., Sigsgaard, E. E., Knudsen, S. W., Jørgensen, O. A., & Willerslev, E. (2016). Environmental DNA from seawater samples correlate with trawl catches of subarctic, deepwater fishes. PLoS ONE, 11(11), e0165252. https://doi.org/10.1371/journal.pone.0165252 Thomsen, P. F., & Willerslev, E. (2015). Environmental DNA as an emerging tool in conservation for monitoring past and present biodiversity. Biological Conservation, 183(C), 4– 18. Valentini, A., Miquel, C., Nawaz, M. A., Bellemain, E., Coissac, E., Pompanon, F., … Taberlet, P. (2009). New perspectives in diet analysis based on DNA barcoding and parallel pyrosequencing: The trnL approach. Molecular Ecology Resources, 9, 51– 60. Willerslev, E., Hansen, A. J., Binladen, J., Brand, T. B., Gilbert, M. T. P., Shapiro, B., … Cooper, A. (2003). Diverse plant and animal genetic records from Holocene and Pleistocene sediments. Science, 300, 791– 795. https://doi.org/10.1126/science.1084114 Citing Literature Volume28, Issue8April 2019Pages 1857-1862 FiguresReferencesRelatedInformation
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