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1,076 results for “Metabarcoding”
Figure 4 in Elasmobranch diversity across a remote coral reef atoll revealed through environmental DNA metabarcoding
Figure 4. Venn diagram showing the overlap of shark species detected in previous UVC and BRUVS surveys in the MPA and the eDNA samples from around Diego Garcia analysed in this study.
rDNA 18S V9 metabarcoding tables (Swarm) for Tara Oceans Expedition (2009-2013), including Tara Polar Circle Expedition (2013)
<p>Reads were grouped into OTUs using the following swarm-based pipeline: paired-end reads were merged with vsearch’s --fastq_mergepairs command (version 2.15.1, allowing for staggered reads; Rognes et al., 2016), and trimmed with cutadapt (version 3.0; Martin, 2011), keeping only reads containing both forward and reverse primers. After trimming, the expected error per read was estimated with vsearch’s command --fastq_filter and the option --eeout. Each sample was then de-replicated, i.e. strictly identical reads were merged, using vsearch’s command --derep_fulllength, and converted into fasta format. Clustering was performed at the sample level with swarm 3.0 using default parameters (Mahé et al., 2015). Prior to global clustering, individual fasta files (one per sample) were pooled and further dereplicated with vsearch. Files containing per-read expected error values were also dereplicated to retain only the lowest expected error for each unique sequence. Global clustering was performed with swarm (using the fastidious option). Cluster representative sequences were then searched for chimeras with vsearch’s command --uchime_denovo using default parameters (Edgar et al., 2011).</p> <p>Clustering results, expected error values, taxonomic assignments, and chimera detection results were used to build a “raw” occurrence table. Reads without primers, reads shorter than 32 nucleotides and reads with uncalled bases (“N”) were discarded. For a “filtered” occurrence table, non-chimeric sequences, sequences with an expected error per nucleotide below 0.0002, and clusters containing at least 2 reads were retained. Since primer trimming is not perfect, some sequences can still contain primer fragments or be excessively trimmed. These sub- or super-sequences were identified using vsearch and merged with their closest, most abundant perfectly trimmed sequence. Finally, occurrence patterns throughout our sample collection were used to further refine the occurrence table. Clusters that contain sub-clusters with only a single-nucleotide difference but with different ecological patterns (defined here as uncorrelated abundance values in at least 5% of the samples) were turned into distinct clusters (https://github.com/frederic-mahe/fred-metabarcoding-pipeline). On the other hand, clusters with similar sequences that had correlated abundance values in at least 95% of the samples, were merged using a re-implementation of lulu's method (Frøslev et al. 2017; https://github.com/frederic-mahe/mumu).</p> <p> </p>
Data to How to best detect threatened deadwood fungi – comparing metabarcoding and fruit body surveys
<p>This data set contains occurence data of threatened (red listed) fungi on deadwood objects in the National Park Bavarian Forest and the Žofínský prales National Nature Reserve to the the publication "How to best detect threatened deadwood fungi – comparing metabarcoding and fruit body surveys" by Rieker et al. (2024). This data is mainly uploaded to recreate analyses. </p> <p>Site: Information on the sampling site</p> <p>ObjectID: ID of the sampled deadwood object</p> <p>Campaign: Sampling year</p> <p>Decaystage: Decaystage of the deadwood object at sampling time (ranging between 1 and 4)</p> <p>Kingdom: Kingdom the eadwood object tree species belongs to</p> <p>Method: sampling method</p> <p>Species: threatend species name </p> <p>Presence: only contains the value "1" for present</p>
Fig. 2 in Elucidating nematode diversity and prevalence in moose across a wide latitudinal gradient using DNA metabarcoding
Fig. 2. Nematode component community in winter with a) the number of nematode taxa detected at each study area and b) the number of nematode taxa shared among study areas.
Fig. 3 in Elucidating nematode diversity and prevalence in moose across a wide latitudinal gradient using DNA metabarcoding
Fig. 3. Prevalence in each study area of the six most common nematodes detected. Whiskers indicate 95% confidence intervals.
Fig. 1. A in Elucidating nematode diversity and prevalence in moose across a wide latitudinal gradient using DNA metabarcoding
Fig. 1. A map showing the distribution of the five study areas across Norway ranging from 59.6◦N to 70.5◦N.
Prey diversity in the deep ocean: metabarcoding feeding ecology of the commercially important queen snapper in the US Caribbean
<p>These data contains the raw information for the article " Prey diversity in the deep ocean: metabarcoding feeding</p> <p>ecology of the commercially important queen snapper in the US Caribbean"</p>
Metabarcoding data package for QIIME2 workshop
<p>Raw data files and intermediate results of the feature tabulation and taxonomic classification of amplicon sequences using QIIME2. This data package is used during the Metabarcoding analyses using QIIME2 workshop, which is part of the 2021 Amsterdam Science Park Study Group summer school. The complete workflow is detailed in this tutorial: https://ejongepier.github.io/metabarcoding-qiime2-workshop/genintro.html.</p>
One‐locus‐several‐primers: A strategy to improve the taxonomic and haplotypic coverage in diet metabarcoding studies
In diet metabarcoding analyses, insufficient taxonomic coverage of PCR primer sets generates false negatives that may dramatically distort biodiversity estimates. In this paper, we investigated the taxonomic coverage and complementarity of three cytochrome c oxidase subunit I gene (COI) primer sets based on in silico analyses and we conducted an in vivo evaluation using fecal and spider web samples from different invertivores, environments, and geographic locations. Our results underline the lack of predictability of both the coverage and complementarity of individual primer sets: (a) sharp discrepancies exist observed between in silico and in vivo analyses (to the detriment of in silico analyses); (b) both coverage and complementarity depend greatly on the predator and on the taxonomic level at which preys are considered; (c) primer sets' complementarity is the greatest at fine taxonomic levels (molecular operational taxonomic units [MOTUs] and variants). We then formalized the "one‐locus‐several‐primer‐sets" (OLSP) strategy, that is, the use of several primer sets that target the same locus (here the first part of the COI gene) and the same group of taxa (here invertebrates). The proximal aim of the OLSP strategy is to minimize false negatives by increasing total coverage through multiple primer sets. We illustrate that the OLSP strategy is especially relevant from this perspective since distinct variants within the same MOTUs were not equally detected across all primer sets. Furthermore, the OLSP strategy produces largely overlapping and comparable sequences, which cannot be achieved when targeting different loci. This facilitates the use of haplotypic diversity information contained within metabarcoding datasets, for example, for phylogeography and finer analyses of prey–predator interactions.
Data from: Andriollo T., Michaux J.R., Ruedi M. (2021). Food for everyone: differential feeding habits of cryptic bat species inferred from DNA metabarcoding. Molecular Ecology
<p><strong>Supporting data for:</strong> Andriollo T., Michaux J.R., Ruedi M. (2021). Food for everyone: differential feeding habits of cryptic bat species inferred from DNA metabarcoding. Molecular Ecology. https://doi.org/ 10.1111/mec.16073</p> <p>Raw DNA sequences of prey of <em>Plecotus auritus</em>, <em>P. austriacus</em> and <em>P. macrobullaris</em> with complete sampling information and taxonomic assignations. Data separated by semicolums as follows:</p> <p>Sample name; Dataset; Colony; Bat species; Date; Season; Read numbers (Size); DNA sequence; Lowest taxonomic identification (ID_MOTU); Family; Order; Class; Is the sequence attributable to the diet or not (Diet)</p>
The hsp65 metabarcoding DNA sequence database for taxonomic allocations using the Mothur (Version 1.0.0)
<ul> <li>The <em>hsp65</em> gene codes for an Heat Shock Protein (Telenti et al., 1993) and is widespread in the Actinobacteria phylum. It is well suited for the species allocation of the Nocardia genus (Rodriguez-Nava et al., 2006).</li> <li>The <em>hsp65</em> database, named ACTIhsp65, was designed to apply the <em>hsp65</em>-metabarcoding analytical scheme published in Vautrin et al. (2021). It includes the full <em>hsp65</em> identifiers, GenBank accession numbers, complete taxonomic records (domain down to strain code) of about 401 nucleotide-long <em>hsp65</em> sequences of 1066 unique taxa belonging to 198 genera.</li> <li>Nucleotide sequences of <em>hsp65</em> (range: 165-565 nucleotides) were either retrieved from public repositories (GenBank) or made available by Veronica Rodriguez-Nava.Vautrin et al. (2021) described the PCR and high throughput Illumina Miseq DNA sequencing procedures used to produce <em>hsp65</em> sequences.</li> <li>ACTIhsp65 V1.0.0 (June 2018 release) is made available under the Creative Commons Attribution 4.0 International Licence. It can be used for the taxonomic allocations of <em>hsp65 </em>sequences down to the species.</li> </ul>
Optimal sequence similarity thresholds for clustering of molecular operational taxonomic units in DNA metabarcoding studies
<p><span>Clustering approaches are pivotal to handle the many sequence variants obtained in DNA metabarcoding datasets, therefore they have become a key step of metabarcoding analysis pipelines. Clustering often relies on a sequence similarity threshold to gather sequences in Molecular Operational Taxonomic Units (MOTUs), each of which ideally representing a homogeneous taxonomic entity, e.g. a species or a genus. However, the choice of the clustering threshold is rarely justified, and its impact on MOTU over-splitting or over-merging even less tested. Here, we evaluated clustering threshold values for several metabarcoding markers under different criteria: limitation of MOTU over-merging, limitation of MOTU over-splitting, and trade-off between over-merging and over-splitting. We extracted sequences from a public database for nine markers, ranging from generalist markers targeting Bacteria or Eukaryota, to more specific markers targeting a class or a subclass (e.g. Insecta, Oligochaeta). Based on the distributions of pairwise sequence similarities within species and within genera, and on the rates of over-splitting and over-merging across different clustering thresholds, we were able to propose threshold values minimizing the risk of over-splitting, that of over-merging, or offering a trade-off between the two risks. For generalist markers, high similarity thresholds (0.96-0.99) are generally appropriate, while more specific markers require lower values (0.85-0.96). These results do not support the use of a fixed clustering threshold. Instead, we advocate a careful examination of the most appropriate threshold based on the research objectives, the potential costs of over-splitting and over-merging, and the features of the studied markers.</span></p>
Data from: Metabarcoding of trap nests reveals differential impact of urbanization on cavity-nesting bee and wasp communities
<p><span>Urbanization is affecting arthropod communities worldwide, for example by changing the availability of food resources. However, the strength and direction of a community's response are species-specific and depend on the species' trophic level. Here, we investigated interacting species at different trophic levels in nests of cavity-nesting bees and wasps along two urbanization gradients in four German cities using trap nests. We analyzed bee and wasp diversity and their trophic interaction partners by metabarcoding the DNA of bee pollen and preyed arthropods found in wasp nests. We found that the pollen richness increased with increasing distance from city centers and at sites characterized by a high percentage of impervious and developed surfaces, while the richness of pollinators was unaffected by urbanization. In contrast, species richness of wasps, but not their arthropod prey, was highest at sites with low levels of urbanization. However, the community structure of wasp prey changed with urbanization at both local and regional scales. Throughout the study area, the community of wasps consisted of specialists, while bee species were generalists. Our results suggest that Hymenoptera and their food resources are negatively affected by increasing urbanization. However, to understand the distribution patterns of both, wasps and bees in urban settings other factors besides food availability should be considered.</span></p>
Using DNA metabarcoding to explore spatial variation in diet across European Hawfinch populations
<p><span>The investigation of diet in avian species is essential to an understanding of their ecology and local adaptations, as well as long-term conservation. This can be particularly challenging due to the wide distribution and high ecological plasticity of many bird species. Dietary richness and variation are under-studied in woodland bird species, due primarily to challenges in accurately identifying plant and invertebrate taxa consumed. Within Europe, Hawfinch (</span><span><em>Coccothraustes</em> <em>coccothraustes</em></span><span>) have shown variation in population trends, with moderate declines across central and eastern Europe, while western European populations have shown moderate increases. Ecological drivers behind this differing trend are still unknown; one possibility is differences in diet, yet little research has been conducted into Hawfinch diet in mainland Europe or elsewhere. This study aimed to present the first molecular dietary analysis of Hawfinch populations across two European countries. Faecal samples were collected between January and July of 2019 from Hawfinch caught at six artificial feed sites: two in Denmark and four in Germany. DNA was successfully extracted from 80 samples and plant Internal Transcribed Spacer 2 (ITS2) and invertebrate Cytochrome Oxidase Subunit 1 (COI) barcodes were amplified. A total of 35 plant and 37 invertebrate taxa were found across the 80 Hawfinch faecal samples, with plant and insect orders Fagales and Lepidoptera respectively the most frequently detected. Hawfinch dietary composition differed significantly between European countries, suggesting Hawfinch can make use of available food resources which are likely to differ spatially. Our study shows how DNA metabarcoding can be used to provide novel ecological information associated with under-studied bird species, thus providing essential information for future management and conservation of Hawfinch and their habitats. <br></span></p>
Otterly delicious: Spatiotemporal variation in the diet of a recovering population of Eurasian otters (Lutra lutra) revealed through DNA metabarcoding and morphological analysis of prey remains
<p>Eurasian otters are apex predators of freshwater ecosystems and a recovering species across much of their European range; investigating the dietary variation of this predator over time and space therefore provides opportunities to identify changes in freshwater trophic interactions and factors influencing the conservation of otter populations. Here we sampled faeces from 300 dead otters across England and Wales between 2007 and 2016, conducting both morphological analysis of prey remains and dietary DNA metabarcoding. Comparison of these methods showed that greater taxonomic resolution and breadth could be achieved using DNA metabarcoding but combining data from both methodologies gave the most comprehensive dietary description. All otter demographics exploited a broad range of taxa and variation likely reflected changes in prey distributions and availability across the landscape. This study provides novel insights into the trophic generalism and adaptability of otters across Britain, which is likely to have aided their recent population recovery, and may increase their resilience to future environmental changes.</p>
Data from: Using environmental DNA metabarcoding to monitor fish communities in small rivers and large brooks: Insights on the spatial scale of information
<p><span>Monitoring fish communities is central to the evaluation of ecological health of rivers. Not only presence/absence of species is important to assess, but also the species composition of local fish assemblages is a crucial parameter. Lotic fish communities are traditionally monitored via electrofishing, characterized by a known limited efficiency and high survey costs. The use of environmental DNA-based analyses could serve as a non-destructive alternative, but this approach requires further insights in practical sampling schemes incorporating transport and dilution of the eDNA fragments; as well as optimization of molecular detection in terms of predictive power and quality assurance. By introducing fifteen species known to occur in Belgian waters via a controlled cage experiment, we aim to extend the knowledge on streamreach of eDNA in small rivers and large brooks, as laid out in the European Water Framework Directive's water typology. Introducing fish communities in two transects of a species poor river characterized by contrasting river discharge rates, we found strong and significant correlations between the eDNA relative abundances and the relative biomass per species in the cage community. Despite a decreasing correlation over distance, the underlying community composition remained stable over a distance of 300 m up to 1 km downstream of the cages, depending on the river discharge rate. Such decrease in similarity between relative source biomass and the corresponding eDNA-based community profile with increasing distance downstream from the source, can partly be attributed to variation in species-specific eDNA persistence. Our findings offer novel insights on eDNA behaviour and characterization of riverine fish communities. We conclude that water sampled from a relatively small river offers an adequate snapshot of the total fish community composition occurring within an upstream perimeter ranging between 300 and 1000 meters. The potential application for other river systems is discussed in this study. </span></p>
Multi‐marker DNA metabarcoding reveals spatial and sexual variation in the diet of a scarce woodland bird
<p>Avian diet can be affected by site‐specific variables, such as habitat, as well as intrinsic factors such as sex. This can lead to dietary niche separation, which reduces competition between individuals, as well as impacting how well avian species can adapt to environmental variation. Estimating dietary niche separation is challenging, due largely to difficulties in accurately identifying food taxa consumed. Consequently, there is limited knowledge of the diets of woodland bird species, many of which are undergoing serious population declines. Here, we show the effectiveness of multi‐marker fecal metabarcoding to provide in‐depth dietary analysis of a declining passerine in the UK, the Hawfinch (Coccothraustes coccothraustes). We collected fecal samples from (n = 262) UK Hawfinches prior to, and during, the breeding seasons in 2016–2019. We detected 49 and 90 plant and invertebrate taxa, respectively. We found Hawfinch diet varied spatially, as well as between sexes, indicating broad dietary plasticity and the ability of Hawfinches to utilize multiple resources within their foraging environments.</p>
DNA metabarcoding captures different macroinvertebrate biodiversity than morphological identification approaches across a continental scale
<p><span>DNA-based aquatic biomonitoring methods show promise to provide rapid, standardized, and efficient biodiversity assessment to supplement and in some cases replace current morphology-based approaches that are often less efficient and can produce inconsistent results. Despite this potential, broad-scale adoption of DNA-based approaches by end-users remains limited, and studies on how these two approaches differ in detecting aquatic biodiversity across large spatial scales are lacking. Here, we present a comparison of DNA metabarcoding and morphological identification, leveraging national-scale, open-source, ecological datasets from the National Ecological Observatory Network (NEON). Across 24 wadeable streams in North America with 179 paired sample comparisons, we found that DNA metabarcoding detected twice as many unique taxa than morphological identification overall. The two approaches showed poor congruence in detecting the same taxa, averaging 59%, 35%, and 23% of shared taxa detected at the order, family, and genus levels, respectively. Importantly, the two approaches detected different proportions of indicator taxa like %EPT and %Chironomidae. DNA metabarcoding detected far fewer Chironomid and Trichopteran taxa than morphological identification, but more Ephemeropteran and Plecopteran taxa, a result likely due to primer choice. Overall, our results showed that DNA metabarcoding and morphological identification detected different benthic macroinvertebrate communities. Despite these differences, our results relating watershed-scale and local-scale abiotic variables to invertebrate community structure from both methods produced similar results. This suggests that DNA and morphological approaches are both suitable for use in basic and applied ecological research. Further refinement of DNA metabarcoding protocols, primers, and reference libraries–as well as more standardized, large-scale comparative studies–may improve our understanding of taxonomic agreement and data linkages between DNA metabarcoding and morphological approaches. </span></p>
Malaise-trap metabarcoding dataset from temperate-zone forest Oregon, USA
<p>DNA-based biodiversity surveys involve collecting physical samples from survey sites and assaying the contents in the laboratory to detect species via their diagnostic DNA sequences. DNA-based surveys are increasingly being adopted for biodiversity monitoring and decision-making. The most commonly employed method is metabarcoding, which combines PCR with high-throughput DNA sequencing to amplify and then read `DNA barcode' sequences. This process generates count data indicating the number of times each DNA barcode was read. However, DNA-based data are noisy and error-prone, with several sources of variation. In this paper, we present a unifying modelling framework for DNA-based survey data, <strong>eDNAPlus</strong>, for the first time simultaneously allowing for key sources of variation, error and noise in the data-generating process. As we discuss, metabarcoding data alone cannot be used to estimate the species-specific amount of DNA present, or DNA concentration, at surveyed sites. Instead, we estimate changes in DNA biomass within species, across sites, and link those changes to environmental covariates, while accounting for between-species and between-sites correlation. Inference is performed using MCMC, where we employ Gibbs or Metropolis-Hastings updates with Laplace approximations. We further implement a re-parameterisation scheme, appropriate for crossed-effects models, leading to improved mixing, and an adaptive approach for updating latent variables, which reduces computation time. We discuss study design and present theoretical and simulation results to guide decisions on replication at different survey stages and on the use of quality control methods. Finally, we demonstrate the new framework on a dataset of Malaise-trap samples. Specifically, we quantify the effects of elevation and distance-to-road on each species, infer species correlations, and produce maps identifying areas of high biodiversity and species DNA biomass, which can be used to rank areas by conservation value. We also estimate the level of noise between sites and within sample replicates, and the probabilities of error at the PCR stage, which are found to be close to zero for most species considered, validating the employed laboratory processing.</p>
MalaMix dataset: contextual and metabarcoding data
<p>1. INTRODUCTION</p> <p><em>MalaMix </em>is a compiled metabarcoding dataset composed of 451 marine samples collected from a range of depths - from the surface (3m) to deep waters (as far down as 4800m). This dataset covers three ocean layers: the epi- (0-200m – including DCM), meso- (200-1000m) and bathypelagic (1000-4000m). <em>MalaMix</em> combines samples obtained during two oceanographic expeditions with similar sampling strategies: i) the Malaspina-2010 global expedition that produced 263 samples collected between December 2010 and July 2011 from 120 stations distributed along the tropical and subtropical portions (latitudes between 35° N and 40° S) of the Pacific, Atlantic and Indian oceans; and ii) the <em>HotMix</em> trans-Mediterranean cruise that produced 188 samples collected between April and May 2014 in 29 stations distributed along the whole Mediterranean Sea (from -5° W to 33° E) and the adjacent Northeast Atlantic Ocean.</p> <p> </p> <p><em>MalaMix</em> comprises:</p> <ul> <li>a 16S-V4V5 rRNA gene ASV table (MalaMix_16S.csv);</li> <li>an 18S-V4 rRNA gene ASV table (MalaMix_18S.csv);</li> <li>two tables of contextual metadata (MalaMix_EnvData_16S and MalaMix_EnvData_18S) including 6 standardized environmental parameters (temperature [°C], salinity, fluorescence, PO<sub>4</sub><sup>3− </sup>[µmol L<sup>-1</sup>], NO<sub>3</sub><sup>− </sup>[µmol L<sup>-1</sup>], and SiO<sub>2 </sub>[µmol L<sup>-1</sup>]) as well as species taxonomic and phylogenetic diversity metrics</li> <li>a table (MalaMix_FCdata.csv) with flow cytometry microbial counts [cell mL<sup>-1</sup>] and bacterial activity measurements [pmol Leu L<sup>-1</sup> h<sup>-1</sup>];</li> <li>a README file (README_Metadata.csv) describing the meaning and units of each variable column in the metadata tables.</li> </ul> <p> </p> <p>The raw DNA sequences are publicly available at the European Nucleotide Archive (<a href="https://www.ebi.ac.uk/ena">https://www.ebi.ac.uk/ena</a>) under accession numbers PRJEB23913 [18S rRNA genes] & PRJEB25224 [16S rRNA genes] for the Malaspina surface dataset; PRJEB23771 [18S rRNA genes] & PRJEB45015 [16S rRNA genes] for the Malaspina vertical profiles; PRJEB45011 [16S rRNA genes] & PRJEB45014 [18S rRNA genes] for the Malaspina deep sea dataset; and PRJEB44683 [18S rRNA genes] & PRJEB44474 [16S rRNA genes] for the HotMix expedition.</p> <p> </p> <p>Further methodological details are available here: <a href="https://www.biorxiv.org/content/10.1101/2023.01.13.523743v1">https://www.biorxiv.org/content/10.1101/2023.01.13.523743v1</a></p> <p> </p> <p>2. FUTURE FORMAT CHANGES</p> <p>No major changes are expected for the main general format of the database.</p> <p>3. ACKNOWLEDGMENTS</p> <p>The current dataset was generated with funds from the projects INTERACTOMICS (CTM2015-69936-P, MINECO, Spain), MicroEcoSystems (240904, RCN, Norway), MINIME (PID2019-105775RB-I00, AEI, Spain), and PID2021-125469NB-C31 (AEI, Spain), as well as DOREMI (CTM2012-34294) and HOTMIX (CTM2011-30010-C02-01 and CTM2011-30010-C02-02) of the Spanish Ministry of Economy and Innovation, co-financed with FEDER funds.</p> <p>4. COPYRIGHT NOTICE<br> <br> This database is provided “as is” and without any warranty of any kind, of openly available for non-commerical purposes (CC BY-NC). <strong>CC BY-NC</strong> means that users can make use of the work (including copying, distributing, adapting and building upon the work), but only for noncommercial purposes and as long as attribution is given to the creator: <a href="https://oabooks-toolkit.org/lifecycle/article/4012101-choosing-a-license">https://oabooks-toolkit.org/lifecycle/article/4012101-choosing-a-license</a></p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.