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1,076 results for “Metabarcoding”
Supplementary material 1 from: Matsuoka S, Sugiyama Y, Sato H, Katano I, Harada K, Doi H (2019) Spatial structure of fungal DNA assemblages revealed with eDNA metabarcoding in a forest river network in western Japan. Metabarcoding and Metagenomics 3: e36335. https://doi.org/10.3897/mbmg.3.36335
: Data type: multimedia
Supplementary material 2 from: Matsuoka S, Sugiyama Y, Sato H, Katano I, Harada K, Doi H (2019) Spatial structure of fungal DNA assemblages revealed with eDNA metabarcoding in a forest river network in western Japan. Metabarcoding and Metagenomics 3: e36335. https://doi.org/10.3897/mbmg.3.36335
: Data type: molecular data
Supplementary material 2 from: Nobile AB, Freitas-Souza D, Ruiz-Ruano FJ, Nobile MLMO, Costa GO, de Lima FP, Camacho JPM, Foresti F, Oliveira C (2019) DNA metabarcoding of Neotropical ichthyoplankton: Enabling high accuracy with lower cost. Metabarcoding and Metagenomics 3: e35060. https://doi.org/10.3897/mbmg.3.35060
: Data type: NGS quality control
Supplementary material 1 from: Nobile AB, Freitas-Souza D, Ruiz-Ruano FJ, Nobile MLMO, Costa GO, de Lima FP, Camacho JPM, Foresti F, Oliveira C (2019) DNA metabarcoding of Neotropical ichthyoplankton: Enabling high accuracy with lower cost. Metabarcoding and Metagenomics 3: e35060. https://doi.org/10.3897/mbmg.3.35060
: Data type: Bioinformatic protocol
Supplementary material 2 from: Ahmed M, Back MA, Prior T, Karssen G, Lawson R, Adams I, Sapp M (2019) Metabarcoding of soil nematodes: the importance of taxonomic coverage and availability of reference sequences in choosing suitable marker(s). Metabarcoding and Metagenomics 3: e36408. https://doi.org/10.3897/mbmg.3.36408
: Data type: source code
Supplementary material 1 from: Ahmed M, Back MA, Prior T, Karssen G, Lawson R, Adams I, Sapp M (2019) Metabarcoding of soil nematodes: the importance of taxonomic coverage and availability of reference sequences in choosing suitable marker(s). Metabarcoding and Metagenomics 3: e36408. https://doi.org/10.3897/mbmg.3.36408
: Data type: species data
Supplementary material 4 from: Bowser ML, Burr SJ, Davis I, Dubois GD, Graham EE, Moan JE, Swenson SW (2019) A test of metabarcoding for Early Detection and Rapid Response monitoring for non-native forest pest beetles (Coleoptera). Research Ideas and Outcomes 5: e48536. https://doi.org/10.3897/rio.5.e48536
Sequences of amplicon sequence variants in FASTA format.
Figure 3 from: Bowser ML, Burr SJ, Davis I, Dubois GD, Graham EE, Moan JE, Swenson SW (2019) A test of metabarcoding for Early Detection and Rapid Response monitoring for non-native forest pest beetles (Coleoptera). Research Ideas and Outcomes 5: e48536. https://doi.org/10.3897/rio.5.e48536
Figure 3 Phylogenetic tree of HTS sequences generated using qiime phylogeny align-to-tree-mafft-fasttree, accepting default parameters. The graphic was rendered using the Interactive Tree Of Life (Letunic and Bork 2019). An interactive version of this tree is available at https://itol.embl.de/tree/1641591522462921555104654. Colors hightlight major taxonomic groups.
Supplementary material 3 from: Bowser ML, Burr SJ, Davis I, Dubois GD, Graham EE, Moan JE, Swenson SW (2019) A test of metabarcoding for Early Detection and Rapid Response monitoring for non-native forest pest beetles (Coleoptera). Research Ideas and Outcomes 5: e48536. https://doi.org/10.3897/rio.5.e48536
Amplicon sequence variant table in standard text format
Figure 2 from: Bowser ML, Burr SJ, Davis I, Dubois GD, Graham EE, Moan JE, Swenson SW (2019) A test of metabarcoding for Early Detection and Rapid Response monitoring for non-native forest pest beetles (Coleoptera). Research Ideas and Outcomes 5: e48536. https://doi.org/10.3897/rio.5.e48536
Figure 2 Comparison of identifications based on morphological and HTS methods. Columns are samples and rows are identifications. White: non-detections. Blue: morphological detections. Red: HTS detections. Purple: detections by both methods.
Supplementary material 2 from: Bowser ML, Burr SJ, Davis I, Dubois GD, Graham EE, Moan JE, Swenson SW (2019) A test of metabarcoding for Early Detection and Rapid Response monitoring for non-native forest pest beetles (Coleoptera). Research Ideas and Outcomes 5: e48536. https://doi.org/10.3897/rio.5.e48536
RTL Genomics Data Analysis Methodology
Data of "Accurate estimation of volumetric diet through metabarcoding in a top predator with confusing effect of blocking primers."
Open the record for dataset details and reuse information.
Linked collectors and determiners for: Metabarcoding Data from an Inventory of Freshwater Invertebrates from the Miller Creek Watershed, Kenai Peninsula, Alaska, USA.
Natural history specimen data linked to collectors and determiners held within, "Metabarcoding Data from an Inventory of Freshwater Invertebrates from the Miller Creek Watershed, Kenai Peninsula, Alaska, USA". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9d7baaac-57db-4852-9993-7f0e7f15635b">https://bionomia.net/dataset/9d7baaac-57db-4852-9993-7f0e7f15635b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9d7baaac-57db-4852-9993-7f0e7f15635b">https://gbif.org/dataset/9d7baaac-57db-4852-9993-7f0e7f15635b</a>. Formatted as a Frictionless Data package.
Data from: Assessing strengths and weaknesses of DNA metabarcoding based macroinvertebrate identification for routine stream monitoring
1) DNA metabarcoding holds great promise for the assessment of macroinvertebrates in stream ecosystems. However, few large-scale studies have compared the performance of DNA metabarcoding with that of routine morphological identification. 2) We performed metabarcoding using four primer sets on macroinvertebrate samples from 18 stream sites across Finland. The samples were collected in 2013 and identified based on morphology as part of a Finnish stream monitoring program. Specimens were morphologically classified, following standardised protocols, to the lowest taxonomic level for which identification was feasible in the routine national monitoring. 3) DNA metabarcoding identified more than twice the number of taxa than the morphology-based protocol, and also yielded a higher taxonomic resolution. For each sample, we detected more taxa by metabarcoding than by the morphological method, and all four primer sets exhibited comparably good performance. Sequence read abundance and the number of specimens per taxon (a proxy for biomass) were significantly correlated in each sample, although the adjusted R2 were low. With a few exceptions, the ecological status assessment metrics calculated from morphological and DNA metabarcoding datasets were similar. Given the recent reduction in sequencing costs, metabarcoding is currently approximately as expensive as morphology-based identification. 4) Using samples obtained in the field, we demonstrated that DNA metabarcoding can achieve comparable assessment results to current protocols relying on morphological identification. Thus, metabarcoding represents a feasible and reliable method to identify macroinvertebrates in stream bioassessment, and offers powerful advantage over morphological identification in providing identification for taxonomic groups that are unfeasible to identify in routine protocols. To unlock the full potential of DNA metabarcoding for ecosystem assessment, however, it will be necessary to address key problems with current laboratory protocols and reference databases.
Data from: Minimizing polymerase biases in metabarcoding
DNA metabarcoding is an increasingly popular method to characterize and quantify biodiversity in environmental samples. Metabarcoding approaches simultaneously amplify a short, variable genomic region, or "barcode", from a broad taxonomic group via the polymerase chain reaction (PCR), using universal primers that anneal to flanking conserved regions. Results of these experiments are reported as occurrence data, which provide a list of taxa amplified from the sample, or relative abundance data, which measure the relative contribution of each taxon to the overall composition of amplified product. The accuracy of both occurrence and relative abundance estimates can be affected by a variety of biological and technical biases. For example, taxa with larger biomass may be better represented in environmental samples than those with smaller biomass. Here, we explore how polymerase choice, a potential source of technical bias, might influence results in metabarcoding experiments. We compared potential biases of six commercially available polymerases using a combination of mixtures of amplifiable synthetic sequences and real sedimentary DNA extracts. We find that polymerase choice can affect both occurrence and relative abundance estimates, and that the main source of this bias appears to be polymerase preference for sequences with specific GC contents. We further recommend an experimental approach for metabarcoding based on results of our synthetic experiments.
Gillnets and stomach content metabarcoding - sharks and rays
<p>Gillnets are the world's most common net-based fishing gear, comprising walls of light mesh designed to entangle fish. Gillnets are often retrieved with holes in the netting, which means some animals escape or are depredated unseen, but with some mortality. To effectively manage fisheries around the world, information is required on not only the harvested and discarded mortalities, but also problematic interactions and mortalities caused by the fishing gear and especially those involving protected species. This study sought to assess a novel method for determining such interactions by sampling five adjacent pieces of netting around each of ten holes in two bather protection polyethylene gillnets for environmental DNA or 'eDNA'. Here we show that eDNA correctly identified all previously entangled-and-landed species. Also, eDNA from three uncaptured taxa were recorded: bull shark, <i>Carcharhinus leucas</i>, white shark, <i>Carcharodon carcharias</i> and dolphins (Delphindae), illustrating the potential to reveal previously cryptic gillnet interactions. We propose that as scientific methods evolve and autonomous real-time DNA surveillance becomes routine, eDNA testing of fishing gears and vessels could provide a novel, complementary fishery-monitoring tool.</p>
Supplementary material 3 from: Macher T-H, Schütz R, Arle J, Beermann AJ, Koschorreck J, Leese F (2021) Beyond fish eDNA metabarcoding: Field replicates disproportionately improve the detection of stream associated vertebrate species. Metabarcoding and Metagenomics 5: e66557. https://doi.org/10.3897/mbmg.5.66557
Table S3. Filtered taXon table
Supplementary material 2 from: Macher T-H, Schütz R, Arle J, Beermann AJ, Koschorreck J, Leese F (2021) Beyond fish eDNA metabarcoding: Field replicates disproportionately improve the detection of stream associated vertebrate species. Metabarcoding and Metagenomics 5: e66557. https://doi.org/10.3897/mbmg.5.66557
Table S2. Raw taXon table as created with TaxonTableTools
Supplementary material 1 from: Macher T-H, Schütz R, Arle J, Beermann AJ, Koschorreck J, Leese F (2021) Beyond fish eDNA metabarcoding: Field replicates disproportionately improve the detection of stream associated vertebrate species. Metabarcoding and Metagenomics 5: e66557. https://doi.org/10.3897/mbmg.5.66557
Table S1. BLAST taxonomy table
Supplementary material 5 from: Macher T-H, Schütz R, Arle J, Beermann AJ, Koschorreck J, Leese F (2021) Beyond fish eDNA metabarcoding: Field replicates disproportionately improve the detection of stream associated vertebrate species. Metabarcoding and Metagenomics 5: e66557. https://doi.org/10.3897/mbmg.5.66557
Figure S2
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