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1,076 results for “Metabarcoding”
Supplementary material 15 from: Lee T, Alemseged Y, Mitchell A (2018) Dropping Hints: Estimating the diets of livestock in rangelands using DNA metabarcoding of faeces. Metabarcoding and Metagenomics 2: e22467. https://doi.org/10.3897/mbmg.2.22467
Species level taxa (BOLD Data), at 3 minimum read depth. Underlined taxa were changed based on the distribution of taxa in the study zone
Supplementary material 10 from: Lee T, Alemseged Y, Mitchell A (2018) Dropping Hints: Estimating the diets of livestock in rangelands using DNA metabarcoding of faeces. Metabarcoding and Metagenomics 2: e22467. https://doi.org/10.3897/mbmg.2.22467
Tests for normailty and equality of variance to establish whether conducting t-tests on the Dorper and Merino speccies and family level diversity data is appropriate.
Supplementary material 6 from: Lee T, Alemseged Y, Mitchell A (2018) Dropping Hints: Estimating the diets of livestock in rangelands using DNA metabarcoding of faeces. Metabarcoding and Metagenomics 2: e22467. https://doi.org/10.3897/mbmg.2.22467
Species level taxa (BOLD Data). Underlined taxa were changed based on the distribution of taxa in the study zone ('*' indicates that the column contains no taxa).
Supplementary material 4 from: Ushio M, Murakami H, Masuda R, Sado T, Miya M, Sakurai S, Yamanaka H, Minamoto T, Kondoh M (2018) Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcoding and Metagenomics 2: e23297. https://doi.org/10.3897/mbmg.2.23297
The relationship between MiSeq sequence reads and copy numbers of standard DNAs for 52 samples. Blue line indicates the linear regression between sequence reads and copy numbers. The regression lines are used to convert the MiSeq reads into the calculated copy numbers. Numbers in a grey region indicate sampling date. Note that regression slopes are different amongst samples, i.e. the number of sequence reads generated per eDNA copy is different amongst samples.
Supplementary material 11 from: Lee T, Alemseged Y, Mitchell A (2018) Dropping Hints: Estimating the diets of livestock in rangelands using DNA metabarcoding of faeces. Metabarcoding and Metagenomics 2: e22467. https://doi.org/10.3897/mbmg.2.22467
Shapiro-Wilk and Levene's tests exploring the appropriateness of the data for use in ANOVA or Kruskal-Wallis tests.
Supplementary material 5 from: Ushio M, Murakami H, Masuda R, Sado T, Miya M, Sakurai S, Yamanaka H, Minamoto T, Kondoh M (2018) Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcoding and Metagenomics 2: e23297. https://doi.org/10.3897/mbmg.2.23297
The relationship between regression residuals and copy numbers of standard DNAs for 52 samples. Dashed line indicates zero residuals.
Supplementary material 3 from: Li Y, Evans NT, Renshaw MA, Jerde CL, Olds BP, Shogren AJ, Deiner K, Lodge DM, Lamberti GA, Pfrender ME (2018) Estimating fish alpha- and beta-diversity along a small stream with environmental DNA metabarcoding. Metabarcoding and Metagenomics 2: e24262. https://doi.org/10.3897/mbmg.2.24262
The longitudinal distance (upper triangular) and β- diversity (lower triangular) between sampling locations along Eagle Creek. :
Supplementary material 4 from: Li Y, Evans NT, Renshaw MA, Jerde CL, Olds BP, Shogren AJ, Deiner K, Lodge DM, Lamberti GA, Pfrender ME (2018) Estimating fish alpha- and beta-diversity along a small stream with environmental DNA metabarcoding. Metabarcoding and Metagenomics 2: e24262. https://doi.org/10.3897/mbmg.2.24262
Mantel r and p-values for all the pairwise comparisons between single marker, three markers and longitudinal distance. :
Supplementary material 5 from: Li Y, Evans NT, Renshaw MA, Jerde CL, Olds BP, Shogren AJ, Deiner K, Lodge DM, Lamberti GA, Pfrender ME (2018) Estimating fish alpha- and beta-diversity along a small stream with environmental DNA metabarcoding. Metabarcoding and Metagenomics 2: e24262. https://doi.org/10.3897/mbmg.2.24262
The correlation between environmental variables and β-diversity and longitudinal distance using Mantel test :
Supplementary data to the manuscript "Morphology and metabarcoding! A test with stream diatoms from Mexico highlights the complementarity of methods", submitted to Freshwater Science by D. Mora, N. Abarca, S. Proft, J. Grau, N. Enke, J. Carmona, O. Skibbe, R. Jahn and J. Zimmermann.
<p>Demultiplexed fastq files for 18 samples, accompanied by a table including sample numbers in the manuscript and sample numbers in the fastq files.</p>
Supplementary material 2 from: Cahoon AB, Huffman AG, Krager MM, Crowell RM (2018) A meta-barcoding census of freshwater planktonic protists in Appalachia – Natural Tunnel State Park, Virginia, USA. Metabarcoding and Metagenomics 2: e26939. https://doi.org/10.3897/mbmg.2.26939
Figure 2. Rarefaction analysis estimates demonstrate that family and genus collections were approaching saturation :
DNA metabarcoding authentication of Ayurvedic herbal products on the European market raises concerns of quality and fidelity
<p>Ayurveda is one of the oldest systems of medicine in the world, but the growing commercial interest in Ayurveda based products has increased the incentive for adulteration and substitution within this herbal market. Fraudulent practices such as the use of undeclared fillers and use of other species of inferior quality is driven both by the increased as well as insufficient supply capacity of especially wild plant species. Developing novel strategies to exhaustively assess and monitor both the quality of raw materials and final marketed herbal products is a challenge in herbal pharmacovigilance. Seventy-nine Ayurvedic herbal products sold as tablets, capsules, powders and extracts were randomly purchased via e-commerce and pharmacies across Europe, and DNA metabarcoding was used to assess the ability of this method to authenticate these products. Our analysis reveals that only two out of 12 single ingredient products contained only one species as labelled, eight out of 27 multiple ingredient products contained none of the species listed on the label, and the remaining 19 products contained 1 to 5 of the species listed on the label along with many other species not specified on the label. The fidelity for single ingredient products was 67 %, the overall ingredient fidelity for multi ingredient products was 21 %, and for all products 24 %. The low level of fidelity raises concerns about the reliability of the products, and detection of threatened species raises further concerns about illegal plant trade. The study highlights the necessity for quality control of the marketed herbal products and shows that DNA metabarcoding is an effective analytical approach to authenticate complex multi ingredient herbal products. However, effort needs to be done to standardize the protocols for DNA metabarcoding before this approach can be implemented as routine analytical approaches for plant identification, and approved for use in regulated procedures.</p>
Bait set used in: Enhancing DNA metabarcoding performance and applicability with bait capture enrichment and DNA from conservative ethanol
<p>Bait set used in the paper "Enhancing DNA metabarcoding performance and applicability with bait capture enrichment and DNA from conservative ethanol"</p> <p>This bait set were designed essentially from COI sequences of invertebrate species present in French streams. 16S, AP6, NAD1, CytB, NAD4 and NAD5 baits were also designed for few species. Order affiliation of each bait is integrated in its name.</p> <p>Baits were designed using BaitFisher software (Mayer et al, 2016).</p> <p> </p>
Validation of a multi-primer assay for monitoring coral diversity using eDNA metabarcoding
<p>Raw data output from Scleractinia eDNA metabarcoding survey completed using multi-assay approach.</p>
Supplementary material 1 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
This file contains the specimen data from the original trap samples, morphological identifications, molecular identifications, and resulting occurrence data.
Data for: Detection and diversity of Phytophthora species from declining Quercus suber stands using both DNA metabarcoding and soil baiting techniques
<p>This dataset on Zenodo accompanies the manuscript Salvatore <em>et al.</em> (2024), Detection and diversity of <em>Phytophthora</em> species from declining <em>Quercus suber</em> stands using both DNA metabarcoding and soil baiting techniques.</p> <p>There are two files here on Zenodo:</p> <ul> <li><code>metadata.tsv</code> - plain text table as tab-separated variables</li> <li><code>raw_data.tar.gz</code> - compressed archive of 56 paired raw FASTQ files</li> </ul> <p>This represents a subset of one complete Illumina Nano MiSeq plate run at the James Hutton Institute also containing a small number of unrelated samples using the same protocol.</p> <p>To repeat the analysis described in the paper, first install THAPBI PICT. See <a href="https://github.com/peterjc/thapbi-pict/">https://github.com/peterjc/thapbi-pict/ </a>for instructions. At the time of the paper, v1.0.16 was the current release.</p> <p>Next, decompress the raw data into a folder of paired gzipped FASTQ files. There is no need to decompress those:</p> <pre><code> $ tar -zxvf raw_data.tar.gz</code><br><code> $ ls -1 raw_data/</code></pre> <p>If you wish, verify the checksums to confirm the data integrity:</p> <pre><code> $ cd raw_data/ $ md5sum -c MD5SUM.txt</code><br><code> $ cd ..</code></pre> <p>Setup output directories:</p> <pre><code> $ mkdir -p intermediate/ summary/</code></pre> <p>Run the THAPBI PICT pipeline:</p> <pre><code> $ thapbi_pict pipeline -m 1s3g -f 0 -a 15 -i raw_data/ \</code><br><code> -s intermediate/ -o summary/sardinia \</code><br><code> -t metadata.tsv -u -x 8 -c 4,5,3,2,7,6</code></pre> <p>The options here are as follows:</p> <ul> <li><code>-m</code> - use the 1s3g classifier (see methods)</li> <li><code>-f</code> - set to zero to disable the fractional abundance threshold</li> <li><code>-a</code> - set a lower absolute abundance threshold</li> <li><code>-i</code> - location of the input raw data</li> <li><code>-s</code> - optional location to store intermediate files</li> <li><code>-o</code> - output stem for reports</li> <li><code>-t</code> - filename for tab-separated-variable metadata</li> <li><code>-u</code> - show unsequenced samples defined in the metadata</li> <li><code>-x</code> - which metadata column contains Illumina FASTQ filename stems</li> <li><code>-c</code> - which metadata columns to include in the report.</li> </ul> <p>This leaves the <code>-d</code> option with the default provided ITS1 database. We are NOT taking advantage of the negative controls to automatically set a blanket minimum abundance as Control-Plate-3-Mix-3-Dry-P3-c_S56_L001 sadly has over 3000 <em>Phytophthora</em> reads.</p> <p>That takes under a minute to run, and classifies most of the samples.</p> <p>Opening the output file <code>summary/sardinia_20240912_v1.0.16.ITS1.samples.1s3g.xlsx</code> in Excel or similar should show you a table resembling Table 3 in the paper, without the baiting results, but with one row per sequencing sample, and additional columns with per-sample per-species read counts etc. The similarly named reads file as one row per unique amplicon sequence variant (ASV), and columns for each sequencing sample.</p> <p>All the <em>Phytophthora</em> classifications were to species level except a single ASV from E5BTB-DILUTE-Wet-P6_S17_L001 (S3 Bultei) with 15 reads, a perfect match to partial sequences MH588088.1 and MH593844.1, isolates Y1 and Y2, which is in the THAPBI PICT database but only at genus level:</p> <pre><code>>bad82a53fff502146e7ea00cbf2d9d3e Phytophthora<br>TTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCTAAAACTTTCCACGTGAACCGTTTCAAACCAAATAGTTGGGGGTCTTGTCTGGTGGCGGCTGCTGGCTTTATTGTTGGCGGCTGCTGCTGGGTGAGCCCTATCATGGCGAGCGTTTGGGCTTCGGCCTGAGCTAGTAGCATTTCTTTTAAACCCATTCCTTAATACTGATTATACT</code></pre> <p>There are 9 samples left simply as "Unknown" (all six from S2 Buddusò, one each from S3 Bultei, S4 Nuoro, and S6 Tempio). A further two samples contained low levels of an unknown sequence in addition to knowns.</p> <p>The unknown ASV from S1BTC-DILUTE-Wet-P6_S9_L001 (S3 Bultei) looks to be a novel <em>Phytophthora</em> if real, similar to <em>P. quercina</em>:</p> <pre><code>>3cd110db0a0b8fa8b971ea6b61c53970 Phytophthora?<br>TTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCTAAAAAACTTTCCACGTGAACCGTTTCAACCAATATTTTGGGGGTCTCGTCTGGCGTGCGGCTGTTGCTGTAAAAGGCGGCGGCTGTTGCTGGGTGAGCCCTATCATGGCAAACGTTTGGGCTTCGGTCTGAACAAGTAGCTCTTTTTTAAACCATTACTTATTACTGATTATACT</code></pre> <p>The unknown ASV from S4OC-DILUTE-Wet-P6_S2_L001 (S4 Nuoro) looks to be a <em>Pythium</em> (a perfect match to partial sequences MN269744 and KY822489 from uncultured clones):</p> <pre><code>>eac8c1931b4f8c57803e6ad9ffb4eb56 Pythium?<br>TTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCAAAAAACTATCCACGTGAACCGTTAAGCAAAAGTCTAGTTGGCTTGTGTTGTTCGGGAGTGTGTTGGGAAGAGCTTGGAGATGTCTTCGGATATTTCGATGCCTAGTACTGGACATCCTGGCGAGCGAGTCGGCTAGCAACGAAGGTCGGGAGTTCGCTTGCGGACTGATGTGCGCTTGTCGCATGTCGGTCGAAAGGCTTGAGCAAACGGCTGATCTATTACTTTTAAACCATACCATAACTACTGATGATACT</code></pre> <p>The unknown ASV from S9OC-Wet-P5_S12_L001 (S4 Nuoro) at only 38 reads seems to be an artefact of some kind, possibly chimeric:</p> <pre><code>>74933d826f6077cd5f3dd036f894429d Artefact?<br>TAGCCGTAGGGGAACCTGCGGCTGGATCACCTCCTTTCTGGATTCGGAAGGCAGGGATCAGTGATCAGTTATCAGAACCGATTGCTGCTCCTCTTCCGAGCATCCACAACGCCAGCTCTGGCAGGAATTCTGATATCTGATATCTGGTTTCTGCGATCTGGCAACGGCGCCGCCGTCTGCGCATCCCTTCTGCCGCGTTATTCCAGAGGGCAGTGATCAGTCATCAGAACCGATGGCGGATCCGCCCCCGGCTTCACGGCGAGCGCCCGAGCCTG</code></pre> <p>The remaining unknown ASVs were likely <em>Plasmopara</em>.</p> <p> </p>
Data from: Assessing the trophic ecology of top predators across a recolonisation frontier using DNA metabarcoding of diets
Top predator populations, once intensively hunted, are rebounding in size and geographic distribution. The cessation of sealing along coastal Australia and subsequent recovery of Australian Arctocephalus pusillus doriferus and long-nosed A. forsteri fur seals represents a unique opportunity to investigate trophic linkages at a frontier of predator recolonisation. We characterised the diets of both species across 2 locations of recolonisation, one site an established breeding colony, and the other, a new but permanent haul-out site. Using DNA metabarcoding, high taxonomic resolution data on diets was used to inform ecological trait-based analyses across time and location. Australian and long-nosed fur seals consumed 76 and 73 prey taxa, respectively, a prey diversity greater than previously reported. We found unexpected overlap of prey functional traits in the diets of both seal species at the haul-out site, where we observed strong trophic linkages with coastal ecosystems due to the prevalence of benthic, demersal and reef-associated prey. The diets of both seal species at the breeding colony were consistent with foraging patterns observed in the centre of their geographic range regarding diet partitioning between predator species and seasonal trends typically observed. The unexpected differences between sites in this region and the convergence of both predators' effective ecological roles at the range-edge haul-out site correlate with known differences in seal population densities and demographics at these and other newly recolonised locations. This study provides a baseline for the diets and trophic interactions for recovering fur seal populations and from which to understand the evolving ecology of predator recolonisation.
Data from: Spatio-temporal monitoring of deep-sea communities using metabarcoding of sediment DNA and RNA
We assessed spatio-temporal patterns of diversity in deep-sea sediment communities using metabarcoding. We chose a recently developed eukaryotic marker based on the v7 region of the 18S rRNA gene. Our study was performed in a submarine canyon and its adjacent slope in the Northwestern Mediterranean Sea, sampled along a depth gradient at two different seasons. We found a total of 5,569 molecular operational taxonomic units (MOTUs), dominated by Metazoa, Alveolata and Rhizaria. Among metazoans, Nematoda, Arthropoda and Annelida were the most diverse. We found a marked heterogeneity at all scales, with important differences between layers of sediment and significant changes in community composition with zone (canyon vs slope), depth, and season. We compared the information obtained from metabarcoding DNA and RNA and found more total MOTUs and more MOTUs per sample with DNA (ca. 20% and 40% increase, respectively). Both datasets showed overall similar spatial trends, but most groups had higher MOTU richness with the DNA template, while others, such as nematodes, were more diverse in the RNA dataset. We provide metabarcoding protocols and guidelines for biomonitoring of these key communities in order to generate information applicable to management efforts.
Data from: DNA metabarcoding illuminates dietary niche partitioning by African large herbivores
Niche partitioning facilitates species coexistence in a world of limited resources, thereby enriching biodiversity. For decades, biologists have sought to understand how diverse assemblages of large mammalian herbivores (LMH) partition food resources. Several complementary mechanisms have been identified, including differential consumption of grasses versus nongrasses and spatiotemporal stratification in use of different parts of the same plant. However, the extent to which LMH partition food-plant species is largely unknown because comprehensive species-level identification is prohibitively difficult with traditional methods. We used DNA metabarcoding to quantify diet breadth, composition, and overlap for seven abundant LMH species (six wild, one domestic) in semiarid African savanna. These species ranged from almost-exclusive grazers to almost-exclusive browsers: Grass consumption inferred from mean sequence relative read abundance (RRA) ranged from >99% (plains zebra) to <1% (dik-dik). Grass RRA was highly correlated with isotopic estimates of % grass consumption, indicating that RRA conveys reliable quantitative information about consumption. Dietary overlap was greatest between species that were similar in body size and proportional grass consumption. Nonetheless, diet composition differed between all species—even pairs of grazers matched in size, digestive physiology, and location—and dietary similarity was sometimes greater across grazing and browsing guilds than within them. Such taxonomically fine-grained diet partitioning suggests that coarse trophic categorizations may generate misleading conclusions about competition and coexistence in LMH assemblages, and that LMH diversity may be more tightly linked to plant diversity than is currently recognized.
Data from: Comparison of fish detections, community diversity, and relative abundance using environmental DNA metabarcoding and traditional gears
Background <p>Detecting species at low abundance, including aquatic invasive species (AIS), is critical for making informed management decisions. Environmental DNA (eDNA) methods have become a powerful tool for rare or cryptic species detection; however, many eDNA assays offer limited utility for community‐level analyses due to their use of species‐specific (presence/absence) 'barcodes'. Metabarcoding methods provide information on entire communities based on sequencing of all taxon‐specific barcodes within an eDNA sample.</p> Aims <p>Evaluate measures of fish species detections, community diversity, and estimates of relative abundance based on eDNA metabarcoding and traditional fisheries sampling approaches in the context of fish community characterization and AIS survellience.</p> Materials and Methods <p>In 2016, eight limnologically diverse lakes (surface area range: 13 – 1,728 ha) in Michigan, USA were sampled using a variety of traditional fisheries gears to characterize fish community composition. Environmental DNAs from surface (33 ± 6, mean ± 1 SD) and benthic (14 ± 2) water samples from each lake were isolated and amplified for two metabarcoding markers (mitochondrial 12S and 16S rDNA loci) using fish‐specific primers. Fish species detected within each lake were determined by comparing the sequencing data to a database of sequences from native Michigan fish species and 19 AIS on the Michigan's Watch List.</p> Results <p>Analysis of species accumulation curves indicated multi‐locus eDNA metabarcoding assays can enhance species detection capacities and characterize 95% of a fish community in fewer sampling efforts than traditional gear (range: 2 – 62, median: 14). In addition, all AIS detected in traditional gear samples were also detected by eDNA, while some AIS detected by eDNA assays were absent from traditional gear samples.</p> Discussion <p>Results reported here are, in part, driven by the lack of species‐selectivity during eDNA sampling events. Given the efficacy of eDNA assays, we suggest multi‐locus eDNA metabarcoding assays be implemented in early detection efforts.</p>
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