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1,076 results for “Metabarcoding”
Supplementary material 7 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S7. Taxonomic classification of the rDNA of fungal.: Explanation note: Taxonomic classification of the rDNA of fungal shotgun metagenome.
Supplementary material 3 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S3. Data set of the SSU V4 and V5 barcodes.: Explanation note: Data set of the SSU V4 and V5 barcodes.
Supplementary material 1 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S1. Characteristics of soil samples.: Explanation note: Characteristics of soil samples used in this study.
Supplementary material 6 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S6. Data set of the LSU D1, D2, and D3 barcodes.: Explanation note: Data set of the LSU D1, D2, and D3 barcodes.
Supplementary material 2 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S2. Taxonomic composition and clustering of the mock community sample.: Explanation note: Taxonomic composition and clustering of the mock community sample.
Supplementary material 1 from: Macher J, Macher T, Leese F (2017) Combining NCBI and BOLD databases for OTU assignment in metabarcoding and metagenomic datasets: The BOLD_NCBI _Merger. Metabarcoding and Metagenomics 1: e22262. https://doi.org/10.3897/mbmg.1.22262
The supplementary material contains the BOLD_NCBI_Merger script, the needed folder structure and the tutorial explaining how to use the script
Supplementary material 8 from: Vamos E, Elbrecht V, Leese F (2017) Short COI markers for freshwater macroinvertebrate metabarcoding. Metabarcoding and Metagenomics 1: e14625. https://doi.org/10.3897/mbmg.1.14625
JAMP metabarcoding pipeline (used R commands) and expected single species mock sample haplotypes (fasta file)
Supplementary material 1 from: Graupner N, Boenigk J, Bock C, Jensen M, Marks S, Rahmann S, Beisser D (2017) Functional and phylogenetic analysis of the core transcriptome of Ochromonadales. Metabarcoding and Metagenomics 1: e19862. https://doi.org/10.3897/mbmg.1.19862
KEGG orthologous genes of the core trancriptome of the herein investigated Ochromonadales (Poteriospumella lacustris strains JBC07, JBM10, JBNZ41; Poterioochromonas malhamensis DS; Spumella vulgaris 199hm; Pedospumella encystans JBMS11) used for phylogenetic analyses.
Supplementary material 1 from: Lefort M, Wratten S, Cusumano A, Varennes Y, Boyer S (2017) Disentangling higher trophic level interactions in the cabbage aphid food web using high-throughput DNA sequencing. Metabarcoding and Metagenomics 1: e13709. https://doi.org/10.3897/mbmg.1.13709
OSR aphid mummy collection. Sampling location and size / Amplification success of mummies' DNA extracts by Illumina sequencing.
Supplementary material 4 from: Boenigk J, Wodniok S, Bock C, Beisser D, Hempel C, Grossmann L, Lange A, Jensen M (2018) Geographic distance and mountain ranges structure freshwater protist communities on a European scalе. Metabarcoding and Metagenomics 2: e21519. https://doi.org/10.3897/mbmg.2.21519
Richness is shown for different elevations. The number of lakes within this elevation range is indicated. While mean richness ranges around 750 OTUs it drops to around 400 OTUs at high elevations. The transition seems to be around or slightly below 1400m.
Data from: Environmental DNA metabarcoding reliably recovers arthropod interactions which are frequently observed by video recordings of flowers
<p>Environmental DNA (eDNA) metabarcoding promises to be a cost- and time-efficient monitoring tool to detect interactions of arthropods with plants. However, observation-based verification of the eDNA derived data is still required to confirm whether the arthropods indeed previously interacted with the plant. Here we conducted a comparative analysis of the performance of eDNA metabarcoding and video camera observations to detect arthropod communities associated with sunflowers (<em>Helianthus annuus</em>, L.). We compared the taxonomic composition, interaction type, and diversity by testing for an effect of arthropod interaction time and occupancy on successful taxon recovery by eDNA. We also tested if pre-washing of the flowers successfully removed eDNA deposition from before the video camera recording, thus enabling a reset of the community for standardized monitoring. We find that eDNA and video camera observations recovered distinct communities, with about a quarter of the arthropod families overlapping. However, the overlapping taxa comprised ~90% of the interactions observed by the video camera. Interestingly, eDNA metabarcoding recovered more unique families than the video cameras, but approx. two-third of those unique observations were rare species. The eDNA-derived families were biased towards plant sap-suckers, showing that such species may deposit more eDNA than e.g. transient pollinators. We also find that pre-washing of the flower heads did not suffice to remove all eDNA traces, suggesting that eDNA on plants may be more temporally stable than previously thought. Our work highlights the great potential of eDNA as a tool to detect plant-arthropod interactions, particularly for specialized and frequently interacting taxa.</p>
Galaxy Training Tutorial: "Divers and Adaptable Visualisations of Metabarcoding Data Using ampvis2"
<p><span>This tutorial teaches you how to filter data for significant information, visualise it effectively, and adapt plots to your needs. You will explore multiple visualisation methods to gain deeper insights from your data.</span></p> <p><a href="https://training.galaxyproject.org/training-material/"><span>Galaxy Training Material Website</span></a></p>
Supplementary material 4 from: Theissinger K, Kästel A, Elbrecht V, Makkonen J, Michiels S, Schmidt S, Allgeier S, Leese F, Brühl C (2018) Using DNA metabarcoding for assessing chironomid diversity and community change in mosquito controlled temporary wetlands. Metabarcoding and Metagenomics 2: e21060. https://doi.org/10.3897/mbmg.2.21060
OTU ID, taxonomy of identified species, BOLD Bin, sequence abundancies per site an time and OTU sequences
Supplementary material 3 from: Theissinger K, Kästel A, Elbrecht V, Makkonen J, Michiels S, Schmidt S, Allgeier S, Leese F, Brühl C (2018) Using DNA metabarcoding for assessing chironomid diversity and community change in mosquito controlled temporary wetlands. Metabarcoding and Metagenomics 2: e21060. https://doi.org/10.3897/mbmg.2.21060
We pooled the library according to the number of specimens per sample and could show that our read abundance highly correlates with specimen abundance. Thus, we could use the read abundancies as surrogates for relative species abundancies.
Supplementary material 9 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
Bland-Altman plots for the total fish eDNA (a), Japanese anchovy (Engraulis japonicus; b) and Japanese jack mackerel (Trachurus japonicus; c). Dashed lines indicate 95% uppper and lower limits and solid line indicates mean value.
Supplementary material 6 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 numbers of eDNA copies of marine fish species quantified by metabarcoding with the internal standard DNA
Supplementary material 1 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
Location of sample sites in south-western New South Wales, Australia. Location of study area shown as a rectangle on the map of Australia (insert). Names of states and territories are marked. Solid lines indicate state boundaries. Dashed line indicates the course of the Darling River. Dotted line indicates the course of the Great Darling Anabranch. Circles indicate sampling locations, squares indicate towns. Created using Inkscape 0.92.0 (https://inkscape.org/en/). Based on information from Geoscience Australia, Commonwealth of Australia 'National base map with external territories', (http://www.ga.gov.au/interactive-maps/#/theme/national-location-information/map/nationalmap) published under the Creative Commons license CC-By-Au.
Supplementary material 8 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 DNA copy numbers quantified by qPCR. Correlations for the total fish eDNA (all data, a; enlarged figure, b), Japanese anchovy (Engraulis japonicus; all data, c; enlarged figure, d) and Japanese jack mackerel (Trachurus japonicus; all data, e; enlarged figure, f). Dashed and soild lines indicate 1:1 line and linear regression line, respectively. Regression lines in the enlarged figures were drawn by excluding outliers. All regression lines, except for the lines for total fish eDNA, were significant (P < 0.05). Dotted boxed regions in a, c and e correspond to the range of the graphs in b, d and f, respectively. The intensity of red colour indicates the slope of the regression line used to convert sequence reads to the copy numbers.
Supplementary material 13 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 (GenBank Data), at 3 minimum read depth. Underlined taxa were changed based on the distribution of taxa in the study zone.
Supplementary material 2 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
Table displaying the closest matches on the BOLD database for the 24 reference samples for matK, rbcL and ITS2.
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