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1,076 results for “Metabarcoding”
Supplementary material 1 from: Ingala MR, Werner IE, Fitzgerald AM, Naro-Maciel E (2021) 18S rRNA amplicon sequence data (V1–V3) of the Bronx river estuary, New York. Metabarcoding and Metagenomics 5: e69691. https://doi.org/10.3897/mbmg.5.69691
Scripts used for metabarcoding analysis
Supplementary material 2 from: Ingala MR, Werner IE, Fitzgerald AM, Naro-Maciel E (2021) 18S rRNA amplicon sequence data (V1–V3) of the Bronx river estuary, New York. Metabarcoding and Metagenomics 5: e69691. https://doi.org/10.3897/mbmg.5.69691
Figure S1, Tables S1, S2
Multiple mitochondrial haplotypes within individual specimens affect biodiversity estimation by DNA metabarcoding
<p>In the global action for biodiversity conservation, the most urgent need is to delimit and identify species. DNA barcoding and metabarcoding based on molecular markers have been increasingly used in species delimitation and species diversity assessment, respectively, owing to their advantages of standardization and high throughput. The molecular markers used in these methods for animals are mainly derived from mitochondrial DNA. The principle involved is that inter-specific genetic distances are greater than intra-specific genetic distances. However, if multiple mitochondrial haplotypes exist within individual specimens and the divergence between these haplotypes is greater than the assumed maximum intra-specific divergence, the species delimitation and species diversity assessment may be affected. In our recent studies, a widespread phenomenon of multiple mitochondrial haplotypes within individual specimens was discovered in fig wasps (Hymenoptera, Insecta), having negative impact on DNA barcoding and metabarcoding. In this study, we expanded the taxa to include up to 480 specimens from 82 species, 66 genera, 45 families, and 13 orders of Insecta and Arachnida. By high-throughput sequencing of mitochondrial <i>cox1</i> fragments within individual specimens, we studied the pattern of multiple mitochondrial haplotypes within individual specimens. Three DNA metabarcoding strategies, one of which was simulated, were applied. The results revealed that multiple mitochondrial haplotypes within individual specimens could lead to an overestimation of species diversity by metabarcoding. Simultaneously, we found that the infection with intracellular endosymbiotic bacteria <i>Wolbachia</i> was significantly negatively related with this effect. These results suggest that additional attention should be paid to the interference of multiple mitochondrial haplotypes within individual specimens on the results of DNA metabarcoding in species diversity assessment of animals.</p>
DNA metabarcoding data characterizing insectivorous diet of purple martins (Progne subis subis) using two COI primer sets (ANML and ZBJ)
<p>DNA metabarcoding is a molecular technique frequently used to characterize diet composition of insectivorous birds. However, results are sensitive to methodological decisions made during sample processing, with primer selection being one of the most critical. The most frequently used DNA metabarcoding primer set for avian insectivores is ZBJ. However, recent studies have found that ZBJ produces significant biases in prey classification that likely influence our understanding of foraging ecology. A new primer set, ANML, has shown promise for characterizing insectivorous bat diets with fewer taxonomic biases than ZBJ, but ANML is not yet widely used to study insectivorous birds. Here, we evaluate the ANML primer set for use in metabarcoding of avian insectivore diets through comparison with the more commonly used ZBJ primer set. Fecal samples were collected from both adult and nestling Purple Martins (<i>Progne subis subis</i>) at two sites in the USA and one site in Canada to maximize variation in diet composition and to determine if primer selection impacts our understanding of diet variation among sites. In total, we detected 71 arthropod prey species, 39 families, and 10 orders. Of these, 40 species were uniquely detected by ANML, whereas only 11 were uniquely detected by ZBJ. We were able to classify 54.8% of exact sequence variants from ANML libraries to species compared to 33.3% from ZBJ libraries. We found that ANML outperformed ZBJ for PCR efficacy, taxonomic coverage, and specificity of classification, but that using both primer sets together produced the most comprehensive characterizations of diet composition. Significant variation in both alpha- and beta-diversity between sites was found using each primer set separately and in combination. To our knowledge, this is the first published metabarcoding study to directly compare avian diet characterizations produced with both ANML and ZBJ primer sets.</p>
Figure 5 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
Figure 5 - Global Nonmetric Multidimensional Scaling (NMDS) graph demonstrating the relative placement of samples (lower case letters, encoded in Suppl. material 1) in the ordination space. 95% confidence ellipses are indicated for each barcode-primer pair combination. For two-dimensional solution, stress=0.191 (R2=0.875).
Figure 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
Figure 2 - Sample-based OTU richness as recovered by different barcode-primer pair combinations. Error bars denote standard error; different letters indicate statistically different groups.
Figure 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
Figure 1 - Map of ribosomal DNA indicating variable regions as well as primers used and/or discussed in this study. Primers pairs used for HTS are highlighted.
Figure 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
Figure 3 - Rarefied OTU accumulation curves for samples based on the (a) ITS1 and (b) ITS2 barcodes and their 95% confidence intervals.
Figure 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
Figure 6 - Relative abundance of fungal classes in the amplicon and metagenomics data sets divided into SSU, ITS, and LSU subsets averaged over different barcodes (amplicon data) and 14 shared samples. Asterisks in the margins indicate significant differences in recovery of classes among SSU, ITS, and LSU of metagenomics (right) and amplicon (left) data sets. Asterisks in the center indicate significant differences between the metagenomics and amplicon-bases approaches.
Figure 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
Figure 7 - Differences in sequence length in the ITS1 and ITS2 barcodes of 16 most abundant fungal classes as revealed based on amplicon libraries in this study. Columns, asterisks, and error bars represent mean and median values and standard deviation, respectively. Numbers inside bars indicate the number of sequences analyzed (n). Taxa are ordered by average length of the ITS1 region.
Figure 4 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
Figure 4 - Relationship between connectance and adjusted coefficient of determination (R2adj) for floristic variables across different barcode-primer pair combinations based on (a) Bray-Curtis distance and (b) Hellinger distance. Pointed line indicates correlation in the ITS1Fngs-ITS2 data set (filled circles), covering eight connectance classes (C<0.45). Open circles, other ITS1 and ITS2 primer pairs; triangles, SSU barcodes; rectangles, LSU barcodes.
Figure 1 from: Riit T, Tedersoo L, Drenkhan R, Runno-Paurson E, Kokko H, Anslan S (2016) Oomycete-specific ITS primers for identification and metabarcoding. MycoKeys 14: 17-30. https://doi.org/10.3897/mycokeys.14.9244
Figure 1 - A Map of universal and oomycete-specific ITS region primers B Taxa with mismatches in the binding sites of primers ITS1oo and ITS3oo. Only taxa with 10% or more mismatching accessions are shown.
Figure 2 from: Riit T, Tedersoo L, Drenkhan R, Runno-Paurson E, Kokko H, Anslan S (2016) Oomycete-specific ITS primers for identification and metabarcoding. MycoKeys 14: 17-30. https://doi.org/10.3897/mycokeys.14.9244
Figure 2 - OTU and read distributions of ITS1 (A) and ITS2 (B) reads. Panels starting from outermost: 1 Oomycete read distribution between orders 2 Read distribution between classes, excluding reads of unknown origin 3 Read distribution between classes, including reads of unknown origin 4 OTU distribution between classes.
Supplementary material 2 from: Baricevic A, Chardon C, Kahlert M, Karjalainen SM, Pfannkuchen DM, Pfannkuchen M, Rimet F, Tankovic MS, Trobajo R, Vasselon V, Zimmermann J, Bouchez A (2022) Recommendations for the preservation of environmental samples in diatom metabarcoding studies. Metabarcoding and Metagenomics 6: e85844. https://doi.org/10.3897/mbmg.6.85844
Data 2
Supplementary material 3 from: Baricevic A, Chardon C, Kahlert M, Karjalainen SM, Pfannkuchen DM, Pfannkuchen M, Rimet F, Tankovic MS, Trobajo R, Vasselon V, Zimmermann J, Bouchez A (2022) Recommendations for the preservation of environmental samples in diatom metabarcoding studies. Metabarcoding and Metagenomics 6: e85844. https://doi.org/10.3897/mbmg.6.85844
Data 3
Supplementary material 1 from: Baricevic A, Chardon C, Kahlert M, Karjalainen SM, Pfannkuchen DM, Pfannkuchen M, Rimet F, Tankovic MS, Trobajo R, Vasselon V, Zimmermann J, Bouchez A (2022) Recommendations for the preservation of environmental samples in diatom metabarcoding studies. Metabarcoding and Metagenomics 6: e85844. https://doi.org/10.3897/mbmg.6.85844
Data 1
Supplementary material 2 from: Osman OA, Andersson J, Martin-Sanchez PM, Eiler A (2022) National eDNA-based monitoring of Batrachochytrium dendrobatidis and amphibian species in Norway. Metabarcoding and Metagenomics 6: e85199. https://doi.org/10.3897/mbmg.6.85199
Table S2
Supplementary material 7 from: Bíró T, Duleba M, Földi A, Kiss KT, Orgoványi P, Trábert Z, Vadkerti E, Wetzel CE, Ács É (2022) Metabarcoding as an effective complement of microscopic studies in revealing the composition of the diatom community – a case study of an oxbow lake of Tisza River (Hungary) with the description of a new Mayamaea species. Metabarcoding and Metagenomics 6: e87497. https://doi.org/10.3897/mbmg.6.87497
Alignment S1
Supplementary material 6 from: Bíró T, Duleba M, Földi A, Kiss KT, Orgoványi P, Trábert Z, Vadkerti E, Wetzel CE, Ács É (2022) Metabarcoding as an effective complement of microscopic studies in revealing the composition of the diatom community – a case study of an oxbow lake of Tisza River (Hungary) with the description of a new Mayamaea species. Metabarcoding and Metagenomics 6: e87497. https://doi.org/10.3897/mbmg.6.87497
Figure S1
Supplementary material 3 from: Osman OA, Andersson J, Martin-Sanchez PM, Eiler A (2022) National eDNA-based monitoring of Batrachochytrium dendrobatidis and amphibian species in Norway. Metabarcoding and Metagenomics 6: e85199. https://doi.org/10.3897/mbmg.6.85199
Table S3, Figures S1, S2
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