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167 results for “Metagenomic DNA”
Catalog of GenBank sequence read archive (SRA) entries of metagenomic DNA sequence analyses of bacterial and archaeal water column communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2012
In contrast to temperate systems, Arctic lagoons that span the Alaska Beaufort Sea coast face extreme seasonality. Nine months of ice cover up to ∼1.7 m thick is followed by a spring thaw that introduces an enormous pulse of freshwater, nutrients, and organic matter into these lagoons over a relatively brief 2–3 week period. Prokaryotic communities link these subsidies to lagoon food webs through nutrient uptake, heterotrophic production, and other biogeochemical processes, but little is known about how the genomic capabilities of these communities respond to seasonal variability. This study characterizes the metabolic capabilities of microbial communities across three seasons in two lagoons and one open coastal site along the eastern Alaska Beaufort Sea coast. We used metagenomic DNA sequence data of bacterial and archaeal water column communities to identify genes of relevant biogeochemical pathways. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA642637 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA642637. This data package is associated with the following publication: Baker, Kristina D., Colleen T. E. Kellogg, James W. McClelland, Kenneth H. Dunton, and Byron C. Crump. “The Genomic Capabilities of Microbial Communities Track Seasonal Variation in Environmental Conditions of Arctic Lagoons.” Frontiers in Microbiology 12 (2021). https://doi.org/10.3389/fmicb.2021.601901. Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provi
Evaluation of an adapted semi-automated DNA extraction for human salivary shotgun metagenomics
<p>This deposit contains :</p> <p>- a RMarkdown filte containing the codes for the mcirobial analysis of saliva samples</p> <p>- the html report with codes, results and figures</p> <p>- a RData containing microbial datasets (MSp species abundance table, genus, family and phylum abundance tables, matrix of genes correlations, taxonomy)</p> <p>- a RData containing associated metadata </p>
SPIKEPIPE: A metagenomic pipeline for the accurate quantification of eukaryotic species occurrences and intraspecific abundance change using DNA barcodes or mitogenomes
<p>The accurate quantification of eukaryotic species abundances from bulk samples remains a key challenge for community ecology and environmental biomonitoring. We resolve this challenge by combining shotgun sequencing, mapping to reference DNA barcodes or to mitogenomes, and three correction factors: (a) a percent‐coverage threshold to filter out false positives, (b) an internal‐standard DNA spike‐in to correct for stochasticity during sequencing, and (c) technical replicates to correct for stochasticity across sequencing runs. The SPIKEPIPE pipeline achieves a strikingly high accuracy of intraspecific abundance estimates (in terms of DNA mass) from samples of known composition (mapping to barcodes R<sup>2</sup> = .93, mitogenomes R<sup>2</sup> = .95) and a high repeatability across environmental‐sample replicates (barcodes R<sup>2</sup> = .94, mitogenomes R<sup>2</sup> = .93). As proof of concept, we sequence arthropod samples from the High Arctic, systematically collected over 17 years, detecting changes in species richness, species‐specific abundances, and phenology. SPIKEPIPE provides cost‐efficient and reliable quantification of eukaryotic communities.</p>
SPIKEPIPE: A metagenomic pipeline for the accurate quantification of eukaryotic species occurrences and intraspecific abundance change using DNA barcodes or mitogenomes
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Comprehensive, targeted eukaryotic metagenomics analysis of environmental DNA biodiversity using Oxford Nanopore sequencing
<p><span>Metagenomics has become a prominent technology for studying the functional potential of all organisms in a microbial and eukaryotic community. The study of symbiotic organisms from different classes or kingdoms, including those previously unknown, is possible with simultaneous and equally efficient metagenomic analysis of these species. A variety of targeted primer sets are used for eukaryotic metagenomic biodiversity, including those that are universal for specific families, classes</span><span>,<span> or kingdoms. The most universal for all existing cellular organisms is the presence of ribosomal RNA encoding gene sequences. For eukaryotic sequences, these are 16S and 23s rDNA, </span>and <span>for eukaryotic sequences of nuclear (18S and 28S) and mitochondrial (12S and 16S) ribosomal RNA. Here we present the application of the eukaryotic metagenomics approach to the simultaneous, quantitative</span>,<span> and unbiased identification of most eukaryotic species. To achieve this, we have developed a universal PCR assay that targets the most conservative nuclear regions of the ribosomal gene for all cellular organisms, including plants, algae, fungi, protists, insects</span>,<span> and animals. The amplification product contains polymorphic regions of both ribosomal genes and the intergenic spacer. The size of the PCR products varies by class, kingdom</span>,<span> or domain, ranging from 2 kb for fungi to 7 kb for birds. This assay is also adapted for use with the Oxford Nanopore Rapid Barcoding Library Kit, which enables metagenomic biodiversity analysis. Our approach provides a rapid, sensitive</span>,<span> and equally efficient way to study the composition of eDNA from mixed species in the environment. This protocol reduces the time and cost of metagenomic biodiversity analysis using Oxford Nanopore sequencing. We can efficiently analyze the biodiversity of mixed species present in environmental samples.</span></span></p>
Oxford Nanopore sequencing for comprehensive, targeted eukaryotic metagenomics analysis of environmental DNA biodiversity
<p><span>The study of symbiotic organisms from different classes or kingdoms, including those previously unknown, is possible with simultaneous and equally efficient metagenomic analysis of these species. A variety of targeted primer sets are used for eukaryotic metagenomic biodiversity, including those that are universal for specific families, classes</span><span>,<span> or kingdoms. The most universal for all existing cellular organisms is the presence of ribosomal RNA encoding gene sequences. For eukaryotic sequences, these are 16S and 23s rDNA, </span>and <span>for eukaryotic sequences of nuclear (18S and 28S) and mitochondrial (12S and 16S) ribosomal RNA. Here, we present the application of the eukaryotic metagenomics approach to the simultaneous, quantitative</span>,<span> and unbiased identification of most eukaryotic species. </span></span></p>
Metagenomic and genomic data associated with the tooth-cavity hair and endogenous DNA of the Tsavo lions
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Supplementary material 2 from: Nugent CM, Adamowicz SJ (2020) Alignment-free classification of COI DNA barcode data with the Python package Alfie. Metabarcoding and Metagenomics 4: e55815. https://doi.org/10.3897/mbmg.4.55815
File S2 – Python script for custom grid search of hyperparameters for optimization of the neural network
Supplementary material 3 from: Nugent CM, Adamowicz SJ (2020) Alignment-free classification of COI DNA barcode data with the Python package Alfie. Metabarcoding and Metagenomics 4: e55815. https://doi.org/10.3897/mbmg.4.55815
File S3 – The parameters utilized in the grid search for each of the five machine learning algorithms tested in the design of the Alfie package
Supplementary material 4 from: Nugent CM, Adamowicz SJ (2020) Alignment-free classification of COI DNA barcode data with the Python package Alfie. Metabarcoding and Metagenomics 4: e55815. https://doi.org/10.3897/mbmg.4.55815
File S4 – Jupyter notebook with tutorial demonstrating how to apply the Alfie classifier in the Python programming language, and how to train custom alignment-free classifiers using the Alfie training module
Supplementary material 1 from: Basset Y, Donoso DA, Hajibabaei M, Wright MTG, Perez KHJ, Lamarre GPA, De León LF, Palacios-Vargas JG, Castaño-Meneses G, Rivera M, Perez F, Bobadilla R, Lopez Y, Ramirez JA, Barrios H (2020) Methodological considerations for monitoring soil/litter arthropods in tropical rainforests using DNA metabarcoding, with a special emphasis on ants, springtails and termites. Metabarcoding and Metagenomics 4: e58572. https://doi.org/10.3897/mbmg.4.58572
Methodological considerations for monitoring soil/litter arthropods in tropical rainforests using DNA metabarcoding, with a special emphasis on ants, springtails and termites
Supplementary material 1 from: Weigand AM, Desquiotz N, Weigand H, Szucsich N (2021) Application of propylene glycol in DNA-based studies of invertebrates. Metabarcoding and Metagenomics 5: e57278. https://doi.org/10.3897/mbmg.5.57278
Overview of DNA-based studies of invertebrates applying propylene glycol, sorted by year and taxonomic group
Supplementary material 1 from: Chua PYS, Carøe C, Crampton-Platt A, Reyes-Avila CS, Jones G, Streicker DG, Bohmann K (2022) A two-step metagenomics approach for the identification and mitochondrial DNA contig assembly of vertebrate prey from the blood meals of common vampire bats (Desmodus rotundus). Metabarcoding and Metagenomics 6: e78756. https://doi.org/10.3897/mbmg.6.78756
A two-step metagenomics approach for prey identification from the blood meals of common vampire bats (Desmodus rotundus)
Supplementary material 3 from: Jeunen G-J, Lipinskaya T, Gajduchenko H, Golovenchik V, Moroz M, Rizevsky V, Semenchenko V, Gemmell NJ (2022) Environmental DNA (eDNA) metabarcoding surveys show evidence of non-indigenous freshwater species invasion to new parts of Eastern Europe. Metabarcoding and Metagenomics 6: e68575. https://doi.org/10.3897/mbmg.6.e68575
Reference databases generated by ecoPCR and used by ecotag for taxonomy assignment of OTUs for fish and crustacean eDNA results
Supplementary material 1 from: Tedersoo L, Liiv I, Kivistik PA, Anslan S, Kõljalg U, Bahram M (2016) Genomics and metagenomics technologies to recover ribosomal DNA and single-copy genes from old fruit-body and ectomycorrhiza specimens. MycoKeys 13: 1-20. https://doi.org/10.3897/mycokeys.13.8140
Full information and metadata about the genomic and metagenomic samples : Explanation note: Detailed information about metadata, DNA quality and genomic/metagenomic results of fruit-body and EcM root tip samples.
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: 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
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.
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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.
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