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915 results for “metagenomics”
Supplementary material 6 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
Table 4. The protist genera identified in Natural Tunnel State Park organised by read count. :
Supplementary material 4 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
Table 2. Primers and PCR Conditions. :
Supplementary material 1 from: Beentjes KK, Speksnijder AGCL, Schilthuizen M, Schaub BEM, van der Hoorn BB (2018) The influence of macroinvertebrate abundance on the assessment of freshwater quality in The Netherlands. Metabarcoding and Metagenomics 2: e26744. https://doi.org/10.3897/mbmg.2.26744
Monitoring event details and EQR scores :
Supplementary material 5 from: Deiner K, Lopez J, Bourne S, Holman LE, Seymour M, Grey EK, Lacoursière-Roussel A, Li Y, Renshaw MA, Pfrender ME, Rius M, Bernatchez L, Lodge DM (2018) Optimising the detection of marine taxonomic richness using environmental DNA metabarcoding: the effects of filter material, pore size and extraction method. Metabarcoding and Metagenomics 2: e28963. https://doi.org/10.3897/mbmg.2.28963
Alternative statistical model :
Supplementary material 4 from: Deiner K, Lopez J, Bourne S, Holman LE, Seymour M, Grey EK, Lacoursière-Roussel A, Li Y, Renshaw MA, Pfrender ME, Rius M, Bernatchez L, Lodge DM (2018) Optimising the detection of marine taxonomic richness using environmental DNA metabarcoding: the effects of filter material, pore size and extraction method. Metabarcoding and Metagenomics 2: e28963. https://doi.org/10.3897/mbmg.2.28963
4_NTC :
Supplementary material 2 from: Deiner K, Lopez J, Bourne S, Holman LE, Seymour M, Grey EK, Lacoursière-Roussel A, Li Y, Renshaw MA, Pfrender ME, Rius M, Bernatchez L, Lodge DM (2018) Optimising the detection of marine taxonomic richness using environmental DNA metabarcoding: the effects of filter material, pore size and extraction method. Metabarcoding and Metagenomics 2: e28963. https://doi.org/10.3897/mbmg.2.28963
Bioinformatic pipeline and thresholds :
Supplementary material 3 from: Deiner K, Lopez J, Bourne S, Holman LE, Seymour M, Grey EK, Lacoursière-Roussel A, Li Y, Renshaw MA, Pfrender ME, Rius M, Bernatchez L, Lodge DM (2018) Optimising the detection of marine taxonomic richness using environmental DNA metabarcoding: the effects of filter material, pore size and extraction method. Metabarcoding and Metagenomics 2: e28963. https://doi.org/10.3897/mbmg.2.28963
Profiling tables for all libraries :
Supplementary material 1 from: Deiner K, Lopez J, Bourne S, Holman LE, Seymour M, Grey EK, Lacoursière-Roussel A, Li Y, Renshaw MA, Pfrender ME, Rius M, Bernatchez L, Lodge DM (2018) Optimising the detection of marine taxonomic richness using environmental DNA metabarcoding: the effects of filter material, pore size and extraction method. Metabarcoding and Metagenomics 2: e28963. https://doi.org/10.3897/mbmg.2.28963
Extraction protocols :
Supplementary material 1 from: Bylemans J, Gleeson DM, Lintermans M, Hardy CM, Beitzel M, Gilligan DM, Furlan EM (2018) Monitoring riverine fish communities through eDNA metabarcoding: determining optimal sampling strategies along an altitudinal and biodiversity gradient. Metabarcoding and Metagenomics 2: e30457. https://doi.org/10.3897/mbmg.2.30457
: Data type: Microsoft Word Document (.docx)
Supplementary material 2 from: Bylemans J, Gleeson DM, Lintermans M, Hardy CM, Beitzel M, Gilligan DM, Furlan EM (2018) Monitoring riverine fish communities through eDNA metabarcoding: determining optimal sampling strategies along an altitudinal and biodiversity gradient. Metabarcoding and Metagenomics 2: e30457. https://doi.org/10.3897/mbmg.2.30457
: Data type: statistical data
Raw metagenomic data from Early Detection Rapid Response samples collected in Alaska in 2017
<p>In response to the threat of introductions of non-native forest insects, the Early Detection and Rapid Response (EDRR) program in Alaska monitors for arrivals of non-native insects, an effort that is limited by the time required to process samples using morphological methods. We compared conventional methods of processing EDRR traps with metabarcoding methods for processing the same samples. </p> <p>We deployed Lindgren funnel traps at three points of entry in Alaska using standard EDRR methods and the trap samples were later processed using routine sorting and identification based on morphology. The samples were then processed using High Throughput Sequencing (HTS) metabarcoding methods. In three samples bycatch was included and in three samples non-native species were added.</p> <p>This dataset includes all of the raw FASTQ files obtained from HTS sequencing. </p> <p>Complete specimen and occurrence data are available via an Arctos (<a href="https://arctosdb.org/">https://arctosdb.org/</a>) archive at <a href="https://arctos.database.museum/archive/2017_edrr_ngs_test_records">https://arctos.database.museum/archive/2017_edrr_ngs_test_records</a>. Sequence data have been deposited in in the NCBI Sequence Read Archive under BioProject <a href="https://www.ncbi.nlm.nih.gov/sra/PRJNA542936">PRJNA542936</a>. Complete sample data are provided in the file 2017_EDRR_STDP_sample_data.csv.</p>
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
Data supporting publication: MiFoDB, a workflow for microbial food metagenomic characterization, enables high-resolution analysis of fermented food microbial dynamics
<p>MiFoDB (Microbial Foods Database) is a workflow and primary reference database which includes 675 assembled MAGs and RefSeq bacterial, yeast, fungal, and substrate genomes from fermented foods.</p>
A soil-based metagenomics study
<p>A soil-based metagenomics study</p>
Japan Metagenome raw data
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Allen Brain Atlas
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International Brain Laboratory public data
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.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.