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915 results for “metagenomics”
Supplementary material 3 from: Sun T, Zou W, Dong Q, Huang O, Tang D, Yu H (2022) Morphology, phylogeny, mitogenomics and metagenomics reveal a new entomopathogenic fungus Ophiocordyceps nujiangensis (Hypocreales, Ophiocordycipitaceae) from Southwestern China. MycoKeys 94: 91-108. https://doi.org/10.3897/mycokeys.94.89425
Rarefaction curves (Shannon-Wiener curve) of the fungal communities collected from the fruiting body from four different specimens of Ophiocordyceps nujiangensis
Supplementary material 2 from: Sun T, Zou W, Dong Q, Huang O, Tang D, Yu H (2022) Morphology, phylogeny, mitogenomics and metagenomics reveal a new entomopathogenic fungus Ophiocordyceps nujiangensis (Hypocreales, Ophiocordycipitaceae) from Southwestern China. MycoKeys 94: 91-108. https://doi.org/10.3897/mycokeys.94.89425
The information of species and their mitochondrial genomes for constructing the mitochondrial-genome phylogenetic tree of Hypocreales
Twenty-five metagenome assembled genomes recovered from the gut microbiome of the domestic ferret, Mustela putorius
<p>This is metadata provided in a single excel file for 25 unique metagenome assembled genomes (MAGs) recovered from the gut microbiome of three domestic ferrets (<em>Mustela putorius</em>). Details on both MAG and host ferret metadata, as well as information on sample collection, DNA sequencing, and bioinformatic processing can be found here, in association with the American Society for Microbiology Resource Announcement by Amundson et al. (in prep). </p>
Rhometa: Population recombination rate estimation from metagenomic read datasets
<p>Rhometa provides a suite of pipelines for calculating the recombination rate in metagenomic datasets. The tool is developed using Nextflow, a workflow management tool, and the Python programming language. The repository contains all results generated using Rhometa for the manuscript. During the development of Rhometa, various pipelines were employed, including our Nextflow LDhat pipeline. The scripts and results for all pipelines are included. The Github repositories for the pipelines are referenced in the accompanying manuscript. Files named "figure" pertain to the evaluation of simulated data, the simulations themselves have not been included to save space. However, the scripts used to generate the simulations and the final results are included. The file "Lookup_tables.zip" contains lookup tables used in the analysis. The lookup tables are too large to include, but the scripts used to create them are included. Where applicable, experiment accession codes are provided.</p>
Supplementary material 1 from: Bourret A, Nozères C, Parent E, Parent GJ (2023) Maximizing the reliability and the number of species assignments in metabarcoding studies using a curated regional library and a public repository. Metabarcoding and Metagenomics 7: e98539. https://doi.org/10.3897/mbmg.7.98539
Creation of Gulf of St. Lawrence regional library (GSL-rl) and creation of an eDNA metabarcoding dataset
Simulated metagenomic datasets used for determining the threshold score cutoff for taxa prediction using StrainIQ software
<p>StrainIQ (Strain Identification and Quantification) is a novel tool that implements a new <em>n</em>-gram based algorithm for predicting and quantifying strain-level taxa from whole genome metagenomic sequencing data. To avoid the false positive prediction of by StrainIQ algorithm, we determined cutoff score for each site specific n-gram model called DSEM (DNA Signature Element Model) using positive and negative metagenomic datasets. The Figure2-datasets.zip has positive (from GI tract genomes) and negative (from non-GI tract genomes) simulated metagenomic datasets that were used for determining optimal cut-off based on GI tract DSEM.</p> <p>Please find the new link for this data at https://zenodo.org/record/8132164</p>
Validation of StarinIQ software performance using metagenomic data from the ATCC gut microbiome genomic mix
<p>StrainIQ (Strain Identification and Quantification) is a novel tool that implements a new <em>n</em>-gram based algorithm for predicting and quantifying strain-level taxa from whole genome metagenomic sequencing data. We tested our method using simulated (GI tract reference genomes) and mock metagenomic datasets (from ATCC microbial genomic mix) and compared its performance with existing methods. The gut_even.zip file contains the metagenomic sequence data from the ATCC Gut Microbiome Genomic Mix (MSA-1006). From this original sequencing sequence (nearly 120X), we created additional datasets with 90X, 60X, 30X, 5X, 3X, and 1X coverage and tested StrainIQ performance on those sequencing datasets with varying coverage.</p> <p>Please find the new link for this data at https://zenodo.org/record/8132164</p>
Metagenome Assembly Genome Bins.tar
<p>Metagenome assembly genome bins of cattle</p>
Supplementary material 2 from: Hubancheva A, Bozicevic V, Morinière J, Goerlitz HR (2023) DNA metabarcoding data from faecal samples of the lesser (Myotis blythii) and the greater (Myotis myotis) mouse-eared bats from Bulgaria. Metabarcoding and Metagenomics 7: e106844. https://doi.org/10.3897/mbmg.7.106844
Taxonomic relationships and relative abundance of prey and parasite species in faecal samples from M. myotis and M. blythii from Bulgaria
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
<p>Database and models for deepARG: </p> <p>see this link for details: <a href="https://bitbucket.org/gusphdproj/deeparg-ss/src/fbe063e24cf79d83a88499353aa15a85b58a300e/?at=master">gusphdproj / deeparg-ss — Bitbucket</a></p>
Supplementary material 9 from: Macher T-H, Schütz R, Yildiz A, Beermann AJ, Leese F (2023) Evaluating five primer pairs for environmental DNA metabarcoding of Central European fish species based on mock communities. Metabarcoding and Metagenomics 7: e103856. https://doi.org/10.3897/mbmg.7.103856
Processed TaXon tables of each primer pair (subtracted negative controls and filtered for fish and lamprey taxa OTUs)
Supplementary material 1 from: Macher T-H, Schütz R, Yildiz A, Beermann AJ, Leese F (2023) Evaluating five primer pairs for environmental DNA metabarcoding of Central European fish species based on mock communities. Metabarcoding and Metagenomics 7: e103856. https://doi.org/10.3897/mbmg.7.103856
Pairwise comparison of the log-transformed reads of the non-normalized mock community (MC1) compared to the DNA concentration (ng/ul) of each species
Supplementary material 3 from: Macher T-H, Schütz R, Yildiz A, Beermann AJ, Leese F (2023) Evaluating five primer pairs for environmental DNA metabarcoding of Central European fish species based on mock communities. Metabarcoding and Metagenomics 7: e103856. https://doi.org/10.3897/mbmg.7.103856
Sampled specimens and their respective species assignment collected for the fish mock community, extraction date, collection site, and concentration after DNA extraction
Supplementary material 4 from: Macher T-H, Schütz R, Yildiz A, Beermann AJ, Leese F (2023) Evaluating five primer pairs for environmental DNA metabarcoding of Central European fish species based on mock communities. Metabarcoding and Metagenomics 7: e103856. https://doi.org/10.3897/mbmg.7.103856
List of all species reported from Germany, their occurrence status, and their presence in the mock community (data from fishbase.org)
Supplementary material 2 from: Macher T-H, Schütz R, Yildiz A, Beermann AJ, Leese F (2023) Evaluating five primer pairs for environmental DNA metabarcoding of Central European fish species based on mock communities. Metabarcoding and Metagenomics 7: e103856. https://doi.org/10.3897/mbmg.7.103856
Pairwise comparison of the log-transformed reads of the non-normalized mock community (MC1) compared to log-transformed reads of the normalized mock community (MC2) of each species
Figure 3 in Whole and nearly complete mitochondrial genomes of an endemic and endangered neotropical rabbit (Romerolagus diazi) assembled using non-invasive eDNA metagenomics (field droppings)
Figure 3. Phylogenetic analysis of Romerolagus diazi and related species in the family Leporidae. Totalevidence phylogenetic tree obtained from ML analysis based on a concatenated alignment of amino acids of the 13 protein-coding genes present in the mitochondrial genome of representatives of the family Leporidae. In the analysis, two species of the family Ochotonidae were used as the outgroup. Numbers above or below the branches represent bootstrap values. Photo credit: J.A. Guerrero.
Figure 2 in Whole and nearly complete mitochondrial genomes of an endemic and endangered neotropical rabbit (Romerolagus diazi) assembled using non-invasive eDNA metagenomics (field droppings)
Figure 2. Relative codon usage analysis for protein coding genes (PCGs) in the mitochondrial genome of Romerolagus diazi assembled from eDNA (field collected droppings, sample SRR14209493 [top] and SRR14209494 [bottom]).
Figure 1 in Whole and nearly complete mitochondrial genomes of an endemic and endangered neotropical rabbit (Romerolagus diazi) assembled using non-invasive eDNA metagenomics (field droppings)
Figure 1. Circular DNA mitochondrial genome map of Romerolagus diazi assembled from eDNA (field collected droppings, sample SRR14209493). The annotated map depicts 13 protein-coding genes (PCGs), two ribosomal RNA genes (rrnS: 12S ribosomal RNA and rrnL: 16S ribosomal RNA), 22 transfer RNA (tRNA) genes, and the putative control region (not annotated). Photo credit: J.A. Guerrero.
Metagenomics for Ocular Inflammation
ClinicalTrials.gov study NCT06974162. IPD Sharing: YES. Countries: 1. Publications: 3.
The Role of the Gut Metagenome on the Development of Age Related Macular Degeneration (AMD)
ClinicalTrials.gov study NCT02438111. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ScienceDex guides
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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.
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.