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915 results for “metagenomics”
Phoronid-associated metagenome assembled genomes
<p>Metagenome assembled genomes (MAGs) associated with:</p> <p>Phoronids and their tubes harbor distinct microbiomes compared to surrounding sediment</p> <p>Analysis, code, intermediate and supporting files are archived here: <a href="../doi/10.5281/zenodo.11225270">10.5281/zenodo.11225270</a><br><br>This archive contains:<br>(i) Five fasta files representing the MAGs described in the above titled work<br>(ii) Metadata file describing the five MAGs (i.e., Table 1 from the above work)</p>
Metagenome assembled genomes for Gilbert et al. 2024 ARW Pond study
<p>MAG assemblies used for GIlbert 2024 ARW Pond Microbiome study. Each MAG assembly has a separate ".fa" file. </p>
Coupled metalipidomics-metagenomics reveal structurally diverse sphingolipids produced by a wide variety of marine bacteria
<p><strong>Abstract</strong></p> <p><span>Microbial lipids, used as taxonomic markers and physiological indicators, have mainly been studied through cultivation. However, this approach is limited due to the scarcity of cultures of environmental microbes, thereby restricting insights into the diversity of lipids and their ecological roles. Addressing this limitation, here we apply metalipidomics combined with metagenomics in the Black Sea, classifying and tentatively identifying 1</span><span>623 lipid-like species across 18 lipid classes. We discovered over 200 novel, abundant, and structurally diverse sphingolipids in euxinic waters, including unique 1-deoxysphingolipids with long-chain fatty acids and sulfur-containing groups. </span><span>Sphingolipids were thought to be rare in bacteria and their molecular and ecological functions in bacterial membranes remain elusive. However, </span><span>genomic analysis focused on sphingolipid biosynthesis genes revealed that members of 38 bacterial phyla in the Black Sea can synthesize sphingolipids, representing a fourfold increase from previously known capabilities and accounting for up to 25% of the microbial community. These sphingolipids appear to be involved in oxidative stress response, cell wall remodeling and are associated with the metabolism of nitrogen-containing molecules. Our findings underscore the effectiveness of multi-omics approaches in exploring microbial chemical ecology.</span></p> <div><br></div> <p><strong>Repository content:</strong></p> <p><strong>1) metalipidome_sphingolipids.zip: </strong>includes source data and code scripts used for figures regarding metalipidome and sphingolipids abundance, classification and diversity in this study. Files are organized as follows and are associated with the corresponding parts of the manuscript: Fig. 1a, Fig. 1b, Fig. 2b, Fig. 2c, Fig. 2e, Fig. 2f, Fig. 2g, Fig. 2h, Fig. 4d, Supplementary Fig. 2, Supplementary Fig. 3.</p> <p><strong>2) Source data_major lipid classification.xlsx:</strong> includes original tables regarding metalipidome identification, abundance, precursor mass, retention time, classification as well as ID (name) in the molecular network.</p> <p><strong>3) Source data_sphingolipids information.xlsx:</strong> includes information about sphingolipids identification, precusor mass, retention time, peak intensity, elemental composition and etc.</p> <p><strong>4) Black_Sea_2013.code.tar.gz:</strong> contains the directory structure and code used for the metagenomics part of this project. Each directory contains a 'commands.sh', which contains the code to generate the content in that directory. Other shell and python scripts are always run from within 'commands.sh', with the exception of the files within the 'figures' directory which contains Jupyter labs and a python script that were run individually.</p> <p><strong>5) MAGs.tar.gz: </strong>all the MAGs generated by DAS Tool including CheckM and GTDB-Tk analyses (inside the 'binners' directory). Final taxonomic annotations of MAGs (see 'MAG2info.txt' file) are based on BAT annotatations with GTDB as a reference database, source data in the directory 'CAT_and_BAT_with_GTDB_refdb'.</p> <p><strong>6) abundance_profile.tar.gz:</strong> taxonomic annotation (based on CAT and BAT) and abundance of all scaffolds in the file 'big_table.txt'. Columns that start with 'mappings' are the read counts mapping to the scaffold in the sample from which it is assembled. Since the scaffolds were assembled per sample, only one of the 15 samples contains read mappings per scaffold. The last 15 columns (that start with 'BlackSea') are the depth per 1e8 mapped reads based on the all versus all mappings and were used for co-abundance analyses with sphingolipids. The file 'taxon2counts.txt' summarizes the taxonomic composition of the water column based on summing of the read mappings of the samples from which scaffolds were assembled (the 'mappings' columns in 'big_table.txt'), i.e. they represent all reads that could be associated with a certain taxon in that sample. The 'taxon2counts.txt' file used in Fig. 3c,d.</p> <p> </p> <p> </p> <p> </p>
Laboratory validation of a clinical metagenomic next-generation sequencing assay for respiratory virus detection and discovery
<p>This repository contains data and code used to analyze data for this manuscript: </p> <p><em>Laboratory validation of a clinical metagenomic next-generation sequencing assay for respiratory virus detection and discovery</em></p>
Metagenomic insights into microbial community structure and metabolism in alpine permafrost on the Tibetan Plateau
<h1>Microbes in Tibetan permafrost</h1> <p> </p> <blockquote> <p>This project repository associated with the following manuscript:</p> </blockquote> <ul> <li>Luyao Kang, Yutong Song, Rachel Mackelprang, Dianye Zhang, Shuqi Qin, Leiyi Chen, Linwei Wu, Yunfeng Peng and Yuanhe Yang*. Metagenomic insights into microbial community structure and metabolism in alpine permafrost on the Tibetan Plateau.</li> </ul> <h2>Abstract</h2> <p><span> Permafrost, characterized by its frozen soil, serves as a unique habitat for diverse microorganisms. Understanding these microbial communities is crucial for predicting the response of permafrost ecosystems to climate change. However, large-scale evidence regarding stratigraphic variations in microbial profiles remains limited. Here, we analyze microbial community structure and functional potential based on 16S <em>rRNA</em> gene amplicon sequencing and metagenomic data obtained from a </span><span>∼</span><span>1,000 km permafrost transect on the Tibetan Plateau. We find that microbial alpha diversity declines but beta diversity increases down the soil profile. Microbial assemblages are primarily governed by dispersal limitation and drift; the importance of drift decreases but that of dispersal limitation increases with soil depth. Moreover, genes related to reduction reactions (<em>e.g.</em>, ferric iron reduction, dissimilatory nitrate reduction, and denitrification) are enriched in the subsurface and permafrost layers. In addition, microbial groups involved in alternative electron accepting processes are more diverse and contribute highly to community-level metabolic profiles in the subsurface and permafrost layers, likely reflecting the lower redox potential and more complicated trophic strategies for microorganisms in deeper soils. Overall, these findings provide comprehensive insights into large-scale stratigraphic profiles of microbial community structure and functional potentials in permafrost regions.</span></p> <p> For the description of the files, please see README.md file.</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.
Supplementary material 15 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 (BOLD Data), at 3 minimum read depth. Underlined taxa were changed based on the distribution of taxa in the study zone
Supplementary material 10 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
Tests for normailty and equality of variance to establish whether conducting t-tests on the Dorper and Merino speccies and family level diversity data is appropriate.
Supplementary material 6 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 (BOLD Data). Underlined taxa were changed based on the distribution of taxa in the study zone ('*' indicates that the column contains no taxa).
Supplementary material 4 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 copy numbers of standard DNAs for 52 samples. Blue line indicates the linear regression between sequence reads and copy numbers. The regression lines are used to convert the MiSeq reads into the calculated copy numbers. Numbers in a grey region indicate sampling date. Note that regression slopes are different amongst samples, i.e. the number of sequence reads generated per eDNA copy is different amongst samples.
Supplementary material 11 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
Shapiro-Wilk and Levene's tests exploring the appropriateness of the data for use in ANOVA or Kruskal-Wallis tests.
Supplementary material 5 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 regression residuals and copy numbers of standard DNAs for 52 samples. Dashed line indicates zero residuals.
Supplementary material 3 from: Li Y, Evans NT, Renshaw MA, Jerde CL, Olds BP, Shogren AJ, Deiner K, Lodge DM, Lamberti GA, Pfrender ME (2018) Estimating fish alpha- and beta-diversity along a small stream with environmental DNA metabarcoding. Metabarcoding and Metagenomics 2: e24262. https://doi.org/10.3897/mbmg.2.24262
The longitudinal distance (upper triangular) and β- diversity (lower triangular) between sampling locations along Eagle Creek. :
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.