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78 results for “community data analysis”
Data from: Ion Torrent PGM as tool for fungal community analysis: a case study of endophytes in Eucalyptus grandis reveals high taxonomic diversity
The Kingdom Fungi adds substantially to the diversity of life, but due to their cryptic morphology and lifestyle, tremendous diversity, paucity of formally described specimens, and the difficulty in isolating environmental strains into culture, fungal communities are difficult to characterize. This is especially true for endophytic communities of fungi living in healthy plant tissue. The developments in next generation sequencing technologies are, however, starting to reveal the true extent of fungal diversity. One of the promising new technologies, namely semiconductor sequencing, has thus far not been used in fungal diversity assessments. In this study we sequenced the internal transcribed spacer 1 (ITS1) nuclear encoded ribosomal RNA of the endophytic community of the economically important tree, Eucalyptus grandis, from South Africa using the Ion Torrent Personal Genome Machine (PGM). We determined the impact of various analysis parameters on the interpretation of the results, namely different sequence quality parameter settings, different sequence similarity cutoffs for clustering and filtering of databases for removal of sequences with insufficient taxonomy. Sequence similarity cutoff values only had a marginal effect on the identified family numbers, whereas different sequence quality filters had a large effect (89 vs. 48 families between least and most stringent filters). Database filtering had a small, but statistically significant, effect on the assignment of sequences to reference sequences. The community was dominated by Ascomycota, and particularly by families in the Dothidiomycetes that harbor well-known plant pathogens. The study demonstrates that semiconductor sequencing is an ideal strategy for environmental sequencing of fungal communities. It also highlights some potential pitfalls in subsequent data analyses when using a technology with relatively short read lengths.
Lemonade Creek, Yellowstone National Park, USA - Microbial Community Analysis - Genome and Transcriptome Data
<p>Genome and Transcriptome data used for analysis of microbial community function over a diurnal cycle in Lemonade Creek, Yellowstone National Park, USA.</p> <p> </p> <p><code>mags.tar</code> Non-redundant metagenome data (genome assemblies, predicted genes, and gene functional annotations).</p> <p> </p> <p>In each directory are the the following files:</p> <p>- <code>*.mRNA.faa</code> protein sequences of protein-coding genes</p> <p>- <code>*.mRNA.fna</code> nucleotide sequences of protein-coding genes</p> <p>- <code>*.mRNA.gff3</code> genomic location of protein-coding genes</p> <p>- <code>*.mRNA.emapper.tsv</code> eggNOG-mapper annotations for the protein-coding genes</p> <p>- <code>*.mRNA.interproscan.gff3</code> InterProScan annotations for the protein-coding genes</p> <p> </p> <p>In the <code>prokaryote</code> directory there are the following files:</p> <p>- <code>*.rRNA.fna</code> nucleotide sequences of rRNA genes</p> <p>- <code>*.rRNA.gff3</code> genomic location of rRNA genes</p> <p>- <code>*.tRNA.fna</code> nucleotide sequences of tRNA genes</p> <p>- <code>*.tRNA.gff3</code> genomic location of tRNA genes</p> <p>- <code>*.other.fna</code> nucleotide sequences of other genes (i.e., CRISPR, ncRNA, oriC, regulatory_region, repeat_region, tmRNA - if any were predicted)</p> <p>- <code>*.other.gff3</code> genomic location of other genes</p> <p> </p> <p><strong>Eukaryotes</strong></p> <p>Five MAGs from other eukaryotes that were assembled from a coassembly of the Soil samples.</p> <p> </p> <p><strong>Prokaryotes</strong></p> <p>The final dereplicated prokaryote MAGs (at 95% ID). The two <code>*stats*</code> files list the taxonomic information (from <code>GTDB-Tk</code>), completeness (from <code>CheckM</code>), and assembly stats (from the <code>stats.sh</code> script from the <code>bbmap</code> package) for each of the prokaryotic MAGs + the number of predicted protein-coding and non-protein-coding genes predicted in each MAG.</p> <p> </p> <p><strong>Viruses</strong></p> <p>The final dereplicated viral MAGs and vOTUs.</p> <p> </p> <p> </p> <p> </p> <p><code>read_mapping.tar</code> Abundance results from metagenome and metatranscriptome read mapping analysis against the non-redundant metagenome data and predicted genes (respectively). This analysis includes the cyanidiophyceae reference nuclear and organelle genomes.</p> <p> </p> <p><strong>mags</strong></p> <p>Results from <code>bbmaps</code> alignment of metagenome reads against a database of non-redudant metagenome MAGs + cyanidiophyceae reference nuclear and organelle genomes. <code>CoverM</code> was used to calculate MAG abundances.</p> <p> </p> <p><strong>genes</strong></p> <p><code>Salmon</code> abundance quantification of PolyA and RiboMinus metatranscriptome reads mapped against the predicted genes in the non-redudant metagenome MAGs + cyanidiophyceae reference nuclear and organelle genomes.</p>
Jeanbille_et_al_2024_Exclusion_experiment_ANALYSIS: code and data for "Size exclusion experiment in a grassland field unravels top-down control of the soil fauna on microbial community assembly"
<p>Release of code and data associated with the publication "Size exclusion experiment in a grassland field unravels top-down control of the soil fauna on microbial community assembly".</p>
Jeanbille_et_al_2024_Exclusion_experiment_ANALYSIS: code and data for "Size exclusion experiment in a grassland field unravels top-down control of the soil fauna on microbial community assembly"
<p>Release of code and data associated with the publication "Size exclusion experiment in a grassland field unravels top-down control of the soil fauna on microbial community assembly".</p>
Jeanbille_et_al_2024_Exclusion_experiment_ANALYSIS: code and data for "Size exclusion experiment in a grassland field unravels top-down control of the soil fauna on microbial community assembly"
<p>Release of code and data associated with the publication "Size exclusion experiment in a grassland field unravels top-down control of the soil fauna on microbial community assembly".</p>
Jeanbille_et_al_2024_Exclusion_experiment_ANALYSIS: code and data for "Size exclusion experiment in a grassland field unravels top-down control of the soil fauna on microbial community assembly"
<p>Release of code and data associated with the publication "Size exclusion experiment in a grassland field unravels top-down control of the soil fauna on microbial community assembly".</p>
Data from: Quantifying (non)parallelism of microbial community change using multivariate vector analysis
<p>Parallel evolution of phenotypic traits is regarded as strong evidence for natural selection and has been studied extensively in a variety of taxa. However, we have limited knowledge of whether parallel evolution of host organisms is accompanied by parallel changes of their associated microbial communities (i.e., microbiotas), which are crucial for their hosts' ecology and evolution. Determining the extent of microbiota parallelism in nature can improve our ability to identify the factors that are associated with (putatively adaptive) shifts in microbial communities. While it has been emphasized that (non)parallel evolution is better considered as a quantitative continuum rather than a binary phenomenon, quantitative approaches have rarely been used to study microbiota parallelism. We advocate using multivariate vector analysis (i.e., phenotypic change vector analysis) to quantify direction and magnitude of microbiota changes and discuss the applicability of this approach for studying parallelism. We exemplify its use by reanalyzing gut microbiota data from multiple fish species that exhibit parallel shifts in trophic ecology. This approach provides an analytical framework for quantitative comparisons across host lineages, thereby providing the potential to advance our capacity to predict microbiota changes. Hence, we encourage the development and application of quantitative measures, such as multivariate vector analysis, to better understand the role of microbiota dynamics during their hosts' adaptive evolution, particularly in settings of parallel evolution.</p>
Data obtained by systematic review (codified data and meta-data) for: Influence of upwelling on coral reef benthic communities: a systematic review and meta-analysis
<p>Highly competitive coral reef benthic communities are acutely sensitive to changes in environmental parameters such as temperature and nutrient concentrations. Physical oceanographic processes that induce upwelling therefore act as drivers of community structure on tropical reefs. How upwelling impacts coral communities, however, is not fully understood; upwelling may provide a natural buffer against climate impacts and could potentially enhance the efficacy of spatial management and reef conservation efforts. This study employed a systematic review to assess existing literature linking upwelling with reef community structure, and a meta-analysis to quantify upwelling impact on the percentage cover of coral reef benthic groups. We show that upwelling has context-dependant effects on the cover of hard coral and fleshy macroalgae, with effect size and direction varying with depth, region and remoteness. Fleshy macroalgae was found to increase by 110% on inhabited reefs yet decrease by 56% around one well-studied remote island in response to upwelling. Hard coral cover was not significantly impacted by upwelling on inhabited reefs but increased by 150% when direct human pressures were absent. By synthesising existing evidence, this review facilitates adaptive and nuanced reef management which considers the influence of upwelling on reef assemblages.</p>
Molecular Typing of Community-acquired Pneumonia Based on Multiple-omic Data Analysis
ClinicalTrials.gov study NCT03093220. IPD Sharing: NO. Countries: 1. Publications: 8.
Data from: 18S rRNA metabarcoding diet analysis of the predatory fish community across seasonal changes in prey availability
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Data from: Community analysis of microbial sharing and specialization in a Costa Rican ant–plant–hemipteran symbiosis
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Data from: Belowground community responses to fire: meta-analysis reveals contrasting responses of soil microorganisms and mesofauna
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Data from: Multidimensional stable isotope analysis illuminates resource partitioning in a sub-Antarctic island bird community
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Data from: Social network analysis of psychological morbidity in an urban slum of Bangladesh: a cross-sectional study based on a community census
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Data from: Using routinely collected laboratory data to identify high rifampicin-resistant tuberculosis burden communities in the Western Cape Province, South Africa: a retrospective spatiotemporal analysis
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Data from: Ion Torrent PGM as tool for fungal community analysis: a case study of endophytes in Eucalyptus grandis reveals high taxonomic diversity
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Data from: Molecular diet analysis finds an insectivorous desert bat community dominated by resource sharing despite diverse echolocation and foraging strategies
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Data from: Community structure of a Neotropical bat fauna as revealed by stable isotope analysis: Not all species fit neatly into predicted guilds
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Data from: Paleocommunity analysis of the Burgess Shale Tulip Beds, Mount Stephen, British Columbia: comparison with the Walcott Quarry and implications for community variation in the Burgess Shale
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Data from: Quantifying (non)parallelism of microbial community change using multivariate vector analysis
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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
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.