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67 results for “ASV”
16S gene and ASV sequences of bacteria isolated from soil and the phyllosphere of Arabidopsis thaliana
<p>Data from "Induction of antibiotic specialized metabolism by co-culturing in a collection of phyllosphere bacteria" by Qi et al. </p> <p>- 16S gene sequences of bacteria isolated from soil and the phyllosphere of Arabidopsis thaliana in FASTA format. </p> <p>- Filtered OTU (ASV) table across all samples </p> <p>- ASV sequences</p>
ASV Tables inferred by DADA2 from the TARA Oceans v9 metabarcoding dataset
<p>Tables of exact amplicon sequence variants (ASVs) were generated from the TARA Oceans metabarcoding data (~766 million reads from 334 plankton samples, V9 region of the 18S rRNA gene, DOI: 10.5281/zenodo.15600) by DADA2 on a 2016 Macbook Pro. The R script used to process the data is included, alongside 3 ASV tables: the observed ASVs before chimera removal (sta), after consensus chimera removal (st.consensus, recommended) and after pooled chimera removal (st.nochim).</p> <p>The ASV tables are available in two formats. The first format is as matrices (rows named by sample, columns named by sequence variant) stored in RDS format -- these can be read back into R with the readRDS command. The second format is as biom-format files (json).</p>
Tara Pacific 16S rRNA ASV table for bacterial communities of crustose coralline algae from the Tuamotu archipelago (French Polynesia)
<p>This data is the result of the primary analysis of the 16S rRNA gene sequencing data collected from the CCA samples collected during the Tara Pacific expedition. The analysis was conducted using cutadapt/snakemake/dada2 and usearch. A full README is contained within the parent data upload (<a href="https://doi.org/10.5281/zenodo.4451892">https://doi.org/10.5281/zenodo.4451892</a>).</p>
Annual plant competition experiment results and associated mycobiome ASV tables
<p>Major theories regarding microbe-mediated plant community dynamics assume that plant species cultivate distinct microbial communities. However, few studies empirically assess the role of species-associated microbial community dissimilarity in plant competitive dynamics. In this study, we paired a competition experiment between eight annual forbs with a characterization of species-associated fungal communities to assess whether mycobiome dissimilarity is associated with pairwise competitive dynamics. Using a quantitative approach informed by modern coexistence theory, we found that fungal dissimilarity was correlated with both increased stabilizing niche differences and fitness inequalities. Additionally, we found that the probability of coexistence increased with mycobiome dissimilarity. When subsetting the community into different fungal functional groups (pathotrophs, saprotrophs, symbiotrophs), overall relationships between dissimilarity and competitive dynamics were independent of these functional groups. </p> <p>Synthesis: These results suggest that fungal community divergence may play an important role in mediating plant competitive dynamics. Although fungal community dissimilarity is associated with both niche and fitness differences, complex biotic and/or abiotic interactions belowground may result in an observed correlation between fungal community dissimilarity and plant coexistence. Ultimately, this study suggests a novel approach to better understanding how microbiome dissimilarity may impact host community dynamics.</p>
Tara Oceans (2009-2013) rDNA 18S V9 ASV table (DADA2) with nf-core/ampliseq
<p>This repository contains datasets describing the DADA2 ASVs generated from <em>Tara</em> Oceans 18S V9 rDNA data. The ASVs were generated using the nf-core workflow <a href="https://nf-co.re/ampliseq" target="_blank" rel="noopener">ampliseq</a>. Please refer to the readme file (README.html) for more details.</p>
Tara Oceans (2009-2013) rDNA 18S V4 ASV table (DADA2) with nf-core/ampliseq
<p>This repository contains datasets describing the DADA2 ASVs generated from <em>Tara</em> Oceans 18S V4 rDNA data. The ASVs were generated using the nf-core workflow <a href="https://nf-co.re/ampliseq" target="_blank" rel="noopener">ampliseq</a>. Please refer to the readme file (README.html) for more details.</p>
Maneuverability Characterization of Autonomous Surface Vehicle (ASV): ITTC zig-zag test dataset
<p>The two files refer to the same dataset: the .csv file is the raw format that is acquired by the ASV robotic platform. The .nc file contains the same data but in a standard format and with global and variable metadata generated using a standardization workflow (based on FAIR Principles) developed at CNR INM which uses controlled and standard vocabularies (ACDD and standard CF).</p> <p>The data refer to the execution of zig-zag maneuvers of the ASV following the ITTC standards</p>
Annual plant competition experiment results and associated mycobiome ASV tables
Open the record for dataset details and reuse information.
Mean relative abundance of fungal and bacterial ASV identified in vegetables and fruits.
<p>Supplementary table S1. Mean relative abundance of fungal and bacterial ASV identified in vegetables and fruits.</p>
Information related to diatom rDNA 18SV4 ASVs for SOMLIT-Astan and LTER-MC
<p>This repository contains the distribution, taxonomy and sequences of diatom rDNA 18SV4 ASVs from SOMLIT-Astan and LTER-MC.</p>
Database S1 - ASVs significantly impacted by chemical stressor treatments
<p>In the study by Wasimuddin et al. 2024 (https://www.biorxiv.org/content/10.1101/2024.04.20.590402v1), we identified Amplicon Sequence Variants (ASVs) that differed significantly in mean relative abundance due to the chemical stressor treatments. We performed differential abundance analysis with DESeq2, i.e. between Ctr-As, Ctr-Bx, Ctr-Tb using negative binomial-based Wald tests (P≤0.05). The individual ASVs can be retrieved in the Database S1, which consists of a R object that can be easily imported in R and further used for downstream analyses as follows.</p> <p>```<br>library(DESeq2)<br>DatabaseS1 <- readRDS("DatabaseS1.RDS")<br>names(DatabaseS1)<br>DatabaseS1[1]<br>```</p>
Amplicon sequence variants (ASV) of gut pathogens in hooded cranes and domestic geese
<p>Driven by habitat loss from anthropogenic activities, wintering migratory birds forage together with poultry in paddy fields, and thus impose risks of cross transmitting pathogens. To date, there is little evidence for such risks of pathogen transmission between wild birds and poultry. Using the high-throughput sequencing, we report on detected potential pathogens of both wild hooded cranes <em>Grus monacha</em> and sympatric domestic geese <em>Anser</em> <em>anser</em> <em>domesticus</em> during the wintering period and infer the possibility of cross-species pathogen transmission. The results revealed that the number of shared amplicon sequence variants (ASVs) of potential pathogens between the gut microbiota of the two species was low during the early wintering stage (17.2%; 5 ASVs shared) but increased to 56.3% (18 ASVs shared) during the late wintering stage. That is, potential pathogens in the gut microbial communities of the two species became more similar through co-foraging in paddy fields, supporting cross-transmission of pathogens between hooded cranes and domestic geese during the wintering period. Importantly, transmission appeared to be largely from wild hooded cranes to domestic geese, although some potential pathogens may have become specialized to the domestic goose in late wintering stage. Humans are also facing the risks of contracting these potential pathogens from migratory birds through their frequent contact with domestic poultry. It is, therefore, necessary to closely monitor this pathway of pathogen transmission from wild birds to domestic animals and even to humans.</p>
Community assembly amplicon sequences, with pipeline to get asv table for "Spatial structure drives compositional convergence between nutrient environments in experimental microbial communities"
<p>Community assembly amplicon sequences, with pipeline to get asv table for "Spatial structure drives compositional convergence between nutrient environments in experimental microbial communities"</p> <p> </p> <p>compressed FASTA files for 16s amplicon sequences relating to two separate projects, "Spatial structure drives compositional convergence between nutrient environments in experimental microbial communities" and "Habitat filtering leads to phylogenetic clustering in synthetic microbial communities". DADA22 pipeline is included, which pools all samples for better accuracy. A Julia script bioinfo.jl is then used to select only the samples relevant to spatial structure project.</p> <p> </p> <p>All csv filenames are appended with "_q" indicating an increase in the stringency of quality filtering parameters (also increasing minimum hamming distance used in DADA2 algorithm to 5) to produce a taxa table with a sensible number of ASVs (given a known number of input strains) with each ASV uniquely aligning to an individual sequence from colony PCR of said input strains.</p> <p> </p>
ASV tables of Myasthenia gravis (MG) and non-Myasthenia gravis
<p><span>Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and used to establishe a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods with nested leave-one-out cross-validation were trained using ASV taxon–based data and full ASV–based data to identify key ASVs in each data set. Overlapping key features extracted when XGBoost was trained using the full ASV–based and ASV taxon–based data were identified, and 31 high-importance ASVs (HIASVs) were obtained. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. </span></p>
SOMLIT-Astan time-series (2009-2016) rDNA 18S V4 ASV table (dada2)
<p>This repository contains a rDNA 18S V4 ASV table (astan-18sv4_dada2_v1.0.filtered.table.with.taxo.lulu.tsv.gz) for SOMLIT-Astan time-series (2009-2016). Each ASV, one per row, is described by the following fields: <strong>amplicon</strong> = ASV identifier; <strong>taxonomy</strong> = taxonomic path assigned to the ASV using IDTAXA; <strong>confidence</strong> = IDTAXA confidence scores for each taxonomic rank; <strong>sequence</strong> = ASV nucleic acid sequence; <strong>total</strong> = total number of reads for the entire dataset; <strong>spread</strong> = number of samples in which the ASV is detected; <strong>RAXXXXXX-X</strong> = number of reads in each of the 375 SOMLIT-Astan time-series samples. Sample ids contain information about the sampling date and the size fraction. The six digits after RA indicate the date (year, month and day), and the value after - indicate the size fraction, 02 for 0.2 to 3 µm and 3 for superior to 3 µm.</p> <p>How this table has been generated:</p> <p>The procedures used for DNA extraction and amplification of the 18S V4 region of the ribosomal operon are described in <a href="https://doi.org/10.1111/mec.16539">https://doi.org/10.1111/mec.16539</a>. The eukaryote-specific primers used were TAReuk454FWD1 (5’-CCAGCASCYGCGGTAATTCC-3’, Saccharomyces cerevisiae position 565‐584) and TAReukREV3 (5’-ACTTTCGTTCTTGATYRA-3’, Saccharomyces cerevisiae position 964‐981) (Stoeck et al., 2010). Raw sequences are available at the European Nucleotide Archive (ENA) under the project id PRJEB48571.</p> <p>The paired-end fastq files obtained from sequencing were demultiplexed and primers were removed using Cutadapt v2.8, filtering out untrimmed reads. Then, forward and reverse reads were trimmed at position 210 and reads with ambiguous nucleotides or with a maximum number of expected errors (maxEE) superior to 2 were filtered out using the function filterAndTrim() from the R package dada2 version 1.22 with R version 4.1.1 . For each run, error rates were defined using the function learnErrors(), reads were dereplicated using the function derepFastq() function and denoised using the dada() function with default options before being merged. Remaining chimaeras were removed using the function removeBimeraDenovo(). Only amplicon sequence variants (ASVs) with at least three reads in two samples were retained. ASVs were taxonomically assigned using IDTAXA with default parameters with the PR2 database version 4.14. Finally, the LULU curation approach was applied to the ASV table to remove remaining erroneous amplicons. For more details relative to the bioinformatic pipeline used to generate the ASV tables, see <a href="https://gitlab.sb-roscoff.fr/nhenry/rosko-naples-bioinfo">https://gitlab.sb-roscoff.fr/nhenry/rosko-naples-bioinfo</a>.</p>
CPAP vs ASV for Insomnia
ClinicalTrials.gov study NCT02365064. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Cardiovascular Improvements With MV ASV Therapy in Heart Failure
ClinicalTrials.gov study NCT01953874. IPD Sharing: Not stated. Countries: 2. Publications: 3.
Modified Adaptive Servoventilation (ASV) Compared to Conventional ASV
ClinicalTrials.gov study NCT01405313. IPD Sharing: NO. Countries: 1. Publications: 6.
Amplicon sequence variants (ASV) of gut pathogens in hooded cranes and domestic geese
Open the record for dataset details and reuse information.
ASV tables of Myasthenia gravis (MG) and non-Myasthenia gravis
Open the record for dataset details and reuse information.
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