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40 results for “16S sequencing data”
Data from: 16S rRNA amplicon sequencing for epidemiological surveys of bacteria in wildlife
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16S sequencing data of Asian female population with BMD reduction
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Data from: Bacterial characterization of Beijing drinking water by flow cytometry and MiSeq sequencing of the 16S rRNA gene
Flow cytometry (FCM) and 16S rRNA gene sequencing data are commonly used to monitor and characterize microbial differences in drinking water distribution systems. In this study, to assess microbial differences in drinking water distribution systems, 12 water samples from different sources water (groundwater, GW; surface water, SW) were analyzed by FCM, heterotrophic plate count (HPC), and 16S rRNA gene sequencing. FCM intact cell concentrations varied from 2.2 × 103 cells/mL to 1.6 × 104 cells/mL in the network. Characteristics of each water sample were also observed by FCM fluorescence fingerprint analysis. 16S rRNA gene sequencing showed that Proteobacteria (76.9–42.3%) or Cyanobacteria (42.0–3.1%) was most abundant among samples. Proteobacteria were abundant in samples containing chlorine, indicating resistance to disinfection. Interestingly, Mycobacterium, Corynebacterium, and Pseudomonas, were detected in drinking water distribution systems. There was no evidence that these microorganisms represented a health concern through water consumption by the general population. However, they provided a health risk for special crowd, such as the elderly or infants, patients with burns and immune-compromised people exposed by drinking. The combined use of FCM to detect total bacteria concentrations and sequencing to determine the relative abundance of pathogenic bacteria resulted in the quantitative evaluation of drinking water distribution systems. Knowledge regarding the concentration of opportunistic pathogenic bacteria will be particularly useful for epidemiological studies.
Data from: Assessment of a 16S rRNA amplicon Illumina sequencing procedure for studying the microbiome of a symbiont-rich aphid genus
The bacterial communities inhabiting arthropods are generally dominated by a few endosymbionts that play an important role in the ecology of their hosts. Rather than comparing bacterial species richness across samples, ecological studies on arthropod endosymbionts often seek to identify the main bacterial strains associated with each specimen studied. The filtering out of contaminants from the results and the accurate taxonomic assignment of sequences are therefore crucial in arthropod microbiome studies. We aimed here to validate an Illumina 16S rRNA gene sequencing protocol and analytical pipeline for investigating endosymbiotic bacteria associated with aphids. Using replicate DNA samples from 12 species (Aphididae: Lachninae, Cinara) and several controls, we removed individual sequences not meeting a minimum threshold number of reads in each sample and carried out taxonomic assignment for the remaining sequences. With this approach, we show that: i) contaminants accounted for a negligible proportion of the bacteria identified in our samples; ii) the taxonomic composition of our samples and the relative abundance of reads assigned to a taxon were very similar across PCR and DNA replicates for each aphid sample; in particular, bacterial DNA concentration had no impact on the results. Furthermore, by analysing the distribution of unique sequences across samples rather than aggregating them into operational taxonomic units (OTUs), we gained insight into the specificity of endosymbionts for their hosts. Our results confirm that Serratia symbiotica is often present in Cinara species, in addition to the primary symbiont, Buchnera aphidicola. Furthermore, our findings reveal new symbiotic associations with Erwinia and Sodalis-related bacteria. We conclude with suggestions for generating and analysing 16S rRNA gene sequences for arthropod endosymbiont studies.
Data from: Bacterial characterization of Beijing drinking water by flow cytometry and MiSeq sequencing of the 16S rRNA gene
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Data from: Highly divergent 16S rRNA sequences in ribosomal operons of Scytonema hyalinum (Cyanobacteria)
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Data from: Assessment of a 16S rRNA amplicon Illumina sequencing procedure for studying the microbiome of a symbiont-rich aphid genus
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16S rRNA sequencing data of cecal digesta in goats infusion of three short-chain fatty acids
GEO Series GSE232036. Capra hircus. 24 samples. Type: Other.
16S rRNA gene sequence data revealed effects of natural phenylethanoids on gut microbiota.
GEO Series GSE194127. Mus musculus. 30 samples. Type: Other.
16S rRNA sequences of gut microbiome and meta data
<p><b>Objective:</b>To understand the role of gut microbiome in influencing the pathogenesis of neuromyelitis optica spectrum disorders (NMOSD)among patients of south Indian origin.</p> <p><b>Methods:</b>In this case control study, stool and blood samples were collected from 39 NMOSD patients, including 17 with aquaporin 4 IgG antibodies (AQP4+) and 36 matched controls. 16S rRNA sequencing was used to investigate the gut microbiome. Peripheral CD4+ T cells were sorted in 12 healthy controls & 12 AQP4+NMOSD patients, RNA extracted, and immune gene expression analyzed using Nanostring nCounter human immunology kit code set.</p> <p><b>Results: </b>Microbiota community structure (beta-diversity) differed between AQP4+ NMOSD and healthy controls (p <0.001, pairwise PERMANOVA test). Linear discriminatory analysis effect size (LEfSe) identified several members of the microbiota that were altered in NMOSD patients, including an increase in <i>Clostridium bolteae </i>(effect size 4.23, pvalue 0.00007). <i>C.bolteae </i>was significantly more prevalent (p=0.02) amongAQP4-IgG + NMOSD (n= 8/17 subjects)compared to seronegative patients (n= 3/22) and was absent among healthy stool samples.<i>C bolteae </i>has a highly conserved glycerol uptake facilitator and related aquaporin protein(p59-71) that shares sequence homology with AQP4 peptide(p92-104), positioned within an immunodominant (AQP4specific)T cell epitope (p91-110).Presence of <i>C. bolteae</i> correlated with expression of inflammatory genes associated with both innate and adaptive immunity and particularly involved in plasma cell differentiation ,B cell chemotaxis and Th17 activation.</p> <p><b>Conclusion: </b>Our study described elevated levels of <i>C. bolteae </i>associated with AQP4+ NMOSD among Indian patients. It is possible that this organism may be causally related to the immunopathogenesis of this disease in susceptible individuals.</p> <p><b>Objective:</b>To understand the role of gut microbiome in influencing the pathogenesis of neuromyelitis optica spectrum disorders (NMOSD)among patients of south Indian origin.</p> <p><b>Methods:</b>In this case control study, stool and blood samples were collected from 39 NMOSD patients, including 17 with aquaporin 4 IgG antibodies (AQP4+) and 36 matched controls. 16S rRNA sequencing was used to investigate the gut microbiome. Peripheral CD4+ T cells were sorted in 12 healthy controls & 12 AQP4+NMOSD patients, RNA extracted, and immune gene expression analyzed using Nanostring nCounter human immunology kit code set.</p> <p><b>Results: </b>Microbiota community structure (beta-diversity) differed between AQP4+ NMOSD and healthy controls (p <0.001, pairwise PERMANOVA test). Linear discriminatory analysis effect size (LEfSe) identified several members of the microbiota that were altered in NMOSD patients, including an increase in <i>Clostridium bolteae </i>(effect size 4.23, pvalue 0.00007). <i>C.bolteae </i>was significantly more prevalent (p=0.02) amongAQP4-IgG + NMOSD (n= 8/17 subjects)compared to seronegative patients (n= 3/22) and was absent among healthy stool samples.<i>C bolteae </i>has a highly conserved glycerol uptake facilitator and related aquaporin protein(p59-71) that shares sequence homology with AQP4 peptide(p92-104), positioned within an immunodominant (AQP4specific)T cell epitope (p91-110).Presence of <i>C. bolteae</i> correlated with expression of inflammatory genes associated with both innate and adaptive immunity and particularly involved in plasma cell differentiation ,B cell chemotaxis and Th17 activation.</p> <p><b>Conclusion: </b>Our study described elevated levels of <i>C. bolteae </i>associated with AQP4+ NMOSD among Indian patients. It is possible that this organism may be causally related to the immunopathogenesis of this disease in susceptible individuals.</p>
16S rRNA sequences of gut microbiome and meta data
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16S sequencing data of plankton
<p>These are the original 16S DNA sequencing data based on the collected planktonic microorganisms, and classified by serial number. All data were double ended sequencing data.</p>
16S sequencing data of plankton
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16S rRNA sequencing data of ruminal bacterial profiles under grain-diet and hay-diet feeding patterns at six different timepoints in sheep
GEO Series GSE286149. Ovis aries. 96 samples. Type: Other.
16S rRNA sequencing data of ileal contents samples in goats infusion of three short-chain fatty acids
GEO Series GSE240818. Capra hircus. 12 samples. Type: Other.
16S Amplicon sequence variants (ASVs) data of NEREA Augmented Observatory
<p>Cleaned raw 16S paired-end sequences were imported into the QIIME2 pipeline v. 2022.2.0. Leftover primers and adapters’ sequences were removed through cutadapt. The amplicon sequence variants (ASV) table, which represent true biological sequences within each sample, was generated using the denoised-paired method including truncation, denoising, dereplication, and chimera filtering of the DADA2 (Divisive Amplicon Denoising Algorithm 2) plugin inside QIIME2. Default parameters were used with the exception of the forward and reverse sequence length (--p-trunc-len-f and --p-trunc-len-r), that were set to 220 and 180, respectively. For taxonomy classification, the V4-V5 region were extracted from the pre-formatted reference sequences and taxonomy file build on the SILVA 138 99% OTUS database and the vsearch v. 2.6.2 global alignment implemented in QIIME2 was used.</p>
RNA-Seq data of ruminal epithelial tissue and 16S rRNA sequencing data of rumen digesta in goats infusion of three short-chain fatty acids [RNA-seq]
GEO Series GSE221507. Capra hircus. 12 samples. Type: Expression profiling by high throughput sequencing.
RNA-Seq data of ruminal epithelial tissue and 16S rRNA sequencing data of rumen digesta in goats infusion of three short-chain fatty acids [16s rRNA-seq]
GEO Series GSE221508. Capra hircus. 24 samples. Type: Other.
RNA-Seq data of ruminal epithelial tissue and 16S rRNA sequencing data of rumen digesta in goats infusion of three short-chain fatty acids
GEO Series GSE221509. Capra hircus. 36 samples. Type: Expression profiling by high throughput sequencing; Other.
The molecular raw data of microbial communities associated with fish gut - in vitro model system using 16S rRNA Illumina amplicon sequencing approach.
<p>This data on the microbial communities and its diversity associated with fish gut. The samples were obtained from fish gut - in vitro model system using 16S rRNA Illumina amplicon sequencing approach.</p>
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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.