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196 results for “16S rRNA”
16S rRNA gene sequencing data from: Breastmilk IgG engages the neonatal immune system to instruct immune responses to gut antigens
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................................................................................................................................................. Fig. 5. Phylogenetic tree based on 16S rRNA sequences (a) and gltA sequences (b) showing the position of strains R1T, R3, R4 and R6 in relation to the known Bartonella spp. The tree was rooted by using Brucella abortus (a) and Sinorhizobium meliloti (b) as the outgroup. in Bartonella schoenbuchii sp. nov., isolated from the blood of wild roe deer.
................................................................................................................................................. Fig. 5. Phylogenetic tree based on 16S rRNA sequences (a) and gltA sequences (b) showing the position of strains R1T, R3, R4 and R6 in relation to the known Bartonella spp. The tree was rooted by using Brucella abortus (a) and Sinorhizobium meliloti (b) as the outgroup.
Data from: Biogeography in a continental island: population structure of the relict endemic centipede Craterostigmus tasmanianus (Chilopoda, Craterostigmomorpha) in Tasmania using 16S rRNA and COI
We used 16S ribosomal RNA (rRNA) and cytochrome c oxidase subunit I (COI) sequence data to investigate the population structure in the centipede Craterostigmus tasmanianus Pocock, 1902 (Chilopoda: Craterostigmomorpha: Craterostigmidae) and to look for possible barriers to gene flow on the island of Tasmania, where C. tasmanianus is a widespread endemic. We first confirmed a molecular diagnostic character in 28S rRNA separating Tasmanian Craterostigmus from its sister species Craterostigmus crabilli (Edgecombe and Giribet 2008) in New Zealand and found no shared polymorphism in this marker for the 2 species. In Tasmania, analysis of molecular variance analysis showed little variation at the 16S rRNA and COI loci within populations (6% and 13%, respectively), but substantial variation (56% and 48%, respectively) among populations divided geographically into groups. We found no clear evidence of isolation by distance using a Mantel test. Bayesian clustering and gene network analysis both group the C. tasmanianus populations in patterns which are broadly concordant with previously known biogeographical divisions within Tasmania, but we did not find that genetic distance varied in a simple way across cluster boundaries. The coarse-scale geographical sampling on which this study was based should be followed in the future by sampling at a finer spatial scale and to investigate genetic structure within clusters and across cluster boundaries.
Data from: 16S rRNA amplicon sequencing for epidemiological surveys of bacteria in wildlife
The human impact on natural habitats is increasing the complexity of human-wildlife interactions and leading to the emergence of infectious diseases worldwide. Highly successful synanthropic wildlife species, such as rodents, will undoubtedly play an increasingly important role in transmitting zoonotic diseases. We investigated the potential for recent developments in 16S rRNA amplicon sequencing to facilitate the multiplexing of the large numbers of samples needed to improve our understanding of the risk of zoonotic disease transmission posed by urban rodents in West Africa. In addition to listing pathogenic bacteria in wild populations, as in other high-throughput sequencing (HTS) studies, our approach can estimate essential parameters for studies of zoonotic risk, such as prevalence and patterns of coinfection within individual hosts. However, the estimation of these parameters requires cleaning of the raw data to mitigate the biases generated by HTS methods. We present here an extensive review of these biases and of their consequences, and we propose a comprehensive trimming strategy for managing these biases. We demonstrated the application of this strategy using 711 commensal rodents, including 208 Mus musculus domesticus, 189 Rattus rattus, 93 Mastomys natalensis, and 221 Mastomys erythroleucus, collected from 24 villages in Senegal. Seven major genera of pathogenic bacteria were detected in their spleens: Borrelia, Bartonella, Mycoplasma, Ehrlichia, Rickettsia, Streptobacillus, and Orientia. Mycoplasma, Ehrlichia, Rickettsia, Streptobacillus, and Orientia have never before been detected in West African rodents. Bacterial prevalence ranged from 0% to 90% of individuals per site, depending on the bacterial taxon, rodent species, and site considered, and 26% of rodents displayed coinfection. The 16S rRNA amplicon sequencing strategy presented here has the advantage over other molecular surveillance tools of dealing with a large spectrum of bacterial pathogens without requiring assumptions about their presence in the samples. This approach is therefore particularly suitable to continuous pathogen surveillance in the context of disease-monitoring programs.
Lower St. Lawrence Estuary bacterial 16S rRNA gene diversity
<p>The Estuary and Gulf of St. Lawrence (EGSL) in eastern Canada is among the largest and most productive coastal ecosystems in the world.<b> </b>Very little information on bacterial diversity exists, hampering our understanding of the relationships between bacterial community structure and biogeochemical function in the EGSL. During the productive spring period, we investigated free-living and particle-associated bacterial communities across the stratified waters of the Lower St. Lawrence Estuary, including the particle-rich surface and bottom boundary layers. Modeling of community structure based on 16S rRNA gene and transcript diversity identified bacterial assemblages specifically associated with four habitat types defined by water mass (upper water or lower water column) and size fraction (free-living or particle-associated). Assemblages from the upper waters represent sets of co-occurring bacterial populations that are widely distributed across Lower St. Lawrence Estuary surface waters., and likely key contributors to organic matter degradation during the spring. In addition, we provide strong evidence that particles in deep hypoxic waters and the bottom boundary layer support a metabolically-active bacterial community that is compositionally distinct compared to surface particles and the free-living communities. Among the distinctive features of the bacterial assemblage associated with lower water particles was the presence of uncultivated lineages of Deltaproteobacteria, including marine Myxobacteria. Overall, these results provide an important ecological framework for further investigations of the biogeochemical contributions of bacterial populations in this important coastal marine ecosystem.</p>
Greengenes May 2013 unmasked 16S rRNA alignments
<p>These data are derived from the raw PyNast aligned 16S rRNA files from Greengenes. The Jupyter (Python 3) notebook used and the original data (provided by Greg Caporaso) are also included.</p>
FIGURE 6. Bayesian inference tree derived from 16S rRNA for all species. The nodal numbers are posterior probability values. Only values above 50 in Cophecheilus bamen, a new genus and species of labeonine fishes (Teleostei: Cyprinidae) from South China
FIGURE 6. Bayesian inference tree derived from 16S rRNA for all species. The nodal numbers are posterior probability values. Only values above 50% are given.
Extended Data Fig. 2-27 Geographical information of bioinformatic predicted samples based on the analysis of 16S rRNA gene in four PE degrading bacteria
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16S rRNA gene data for aerobic BTEX-degrading enrichments exposed to sulfonamide polyfluorinated substances in fire-fighting foams and transformation products
<p>Per- and polyfluoroalkyl substances (PFASs) from aqueous film forming foams (AFFFs) can hinder bioremediation of co-contaminants, such as trichloroethene (TCE) and benzene, toluene, ethylbenzene, and xylene (BTEX). Anaerobic dechlorination can require bioaugmentation of <em>Dehalococcoides</em> and for BTEX, oxygen is often sparged to stimulate in-situ aerobic biodegradation. We tested PFAS inhibition to TCE and BTEX bioremediation by exposing an anaerobic TCE-dechlorinating co-culture, an aerobic BTEX-degrading enrichment culture, and an anaerobic toluene-degrading enrichment culture to n-dimethyl perfluorohexane sulfonamido amine (AmPr-FHxSA), perfluorohexane sulfonamide (FHxSA), perfluorohexane sulfonic acid (PFHxS), or non-fluorinated surfactant sodium dodecyl sulfate (SDS). The anaerobic TCE-dechlorinating co-culture was resistant to individual PFASs exposures but was inhibited by >1,000x diluted AFFF. FHxSA and AmPr-FHxSA inhibited the aerobic BTEX-degrading enrichment. The anaerobic toluene-degrading enrichment was not inhibited by AFFF or individual PFASs. Increases in amino acids in the anaerobic TCE-dechlorinating co-culture compared to the control indicated stress response, while the BTEX culture exhibited lower concentrations of all amino acids upon exposure to most surfactants (both fluorinated and non-fluorinated) compared to the control. These data suggest the main mechanisms of microbial toxicity are related to interactions with cell membrane synthesis as well as protein stress signaling.</p>
Fig. 1. Neighbour-joining phylogenetic tree derived using 16S rRNA gene sequences, showing the relationships between strain HNM0687T in Gordonia mangrovi sp. nov., a novel actinobacterium isolated from mangrove soil in Hainan
Fig. 1. Neighbour-joining phylogenetic tree derived using 16S rRNA gene sequences, showing the relationships between strain HNM0687T and other type strains of the genus Gordonia. Only values above 50% are shown. Asterisks represent clades that were also recovered by the maximum-likelihood and maximum-parsimony methods. Bar, one nucleotide substitution per 100 nucleotides.
16S rRNA sequence database
<p> This 16S rRNA database was curated for honeybee gut microbiome.</p>
16s rRNA and copLAB partial sequences of copper resistant Xanthomonas spp. isolated from agrochemical impacted crucifer fields in Trinidad
<p>Sanger sequence generated partial sequences of copLAB genes present in copper resistant Xanthomonas spp associated with leaf lesion and 16s rRNA sequences used for bacterial genera identification. Part of an upcoming manuscript, will be linked within document.</p>
Caulerpa-associated bacterial 16S rRNA in response to environmental stress
<p>This dataset contains data from a<span>lgal-associated bacteria from the green macroalgae, <i>Caulerpa, </i>from the paper " Morrissey, K.L. et al. (2021) Impacts of environmental stress on resistance and resilience of algal-associated bacterial communities. Ecology and Evolution". </span>The experiment investigates the effects of a factorial combination of nutrient and temperature stress on the bacterial communities. We have also assessed the<span> resistance and resilience of the algal-associated microbiota to environmental stress, using community dissimilarity metrics. </span></p> <p><span>Bacteria were characterised using the 16S rRNA gene and the community compositions were compared between </span>different parts of the algal thallus (endo-, epi- and rhizomicrobiome).</p> <p><span>The results of this study provide evidence that nutrient enrichment has a significant influence on the taxonomic and functional structure of the epimicrobiota, with a low community resistance index observed for both. Temperature and nutrient stress had a significant effect on the rhizomicrobiota taxonomic composition, exhibiting the lowest overall resistance to change. The functional performance of the rhizomicrobiota had low resilience to the combination of stressors, indicating potential additive effects. Interestingly, the endomicrobiota had the highest overall resistance, yet the lowest overall resilience to environmental stress. This further contributes to our understanding of algal microbiome dynamics in response to environmental changes.</span></p>
Supplementary material 1 from: Lee G-E, Han T, Jeong J, Kim S-H, Park IG, Park H (2015) Molecular phylogeny of the genus Dicronocephalus (Coleoptera, Scarabaeidae, Cetoniinae) based on mtCOI and 16S rRNA genes. ZooKeys 501: 63-87. https://doi.org/10.3897/zookeys.501.8658
COI sequences dataset of Dicronocephalus species in this study.: Explanation note: This COI data includes 50 individual sequences of the examined Dicronocephalus species and subspecies in this study
Supplementary material 3 from: Lee G-E, Han T, Jeong J, Kim S-H, Park IG, Park H (2015) Molecular phylogeny of the genus Dicronocephalus (Coleoptera, Scarabaeidae, Cetoniinae) based on mtCOI and 16S rRNA genes. ZooKeys 501: 63-87. https://doi.org/10.3897/zookeys.501.8658
The combined dataset of COI and 16S rRNA of Dicronocephalus species in this study.: Explanation note: There is the concatenated sequences of COI and 16S rRNA genes correspondence with each sample.
Supplementary material 2 from: Lee G-E, Han T, Jeong J, Kim S-H, Park IG, Park H (2015) Molecular phylogeny of the genus Dicronocephalus (Coleoptera, Scarabaeidae, Cetoniinae) based on mtCOI and 16S rRNA genes. ZooKeys 501: 63-87. https://doi.org/10.3897/zookeys.501.8658
16S rRNA sequences data set of Dicronocephalus species in this study.: Explanation note: This 16S rRNA data includes 46 individual sequences of the examined Dicronocephalus species in this study.
Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota
<div> <p><span><span>Background</span></span><span><span>: Gut dysbiosis has been associated with colorectal cancer (CRC), the third most prevalent cancer in the world. </span><span>This study compares microbiota taxonomic and abundance results obtained by 16S rRNA gene sequencing (16S) and whole shotgun metagenomic sequencing to investigate their reliability for bacteria profiling. The experimental design included 156 human stool samples from healthy controls, advanced (high-risk) colorectal lesion patients (HRL), and CRC cases</span><span>, with each sample sequenced using both 16S and shotgun methods</span><span>. We thoroughly compared both sequencing technologies at the species, genus, and family annotation levels, the abundance differences in these taxa, sparsity, alpha and beta diversities, ability to train prediction models, and the similarity of the microbial signature derived from these models.</span></span><span> </span></p> </div> <div> <p><span><span>Results</span></span><span><span>: </span><span>As expected, the results showed that </span></span><span><span>16S detects only part of the gut microbiota community revealed by shotgun, although some genera were only profiled by 16S. The </span></span><span><span>16S </span><span>abundance data was sparser and </span><span>exhibited</span><span> lower alpha diversity. In lower taxonomic ranks, shotgun and 16S highly differed, </span><span>partially</span><span> due to a disagreement in reference databases. When considering only shared taxa, the abundance was positively correlated between the two strategies. We also found a moderate correlation between the shotgun and 16S alpha-diversity measures, as well as their </span><span>PCoAs</span><span>. </span><span>Regarding</span><span> the machine learning models, only some of the shotgun models showed some degree of predictive power in an independent test set, but we could not </span><span>demonstrate</span><span> a clear superiority of one technology over the other. Microbial signatures from both sequencing techniques reveal</span><span>ed</span><span> taxa previously associated with CRC development, e.g., </span></span><span><span>Parvimonas</span><span> micra</span></span><span><span>.</span></span><span> </span></p> </div> <div> <p><span><span>Conclusions</span></span><span><span>: </span></span><span><span>Shotgun and 16S sequencing provide two different lenses to examine microbial communities.</span><span> While we have </span><span>demonstrated</span><span> that they can unravel common patterns (including microbial signatures), </span><span>shotgun often gives</span><span> a more detailed snapshot than 16S, both in depth and breadth. </span><span>Instead</span><span>,</span> <span>16S will tend to show only part of the picture, giving greater weight to dominant bacteria in a sample.</span> <span>Therefore, w</span><span>e recommend choosing one or another sequencing technique before launching a study.</span><span> Specifically, </span><span>s</span><span>hotgun sequencing is preferred for stool microbiome samples and in-depth analyses, while 16S is </span><span>more </span><span>suitable for tissue samples</span><span> and</span><span> studies with </span><span>targeted</span> <span>aims</span><span>.</span></span><span> </span></p> </div>
The molecular investigation of Blood cockles (Anadara granosa) associated bacterial communities using 16S rRNA sequencing
<p>This data on the bacterial communities and its diversity associated with <em>Anadara granosa</em>. The <em>Anadara granosa</em> samples were obtained from two major estuaries in Penang, Malaysia using a culture dependent and 16S rRNA sequencing approach.</p>
16S rRNA and ITS raw data
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FIGURE 5. 16S rRNA Bayesian phylogeny with a in New cyanobacterium Nodosilinea svalbardensis sp. nov. (Prochlorotrichaceae, Synechococcales) isolated from alluvium in Mimer river valley of the Svalbard archipelago
FIGURE 5. 16S rRNA Bayesian phylogeny with a total of 181 sequences from order Synechococcales/Gloeobacterales, including 75 sequences from genus Nodosilinea, showing evolutionary lineage corresponds to new species Nodosilinea svalbardensis, Symbol "-" show support less than 50% on representative nodes. The type sequences of established Nodosilinea spp. are given in bold font. Taxa in the quotation mark needs to be revised.
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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.