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12 results for “bacterial 16S rRNA gene”
Catalog of GenBank sequence read archive (SRA) entries of 16S and 18S rRNA genes from bacterial and protistan planktonic communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2011-2013
Microbial communities in the coastal Arctic Ocean experience extreme variability in organic matter and inorganic nutrients driven by seasonal shifts in sea ice extent and freshwater inputs. Lagoons border more than half of the Beaufort Sea coast and provide important habitats for migratory fish and seabirds; yet, little is known about the planktonic food webs supporting these higher trophic levels. To investigate seasonal changes in bacterial and protistan planktonic communities, amplicon sequences of 16S and 18S rRNA genes were generated from samples collected during periods of ice-cover (April), ice break-up (June), and open water (August) from shallow lagoons along the eastern Alaska Beaufort Sea coast from 2011 through 2013. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA530074 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA530074. This data package is associated with the following publication: Kellogg CTE, McClelland JW, Dunton KH and Crump BC (2019) Strong Seasonality in Arctic Estuarine Microbial Food Webs. Front. Microbiol. 10:2628. doi: 10.3389/fmicb.2019.02628 Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provided site codes (column "site_name" here) and collection dates (column "collection_date" here) in each dataset. Note that the site codes in this package are without hyphens (e.g. JAA) while site codes in the above environmental data package have hyphens (e.g. JA-A). Instead of citing this package which is jus
Comparing bacterial microbiome composition of Xylocopa species across populations using PacBio 16S rRNA gene sequencing
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KuafuPrimer: Machine learning facilitates the design of 16S rRNA gene primers with minimal bias in bacterial communities
<p>KuafuPrimer is a machine learning-aided method that learns community characteristics from several samples to design 16S rRNA gene primers with minimal bias for microbial communities. It is built on <strong>Python 3.9.0</strong>, <strong>Pytorch 1.12.0</strong>. Here are some large size files required to run KuafuPrimer, and users need to download and put them in correct directories before running the program.</p> <ol> <li>Silva_ref_data.zip: processed files of silva dataset that should be put in <code>Model_data/Silva_ref_data/</code>.</li> <li>DeepAnno16_publicated_model.zip: parameters of the trained DeepAnno16 model that should be put in <code>Model_data/DeepAnno16_publicated_model/</code> .</li> </ol> <p>For more information, please refer to https://github.com/zhanghaoyu9931/KuafuPrimer.</p>
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>
Lower St. Lawrence Estuary bacterial 16S rRNA gene diversity
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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.
Bacterial 16s rRNA gene amplicon data (V3-V4) and qPCR data
<p><span>Within a given species, considerable inter-individual, spatial, and temporal variation in the composition of the host microbiome exists. In group-living animals, social interactions homogenize microbiome composition among group members, nevertheless, divergence in microbiome composition among related groups arises. Such variation can result from deterministic and stochastic processes. Stochastic changes, or ecological drift, can occur among symbionts with potential for colonizing a host and within individual hosts, and drive divergence in microbiome composition among hosts or host groups. We tested whether ecological drift associated with dispersal and foundation of new groups cause divergence in microbiome composition between natal and newly formed groups in the social spider <em>Stegodyphus</em> <em>dumicola</em>. We simulated initiation of new groups and compared variation in microbiome composition among and within groups. Theory predicts a decrease in beta diversity with increasing group size, and we found that single founders harboured the highest diversity. Divergence in microbiome composition from the natal nest was mainly driven by a higher number of non-core symbionts. This suggests that stochastic divergence in host microbiomes can arise during the process of group formation by individual founders, which could explain the existence of among-group variation in microbiome composition in the wild. Consistent host-symbiont relationships in the species must then be maintained by other processes. Individual founders harboured higher relative abundances of non-core symbionts some of which are possible pathogens, compared with founders in small groups. These symbionts vary in occurrence with group size, indicating that group dynamics influence various core and non-core symbionts differently.</span></p>
Bacterial 16s rRNA gene amplicon data (V3-V4) and qPCR data
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Data from: Bacterial characterization of Beijing drinking water by flow cytometry and MiSeq sequencing of the 16S rRNA gene
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Development and evaluation of a 60-mer oligonucleotide microarray for profiling of biodegradation and bacterial 16S rRNA genes in diverse contaminated ecosystems
GEO Series GSE24353. Escherichia coli BL21; freshwater sediment metagenome; Rhodococcus jostii RHA1; Bordetella sp. IITR-02; Escherichia coli K-12; Sphingomonas sp. NM05; soil metagenome; synthetic construct; Escherichia coli DH5[alpha]. 17 samples. Type: Genome variation profiling by array.
Survey of the Collective 16s rRNA Genes From Bacterial Populations From Exercising and Non-exercising Participants
ClinicalTrials.gov study NCT02639455. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
A phylogenetic microarray targeting 16S rRNA genes from the bacterial division Acidobacteria
GEO Series GSE18711. Acidobacteriota; uncultured Acidobacteriota bacterium. 10 samples. Type: Other.
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Allen Brain Atlas
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