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196 results for “16S rRNA”

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zenodo36/100

Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.

Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.

opencc-by-4.0Feb 2017View details →

zenodo36/100

Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.

Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.

opencc-by-4.0Feb 2017View details →

dryad36/100

16S rRNA sequences from Siganidae (S. rivulatus and S. luridus) gut microbiome in their native (Red Sea) and invaded (Mediterranean Sea) ranges

The microbiome of invasive species is increasingly seen as a potential key factor of their ecological success, and this appears particularly true in herbivorous invaders whose digestive abilities rely on the microbes hosted in their gut. We characterized the gut microbiome of two invasive herbivorous fishes (Siganus rivulatus and Siganus luridus) in their native (Red Sea) and invaded (Levantine Sea and Northern Crete) ranges. We found that gut bacterial communities contain a higher taxonomic and phylogenetic diversity while becoming increasingly different from the native microbiome as the fishes move away from the native zone. This shift resulted in the homogenization of the microbiomes between individuals from the same species as well as between the two species. Firmicutes and Tenericutes reduced drastically in abundance while Proteobacteria and Bacteroidetes became more dominant in both species. This led to a modification of the functional potential of the gut microbiome associated with the metabolism of short-chanin fatty acids that also became more homogeneous in the invaded range. Altogether, our results suggest that the plasticity of the gut microbiome in Siganidae could be a key factor underlying their ecological success in Mediterranean ecosystems.

opencc-zeroFeb 2024View details →

dryad36/100

16S rRNA V4 gut microbiome of Leptonycteris yerbabuenae in Mexico

Migratory animals live in a world of constant change. Animals undergo many physiological changes preparing themselves for the migration. Although this field has been extensively studied over the last decades, we know relatively little about the seasonal changes that occur in the microbial communities that these animals carry in their guts. Here we assessed the V4 region of the 16S rRNA high-throughput sequencing data as a proxy to estimate microbiome diversity of Tequila Bats from fecal pellets and evaluate how the natural process of migration shapes the microbiome composition, and diversity. We collected samples from individual bats at two localities in the Dry Forest biome (Chamela and Coquimatlán) and one site at the end point of the migration in the Sonoran Desert (Pinacate). We found that the gut microbiome of the Tequila bats is largely dominated by Firmicutes and Proteobacteria. Our data also provide insights on how microbiome diversity shifts at the same site in consecutive years. Our study has demonstrated that both locality and year-to-year variation contribute to shaping the composition, overall diversity, and the 'uniqueness' of the gut microbiome of migratory nectar-feeding female bats with localities from the dry forest biome looking more like each other when compared to the desert biome. In terms of beta diversity, our data show a stratified effect in which the samples locality was the strongest factor influencing the gut microbiome, but with significant variation between consecutive years at the same locality.

opencc-zeroNov 2021View details →

zenodo36/100

ARF / YA16Sdb collection of curated 16S rRNA alleles

A collection of 16s rRNA alleles filtered by read quality and annotation quality. This repository contains 376,934 full-length 16s rRNA alleles with validated (by majority-rules) taxonomic annotations. These are a subset of 727,361 full-length 16s rRNA alleles. The pipeline used to create this repo is also available at: github.com/jgolob/arf

opencc-by-4.0Jul 2022View details →

zenodo36/100

Row fastq files generated by 16S rRNA sequencing for the metabarcoding analysis of Hidalgo-Villeda et al. paper

Fastq files used for the microbiome profiling of the terminal ileal and caecum content. Sequencing of the terminal ileal content was performed at the Institute Hospitalo-Universitaire Méditerannée Infection, Marseille, France. Sequencing of the caecum content was performed at Genoscreen, Lille, France The sequencing methodology is described on the methods part of the paper "A physiological mouse model of severe acute malnutrition reveals prolonged dysbiosis and altered immunity under nutritional intervention, Hidalgo-Villeda et al.". The metadata.xlsx table present the description and correspondance of each fastq files.

opencc-by-4.0Sep 2022View details →

zenodo36/100

Modena E. coli (16S and 23S) and S. cerevisiae (18S and 25S) rRNA test dataset samples 1 to 5

Samples 1 to 5 of the rRNA test dataset accompanying the article "Nanopore based computational method for detecting a wide range of epigenetic/epitranscriptomic modifications". Each sample comprises seven subsamples with coverage depths ranging from 10 to 2000. If you have any questions about the content of this dataset, feel free to contact Sini&scaron;a Biđin at sinisa@heuristika.hr.

opencc-by-4.0Jul 2024View details →

zenodo36/100

Modena E. coli (16S and 23S) and S. cerevisiae (18S and 25S) rRNA test dataset samples 6 to 10

Samples 6 to 10 of the rRNA test dataset accompanying the article "Nanopore based computational method for detecting a wide range of epigenetic/epitranscriptomic modifications". Each sample comprises seven subsamples with coverage depths ranging from 10 to 2000. If you have any questions about the content of this dataset, feel free to contact Sini&scaron;a Biđin at sinisa@heuristika.hr.

opencc-by-4.0Jul 2024View details →

zenodo36/100

FIG. 1 in A journey through Cyanobacteria in Brazil: a review of novel genera and 16S rRNA sequences

FIG. 1. — Flowchart of search methods for identification and selection of studies.

opencc-zeroJul 2024View details →

zenodo36/100

KuafuPrimer: Machine learning facilitates the design of 16S rRNA gene primers with minimal bias in bacterial communities

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 Python 3.9.0, Pytorch 1.12.0. 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. <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> For more information, please refer to https://github.com/zhanghaoyu9931/KuafuPrimer.

opencc-by-4.0Sep 2024View details →

zenodo36/100

Screening of bacterial diversity by 16S rRNA metabarcoding in Orbicella faveolata healthy and with Black Band Disease

Screening of bacterial diversity by 16S rRNA metabarcoding in Orbicella faveolata healthy and with Black Band Disease

opencc-by-4.0Oct 2024View details →

dryad36/100

16s rRNA sequencing of Wolbachia-infected and uninfected Drosophila male gut

Wolbachia are the most widely distributed intracellular bacteria, and their most common effect on host phenotype is cytoplasmic incompatibility (CI). A variety of models have been proposed to decipher the molecular mechanism of CI, in which the HM (host modification) model predicts that the effectors of Wolbachia play an important role in sperm modification. However, due to the complexity of spermatogenesis and cell-type heterogeneity in the testis, we still do not know whether Wolbachia have different effects on cells at different stages of spermatogenesis, nor whether these effects are linked with CI. Therefore, we used single-cell RNA sequencing to analyze gene expression profiles in the adult male Drosophila testes with or without Wolbachia infection. We found that Wolbachia significantly affected the proportion of different types of germ cells and affected multiple metabolic pathways in germ cells. Most importantly, Wolbachia had the greatest impact on germline stem cells (GSCs), resulting in the dysregulation expression of genes related to nucleosome assembly and CI, Wolbachia infection also influenced the histone-to-protamine transition in the late stage of sperm development. These results suggest that future studies of Wolbachia-mediated sperm modification should focus more on cells in the early stages of spermatogenesis.

opencc-zeroJan 2023View details →

dryad36/100

Reference data and simulated communities for 16S rRNA GCN prediction

16S rRNA gene has been widely used in microbial diversity studies to determine the community composition and structure. 16S rRNA gene copy number (16S GCN) varies among microbial species and this variation introduces biases to the relative cell abundance estimated using 16S rRNA read counts. To correct the biases, methods (e.g., PICRUST2) have been developed to predict 16S GCN. 16S GCN predictions come with inherent uncertainty, which is often ignored in the downstream analyses. However, a recent study suggests that the uncertainty can be so great that copy number correction is not justified in practice. Despite the significant implications in 16S rRNA-based microbial diversity studies, the uncertainty associated with 16S GCN predictions has not been well characterized. Here we develop a novel method to better model and capture the inherent uncertainty. Using cross-validation, we show that our method provides robust confidence estimates for the GCN predictions and outperforms PICRUST2 in both precision and recall. We found that 16S GCN correction should improve compositional and functional profiles estimated using 16S rRNA reads. On the other hand, we found that GCN variation has limited impacts on PCoA, PERMANOVA and random forest test, and 16S rRNA GCN correction is unnecessary in beta-diversity analyses.

opencc-zeroJun 2023View details →

dryad36/100