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32 results for “ribosomal RNA genes”
Fig. 1 in Phylogenetic position of the freshwater fish trypanosome, Trypanosoma ophiocephali (Kinetoplastida) inferred from the complete small subunit ribosomal RNA gene sequence
Fig. 1 The neighbor-joining tree of aquatic trypanosomes constructed from complete small subunit ribosomal RNA (SSrRNA) sequences indicating the systematic position of T. ophiocephali and phylogenetic relationships among the aquatic trypanosomes whose sequences are available. T. lewisi, T. theileri, and T. avium are taken as the outgroup. Bootstrap values are shown for the maximum parsimony/neighborjoining/Bayes analyses
Figure 6 in Genetic divergences of South and Southeast Asian frogs: a case study of several taxa based on 16S ribosomal RNA gene data with notes on the generic name Fejervarya
Figure 6. Maximum likelihood (ML) tree of bufonid frogs based on nucleotide sequences of the mitochondrial 16S rRNA gene with Leptophryne borbonica as an outgroup. The bootstrap support (>50%) is indicated at nodes in the order of ML (500) replicates. Asterisks represent Bayesian posterior probability (BPP; * ≥95%). Specimens examined in this study are indicated by boldface type.
Figure 2 in Genetic divergences of South and Southeast Asian frogs: a case study of several taxa based on 16S ribosomal RNA gene data with notes on the generic name Fejervarya
Figure 2. Maximum likelihood (ML) tree based on nucleotide sequences of the mitochondrial 16S rRNA gene from 88 haplotypes of frogs (Table 1), with Xenopus laevis as an outgroup. Bootstrap support (>50%) is indicated at nodes in the order of ML (1000) replicates. Asterisks represent Bayesian posterior probability (BPP; * ≥95%).
mTAGs: taxonomic profiling using degenerate consensus reference sequences of ribosomal RNA gene
<p>mTAGs is a tool for the taxonomic profiling of metagenomes. It detects sequencing reads belonging to the small subunit of the ribosomal RNA (SSU-rRNA) gene and annotates them through the alignment to full-length degenerate consensus SSU-rRNA reference sequences. The tool is capable of processing single-end and pair-end metagenomic reads, takes advantage of the information contained in any region of the SSU-rRNA gene and provides relative abundance profiles at multiple taxonomic ranks (Domain, Phylum, Class, Order, Family, Genus and OTUs defined at a 97% sequence identity cutoff).</p>
A Pleistocene legacy of gene pools, ecodemes and admixtures of Stuckenia pectinata (L.) Börner as evidenced from microsatellites, complete chloroplast genomes and ribosomal RNA cistron (Europe, Africa)
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FIGURE 6. Fast distance based analysis tree for 16s ribosomal RNA gene. Note total genetic uniformity among 28 in Billions and billions sold: Pet-feeder crickets (Orthoptera: Gryllidae), commercial cricket farms, an epizootic densovirus, and government regulations make for a potential disaster
FIGURE 6. Fast distance based analysis tree for 16s ribosomal RNA gene. Note total genetic uniformity among 28 individuals of G. locorojo from eight "localities" on three continents. See Appendix A for specimen source data.
FIGURE 6 in Phylogenetic analysis of the Prionini (Coleoptera: Cerambycidae: Prioninae) from China based on mitochondrial ribosomal RNA genes and Cytochrome oxidase I gene
FIGURE 6. Phylogeny of the Chinese Prionini based on combined sequences of 12S rRNA and 16S rRNA. A: Bootstrap 50% majority-rule consensus tree of distance method by PAUP* with bootstrap values (%), Wtd. S.S. = 0.0496, APSD = 3.764, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; B: Bootstrap 50% majority-rule consensus tree of maximum likelihood method by PAUP* with bootstrap values (%), -Ln likelihood = 4527.5195, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; C: Bootstrap 50% majority-rule consensus tree of parsimony method by PAUP* with bootstrap values (%), tree length = 792, CI = 0.7109, RI = 0.3639, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; D: Bayesian tree by MrBayes with Bayesian posterior probabilities (%), the scale bar in the bottom left corner of the tree meaning 0.1 nucleotide substitutions per site.
FIGURE 4 in Phylogenetic analysis of the Prionini (Coleoptera: Cerambycidae: Prioninae) from China based on mitochondrial ribosomal RNA genes and Cytochrome oxidase I gene
FIGURE 4. Phylogeny of the Chinese Prionini based on partial sequences of 16S rRNA (excluding Priotyrannus closteroides)
FIGURE 3 in Phylogenetic analysis of the Prionini (Coleoptera: Cerambycidae: Prioninae) from China based on mitochondrial ribosomal RNA genes and Cytochrome oxidase I gene
FIGURE 3. Phylogeny of the Chinese Prionini based on partial sequences of 16S rRNA. A: Bootstrap 50% majority-rule consensus tree of distance method by PAUP* with bootstrap values (%), Wtd. S.S. = 0.1041, APSD = 5.454, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; B: Bootstrap 50% majority-rule consensus tree of maximum likelihood method by PAUP* with bootstrap values (%), -Ln likelihood = 1754.1334, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; C: Bootstrap 50% majority-rule consensus tree of parsimony method by PAUP* with bootstrap values (%), tree length = 280, CI = 0.7429, RI = 0.4586, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; D: Bayesian tree by MrBayes with Bayesian posterior probabilities (%), the scale bar in the bottom left corner of the tree meaning 0.1 nucleotide substitutions per site.
FIGURE 2 in Phylogenetic analysis of the Prionini (Coleoptera: Cerambycidae: Prioninae) from China based on mitochondrial ribosomal RNA genes and Cytochrome oxidase I gene
FIGURE 2. Phylogeny of the Chinese Prionini based on partial sequences of 12S rRNA (excluding Priotyrannus closteroides). A: Bootstrap 50% majority-rule consensus tree of distance method by PAUP* with bootstrap values, Wtd. S.S. = 0.0266, APSD = 3.139, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; B: Bootstrap 50% majority-rule consensus tree of maximum likelihood method by PAUP* with bootstrap values, -Ln likelihood = 2474.8359, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; C: Bootstrap 50% majority-rule consensus tree of parsimony method by PAUP* with bootstrap values, tree length =431, CI = 0.7425, RI = 0.4158, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; D: Bayesian tree by MrBayes with Bayesian posterior probabilities, the scale bar in the bottom left corner of the tree meaning 0.1 nucleotide substitutions per site.
FIGURE 1 in Phylogenetic analysis of the Prionini (Coleoptera: Cerambycidae: Prioninae) from China based on mitochondrial ribosomal RNA genes and Cytochrome oxidase I gene
FIGURE 1. Phylogeny of the Chinese Prionini based on partial sequences of 12S rRNA. A: Bootstrap 50% majority-rule consensus tree of distance method by PAUP* with bootstrap values, Wtd. S.S. = 0.0787, APSD = 4.741, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; B: Bootstrap 50% majority-rule consensus tree of maximum likelihood method by PAUP* with bootstrap values, -Ln likelihood = 2748.8839, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; C: Bootstrap 50% majority-rule consensus tree of parsimony method by PAUP* with bootstrap values, tree length = 499, CI = 0.7054, RI = 0.3849, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; D: Bayesian tree by MrBayes with Bayesian posterior probabilities, the scale bar in the bottom left corner of the tree meaning 0.1 nucleotide substitutions per site.
FIGURE 7 in Phylogenetic analysis of the Prionini (Coleoptera: Cerambycidae: Prioninae) from China based on mitochondrial ribosomal RNA genes and Cytochrome oxidase I gene
FIGURE 7. Phylogeny of the Chinese Prionini based on combined sequences of 12S rRNA and 16S rRNA (excluding Priotyrannus closteroides). A: Bootstrap 50% majority-rule consensus tree of distance method by PAUP* with bootstrap values (%), Wtd. S.S. = 0.0251, APSD = 3.047, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; B: Bootstrap 50% majority-rule consensus tree of maximum likelihood method by PAUP* with bootstrap values (%), -Ln likelihood = 4077.7392, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; C: Bootstrap 50% majority-rule consensus tree of parsimony method by PAUP* with bootstrap values (%), tree length =682, CI = 0.7405, RI = 0.3723, the scale bar in the bottom left corner of the tree being in units appropriate to the tree; D: Bayesian tree by MrBayes with Bayesian posterior probabilities (%), the scale bar in the bottom left corner of the tree meaning 0.1 nucleotide substitutions per site.
Data from: Ribosomal RNA gene repeats associate with the nuclear pore complex for maintenance after DNA damage
The ribosomal RNA genes (rDNA) comprise a highly repetitive gene cluster. The copy number of genes at this locus can readily change and is therefore one of the most unstable regions of the genome. DNA damage in rDNA occurs after binding of the replication fork blocking protein Fob1 in S phase, which triggers unequal sister chromatid recombination. However, the precise mechanisms by which such DNA double-strand breaks (DSBs) are repaired is not well understood. Here, we demonstrate that the conserved protein kinase Tel1 maintains rDNA stability after replication fork arrest. We show that rDNA associates with nuclear pores, which is dependent on DNA damage checkpoint kinases Mec1/Tel1 and replisome component Tof1. These findings suggest that rDNA-nuclear pore association is due to a replication fork block and subsequent DSB. Indeed, quantitative microscopy revealed that rDNA is relocated to the nuclear periphery upon induction of a DSB. Finally, rDNA stability was reduced in strains where this association with the nuclear envelope was prevented, which suggests its importance for avoiding improper recombination repair that could induce repeat instability.
Data from: Ribosomal RNA gene repeats associate with the nuclear pore complex for maintenance after DNA damage
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RNA polymerase I activators count and adjust ribosomal RNA gene copy number
GEO Series GSE116661. Saccharomyces cerevisiae BY4741. 25 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Genome variation profiling by high throughput sequencing.
The DYRK1A protein kinase is recruited to a subset of ribosomal protein gene promoters in human and mouse cells [HeLa RNA-seq]
GEO Series GSE158714. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Treehouse compendium of ribosomal-depletion RNA-Seq gene expression data from 2079 tumors
GEO Series GSE294353. Homo sapiens. 0 samples. Type: Expression profiling by high throughput sequencing; Third-party reanalysis.
FMRP Control of Ribosome Translocation Promotes Chromatin Modifications and Alternative Splicing of Neuronal Genes Linked to Autism (Polysome RNA-seq)
GEO Series GSE143332. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
Endonucleolytic RNA cleavage drives changes in gene expression during the innate immune response [ribosome profiling]
GEO Series GSE244176. Homo sapiens. 14 samples. Type: Other.
Rph1 coordinates transcription of ribosomal protein gene and ribosomal RNA to control cell growth under nutrient stress conditions
GEO Series GSE141034. Saccharomyces cerevisiae. 24 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
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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.