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1,344
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1,344 results for “ribosome”
Genetic and functional analysis of archaeal ATP-dependent RNA ligase in ribosomal RNA biogenesis
GEO Series GSE186817. Thermococcus kodakarensis KOD1. 4 samples. Type: Expression profiling by high throughput sequencing.
The 2',3' cyclic phosphatase Angel1 facilitates mRNA degradation during human ribosome-associated quality control
GEO Series GSE199650. Homo sapiens. 6 samples. Type: Other.
Ribosome profiling of NIH-3T3 cells infected with MCMV
GEO Series GSE102375. Mus musculus. 4 samples. Type: Other.
IDHwt and IDHmut adult-type diffuse gliomas display distinct alterations in ribosome biogenesis and 2’O-methylation of ribosomal RNA
GEO Series GSE224104. Homo sapiens. 94 samples. Type: Other.
Translation efficiency changes measured using ribosome footprinting (Ribo-seq) in poorly and highly metastatic cancer cells
GEO Series GSE186639. Homo sapiens. 16 samples. Type: Other.
Ribosomal profiling of WT and mORF4/NBDY KO hearts [WT_vs_mORF4-NBDYKO_RNAseq]
GEO Series GSE155133. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Profiling of terminating ribosomes reveals translational control at stop codons
GEO Series GSE270879. Mus musculus; Homo sapiens. 27 samples. Type: Expression profiling by high throughput sequencing.
Antisense ribosomal siRNAs inhibit RNA polymerase I-directed transcription in C. elegans
GEO Series GSE165078. Caenorhabditis elegans. 17 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Ribosomal profiling of CRISPR edited and parental CHL-1 cell line
GEO Series GSE71763. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing; Other.
Anatomy of footprint extension in ribosome profiling reveals translational landscape mediated by S1 protein in bacteria
GEO Series GSE155243. Escherichia coli str. K-12 substr. MG1655. 25 samples. Type: Expression profiling by high throughput sequencing; Other; Non-coding RNA profiling by high throughput sequencing.
Comparative translating ribosome affinity purification-RNAseq (TRAPseq) by lung cell-specific drivers
GEO Series GSE250140. Mus musculus. 49 samples. Type: Expression profiling by high throughput sequencing.
Ribosomal protein control of hematopoietic stem cell transformation through direct, non-canonical regulation of metabolism.
GEO Series GSE237505. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Landscape of ribosome-engaged alternative transcript isoforms across neuronal cell classes
GEO Series GSE133291. Mus musculus. 32 samples. Type: Expression profiling by high throughput sequencing.
LIN28B alters ribosomal dynamics to promote metastasis in MYCN-driven malignancy (RNA-Seq)
GEO Series GSE158320. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
TRAP-based allelic translation efficiency imbalance analysis to identify genetic regulation of ribosome occupancy in specific cell types in vivo
GEO Series GSE156414. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Ribosome profiling of wild-type and NSUN2 knockout flies
GEO Series GSE160564. Drosophila melanogaster. 4 samples. Type: Other.
Ribosome profiling of lncRNA-derived sORFs in radiosensitive and radioresistant HNSCCs
GEO Series GSE217641. Homo sapiens. 6 samples. Type: Other.
eIF1-eIF4G1 inhibitors uncover alternative translation activation of stress-response genes via enhanced ribosome loading and 5’UTR translation [MARS-seq]
GEO Series GSE166743. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Prostaglandin E2-EP4 signaling shapes immunosuppressive tumor microenvironment in human tumors by suppressing bioenergetics and ribosome biogenesis in infiltrating immune cells [scRNA-seq]
GEO Series GSE242271. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
Ribosome Footprint Density in Human Lymphoblast Cell Lines (WT vs RPL17 Heterozygous c.217-3C>G)
<p><strong><span>Supplementary Information from: </span></strong></p> <p><span>Fellmann, F., Saunders, C., <span>O’Donohue, M.-F., Reid, D. W., McFadden, K. A., Montel-Lehry, N., Yu, C., Fang, M., Zhang, J., Royer-Bertrand, B., Farinelli, P., Karboul, N., Willer, J. R., Fievet, L., Bhuiyan, Z. A., Kleinhenz, A. L. W., Jadeau, J., Fulbright, J., Rivolta, C., Renella, R., Katsanis, N., Beckmann, J. S., Nicchitta, C. V., Da Costa, L., Davis, E. E., Gleizes,<sup> </sup></span>P.-E. </span></p> <p><span>An atypical form of 60S ribosomal subunit in Diamond-Blackfan anemia linked to RPL17 variants</span></p> <p><span> </span></p> <p><strong><span>Data Type: </span></strong></p> <p><span>Processed RNAseq to Measure Ribosome Footprint Density</span></p> <p><span> </span></p> <p><strong><span>Sample information:</span></strong></p> <p><span>Species: Human</span></p> <p><span>Cell type: EBV-transformed lymphoblast cell line (LCL)</span></p> <p><span>N=3 healthy control individuals; WT</span></p> <p><span>N=3 individuals with heterozygous RPL17 <span>c.217-3C>G (1-IV-2, 1-III-3, and 1-III-5); mutant</span></span></p> <p><span> </span></p> <p><strong><span>Table S6 – Ribosome Footprint Density – Gene Level Data</span></strong></p> <p><span>Abbreviations:</span></p> <p><span>Muttrln: <span> </span>mutant translation (ribosome-associated mRNA)</span></p> <p><span>WTtrln: <span> </span>WT translation (ribosome-associated mRNA)</span></p> <p><span>Mutmrna: <span> </span>mutant mRNA (bulk mRNA)</span></p> <p><span>WTmrna: <span> </span>WT mRNA (bulk mRNA)</span></p> <p><span>Mut eff: <span> </span>mutant effect (ribosome-associated mRNA / bulk mRNA)</span></p> <p><span>Wt eff: <span> </span>WT effect (ribosome-associated mRNA / bulk mRNA)</span></p> <p><span> </span></p> <p><strong><span>Table S7 – Ribosome Footprint Density – Gene Ontology Analysis</span></strong></p> <p><span>Abbreviations:</span></p> <p><span>Mut / WT ribo-seq: <span> </span>mutant effect / WT effect</span></p> <p><span> </span></p> <p><strong><span>Methods:</span></strong></p> <p><span>Ribosome profiling was performed essentially as described </span><span>(1-3)</span><span>. For LCLs, 5.10<sup>6</sup> cells were collected by centrifugation, then lysed in 250 µl 200 mM KOAc, 15 mM MgCl<sub>2</sub>, 25 mM K-HEPES pH 7.2, 4 mM CaCl<sub>2</sub>, 2% dodecylmaltoside. For samples where RNA-seq was performed, 50 µl of the lysate was set aside and RNA was extracted using GT/phenol </span><span><span>(4)</span></span><span>. With the remaining lysate, the sample was diluted 1:1 with water, then micrococcal nuclease (Sigma-Aldrich) was added to a final concentration of 20 µg/ml. The sample was incubated for 30 min at 37 °C. Ribosomes were then pelleted through a 500 mM sucrose cushion at 90,000 rpm for 40 min in a TLA-100.2 (Beckman-Coulter). the resulting ribosome pellet was resuspended in 200 µl 50 mM NaCl, 50 mM K-HEPES pH 7.2, 5 mM EDTA, 0.5% SDS, 200 ug/ml proteinase K. RNA was extracted by phenol/chloroform, then treated with polynucleotide kinase (New England Biolabs). Ribosome footprints were isolated by polyacrylamide gel electrophoresis, and deep sequencing libraries prepared using the NEBNext Small RNA Library Prep Set (New England Biolabs). RNA-seq libraries were generated using the NEBNext Ultra Directional RNA Library Prep Kit for Illumina (New England Biolabs). All sequencing was performed using either Illumina HiSeq 2500 (for ribosome profiling) or Illumina Genome Analyzer (for RNA-seq). Reads were mapped to the RefSeq transcriptome (<span>RefSeq release 60</span>), mapping to the longest transcript derived from each gene. Reads with more than five valid mapped positions were discarded and as many as two valid mappings were allowed. A 20 nt seed region was used. Following mapping, the position of each ribosome was defined by adding 14 nt to the start of each read. The abundance of ribosomes or of mRNA was determined by the number of coding sequence-mapped reads normalized by the length of the coding sequence and the size of each deep sequencing library. Ribosome density was defined as the number of ribosome footprinting read density divided by RNA-seq read density. Statistical significance of differences in ribosome density was determined by Student’s t-test. Gene ontology analysis was performed by bootstrapping, where the mean log<sub>2</sub> difference in ribosome density was calculated, then compared to random permutations to determine p-value.</span></p> <p><strong><span>References</span></strong></p> <p><span>1. Reid DW, et al. The unfolded protein response triggers selective mRNA release from the endoplasmic reticulum. <em>Cell</em>. 2014;158(6):1362–1374.</span></p> <p><span>2. Reid DW, Nicchitta CV. Primary role for endoplasmic reticulum-bound ribosomes in cellular translation identified by ribosome profiling. <em>J Biol Chem</em>. 2012;287(8):5518–5527.</span></p> <p><span>3. Reid DW, Shenolikar S, Nicchitta CV. Simple and inexpensive ribosome profiling analysis of mRNA translation. <em>Methods</em>. 2015;91:69–74.</span></p> <p><span>4. Stephens SB, et al. Analysis of mRNA partitioning between the cytosol and endoplasmic reticulum compartments of mammalian cells. <em>Methods Mol Biol</em>. 2008;419:197–214.</span></p>
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