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674 results for “RNA binding protein”
SIRAH-CoV2 initiative: NSP9 RNA binding protein (PDBid:6W4B)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 NSP9 RNA binding protein (PDB id: 6W4B, Bioassembly 1). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The file 6W4B_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W4B_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6W4B_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W4B_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6W4B_SIRAHcg_prot.prmtop 6W4B_SIRAHcg_prot.ncrst 6W4B_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: Nucleocapsid protein N-terminal RNA binding domain (PDB id:6M3M)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Nucleocapsid protein N-terminal RNA binding domain (PDB id:6M3M). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The files 6M3M_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6M3M_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6M3M_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6M3M_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6W4B_SIRAHcg_prot.prmtop 6W4B_SIRAHcg_prot.ncrst 6W4B_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
Paramecium Polycomb Repressive Complex 2 physically interacts with the small RNA binding PIWI protein to repress transposable elements
<p>Polycomb Repressive Complex 2 (PRC2) maintains transcriptionally silent genes in a repressed state via deposition of histone H3 K27 trimethyl (me3) marks. PRC2 has also been implicated in silencing transposable elements (TEs), yet how PRC2 is targeted to TEs remains unclear. To address this question, we identified proteins that physically interact with the <em>Paramecium</em> Enhancer-of-zeste Ezl1 enzyme, which catalyzes H3K9me3 and H3K27me3 deposition at TEs. We show that the <em>Paramecium</em> PRC2 core complex comprises four subunits, each required <em>in vivo</em> for catalytic activity. We also identify PRC2 cofactors, including the RNA interference (RNAi) effector Ptiwi09, which are necessary to target H3K9me3 and H3K27me3 to TEs. We find that the physical interaction between PRC2 and the RNAi pathway is mediated by a RING finger protein and that small RNA recruitment of PRC2 to TEs is analogous to the small RNA recruitment of H3K9 methylation SU(VAR)3-9 enzymes.</p>
Double-stranded RNA structural elements holding the key to translational regulation in cancer: the case of editing in RNA Binding Motif Protein 8A
<p>Raw data supporting the manuscript</p> <p>Abukar, A.;Wipplinger, M.;<br> Hariharan, A.; Sun, S.; Ronner, M.;<br> Sculco, M.; Okonska, A.;<br> Kresoja-Rakic, J.; Rehrauer, H.; Qi, W.;<br> et al. Double-Stranded RNA<br> Structural Elements Holding the Key<br> to Translational Regulation in Cancer:<br> The Case of Editing in RNA-Binding<br> Motif Protein 8A. Cells 2021, 10, 3543.<br> https://doi.org/10.3390/<br> cells10123543</p>
Dataset for RNA-binding protein FUS antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aiming at characterizing antibodies for the RNA-binding protein FUS protein. Version 2 contains the FUS FSC file describing the experimental set up used for the Flow Cytometry experiment. The original study is also available on the Zenodo YCharOS community (<a href="https://doi.org/10.5281/zenodo.5259945">https://doi.org/10.5281/zenodo.5259945</a>).</em></p>
RNAProt: An efficient and feature-rich RNA binding protein binding site predictor
<p>RNAProt Supplementary Data Archive, containing benchmark and training datasets (see content.txt for more details on included datasets)</p>
Full dataset for 'RNA-binding proteins in human genetic disease'
<p>This extended version of the Supplementary Table S1 of the publicaiton "RNA-binding proteins in human genetic disease" (Gebauer, F., Schwarzl, T., Valcárcel, J. <em>et al.</em> <em>Nat Rev Genet</em> <strong>22</strong>, 185–198 (2021). https://doi.org/10.1038/s41576-020-00302-y) contains all human RNA-binding proteins (RBPs) found in at least one high-throughput RNA interactome caputure study with all their annotation along with known RBP.</p> <p>The whole set is available as download on RBPbase with additional annotation and additional studies. This table contains the version used in the original publication</p> <p> </p>
Data Availability for "The RNA-binding protein landscapes differ between mammalian organs and cultured cells"
<p>Data Availability for "The RNA-binding protein landscapes differ between mammalian organs and cultured cells" Joel I. Perez-Perri, Dunja Ferring-Appel, Ina Huppertz, Thomas Schwarzl, Sudeep Sahadevan, Frank Stein, Mandy Rettel, Bruno Galy, and Matthias W. Hentze</p>
TIRF Microscopy Data Files. "Multiple RNA- and DNA-binding proteins exhibit direct transfer of polynucleotides: Implications for target site search"
<p>TIRF-microscopy images and analysis data from single-molecule experiments assessing the direct transfer phenomenon in the TREX1 exonuclease. </p>
Identifying cellular RNA-binding proteins during infection uncovers a role for MKRN2 in influenza mRNA trafficking
Open the record for dataset details and reuse information.
Structural dynamics of SARS-CoV-2 nucleocapsid protein induced by RNA binding
<p>This dataset contains files of the molecular dynamics simulations performed in "Structural dynamics of SARS-CoV-2 nucleocapsid protein induced by RNA binding" study. Further information in presented in README.md file and the abstract of the study is presented below:</p> <p>"The nucleocapsid (N) protein of the SARS-CoV-2 virus, the causal agent of COVID-19, is a multifunction phosphoprotein that plays critical roles in the virus life cycle, including transcription and packaging of the viral RNA. To play such diverse roles, the N protein has two globular RNA-binding modules, the N- (NTD) and C-terminal (CTD) domains, which are connected by an intrinsically disordered region. Despite the wealth of structural data available for the isolated NTD and CTD, how these domains are arranged in the full-length protein and how the oligomerization of N influences its RNA-binding activity remains largely unclear. Herein, using experimental data from electron microscopy and biochemical/biophysical techniques combined with molecular modeling and molecular dynamics simulations, we showed that, in the absence of RNA, the N protein formed structurally dynamic dimers, with the NTD and CTD arranged in extended conformations. However, in the presence of RNA, the N protein assumed a more compact conformation where the NTD and CTD are packed together. We also provided an octameric model for the full-length N bound to RNA that was consistent with electron microscopy images of the N protein in the presence of RNA. Together, our results shed new light on the dynamics and higher-order oligomeric structure of this versatile protein."</p>
Paired datasets to study alternative splicing regulation by individual RNA-binding proteins
<p>This project stores datasets generated to study the regulation of alternative splicing using deep learning models (e.g., SpliceAI). In particular, these datasets were used to perform ablation studies (sequence perturbations at motif locations) to evaluate their effects on the deep learning model.</p> <p><span>I </span><span>used public RNA-Seq data from the ENCODE consortium to identify exons sensitive to the knockdown of RNA-binding proteins (RBPs). The idea is that exons sensitive to RBP knockdowns are more likely to be directly or indirectly regulated by such RBPs, hence providing hints on their regulation mechanisms. Importantly,</span><span> I </span><span>also generated paired control exons, which were not alternatively spliced upon RBP knockdown but have similar GC composition and length compared to the knockdown-sensitive exons (target exon and surrounding introns). These control sets were generated to account for potential confounding factors of gene architecture features and, therefore, focus only on RBP binding motifs and their regulatory logic.</span></p> <p><strong>Information about the files</strong></p> <p>After uncompressing the 'paired_dataset.tar.gz' file, a directory with multiple files will be created with the following structure:</p> <ul> <li><em>0_rMATS_ES_events.tsv.gz</em><em>: </em>Summary tables of differential splicing analysis, with deltaPSI estimates referring to Ctrl - Knockdown groups. Important columns: 'target_coordinates' refers to the 1-based coordinates of the alternatively spliced exon, and 'group' indicates the individual knockdown experiments where the exon was observed to be alternatively spliced.</li> <li><em>0_rMATs_ES_non_changing_events.tsv.gz:</em> Summary tables of differential splicing analysis, but in this case, contains all non-changing events (dPSI < |0.025|).</li> <li> <p><em>1_KD_exons_dPSI0.1.tsv.gz:</em> Table with knockdown-sensitive exons along with values for gene architecture features along the exon triplet (exon upstream, intron upstream, cassette exon, intron downstream exon downstream).</p> </li> <li> <p><em>1_Ctrl_exons_dPSI0.025.tsv.gz:</em> Same as '1_KD_exons_dPSI0.1.tsv.gz', but for all non-changing events.</p> </li> <li> <p><em><strong>2_paired_datasets.tsv.gz:</strong></em> Paired datasets in tidy format, where Knockdown-sensitive exons and their Control pairs come in consecutive lines. The 'rbp_name' column refers to the individual knockdown experiment where that exon was observed.</p> </li> <li><em>2_paired_datasets_negative_dPSI.tsv.gz, 2_paired_datasets_positive_dPSI.tsv.gz:</em> Same as '2_paired_datasets.tsv.gz', but knockdown-sensitive exons are split according to the direction of dPSI observed in the RNA-Seq data (along with the respective control pair).</li> <li><em>2_paired_datasets_individualRBPs</em>: This folder contains the paired datasets in wide format, where a single line contains both the knockdown-sensitive and control pair. In addition, each paired dataset (knockdown of individual RBP) is written in a separate file.</li> </ul> <div><strong>Details of the sh knockdown RNA-Seq analysis</strong></div> <div>Because in the ENCODE study (Van Nostrand E.L. et al., 2020), authors analyzed knockdown RNA Seq data using an older version of the human genome (hg19) along with old genome annotations (GENCODE v19), I reanalyzed ENCODE data aligned to the hg38 genome build. I used rMATS v4.1.2 on each RBP knockdown experiment to detect differentially spliced events between the two knockdown replicates vs the two control replicates. rMATS was run with GENCODE annotations v44 and specifically tweaked with <em>--cstat 0.05</em>. <div> </div> <div>Significant knockdown-sensitive events were identified with a deltaPSI > |0.1|, using a False Discovery Rate cutoff of 0.05. Non-changing events, assumed as knockdown-agnostic controls, were defined as those exhibiting negligible deltaPSI variation (< |0.025|). To ensure the high quality of the exon sets, further analytical steps were performed. First, I applied a read coverage filter, by retaining events where the median coverage across replicates per condition for the isoform with more read counts was higher than 7. Then, I exclusively focused on exon skipping events in protein-coding genes, and filtered out unannotated exons (pseudoexons) as well as first or last exons of genes. In addition, I excluded duplicate exon skipping events by picking the transcript with the highest biological importance (based on the presence of transcript flags such as MANE selected, CCDS, or APPRIS). A total of 15,235 events were detected across all RBP knockdown experiments (N=72, splicing-associated RBPs with data available for the HepG2 cell line), covering 6,659 unique exons.</div> </div> <div> <div> </div> </div>
What's in a name: The multifaceted function of DNA- and RNA-binding proteins in T cell responses
<p>Data and analyses code used in the manuscript "What’s in a name: The multifaceted function of DNA- and RNA-binding proteins in T cell responses." (DOI:XXXX-XXX).</p> <p>Analyses can also be found here: https://github.com/kasbress/DRBP_Tcell_responses</p> <p> </p> <p>"Data" folder contains all data used for the analysis.</p> <p>"Output" folder contains all output generated in the analysis.</p> <p>"Figs" folder contains all figures generated in the analysis.</p> <p>The R markdown file contains all analysis code used. </p>
Data from: Functional advantages of conserved intrinsic disorder in RNA-binding proteins
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Data from: The RNA-binding protein Celf1 post-transcriptionally regulates p27Kip1 and Dnase2b to control fiber cell nuclear degradation in lens development
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RNA-binding proteins that lack canonical RNA-binding domains are rarely sequence-specific
GEO Series GSE215198. synthetic construct. 667 samples. Type: Expression profiling by array.
Systematic identification of RNA-binding proteins and tethered domains that activate exon splicing inclusion [eCLIP-seq]
GEO Series GSE232597. Homo sapiens. 20 samples. Type: Other.
FSCN1 promotes esophageal carcinoma progression through downregulating PTK6 via its RNA-binding protein effect
GEO Series GSE197624. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Robust single-cell discovery of RNA targets of RNA binding proteins and ribosomes [RNA-seq]
GEO Series GSE155649. Homo sapiens. 77 samples. Type: Other.
RNA-binding protein RPS27 and gene expression regulation in Kaposi’s sarcoma [RIP-seq]
GEO Series GSE212960. Homo sapiens. 4 samples. Type: Other.
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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)
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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.