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333 results for “RNA-binding protein”
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>
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>
Identifying cellular RNA-binding proteins during infection uncovers a role for MKRN2 in influenza mRNA trafficking
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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.
RNA-binding protein RPS27 and gene expression regulation in Kaposi’s sarcoma [RIP-seq]
GEO Series GSE212960. Homo sapiens. 4 samples. Type: Other.
RNA-binding proteins Zfp36l1 and Zfp36l2 protect against premature thymic involution.
GEO Series GSE315892. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Probing the orthogonality and robustness of the mammalian RNA-binding protein Musashi-1 in Escherichia coli [Ribo-seq]
GEO Series GSE275581. Escherichia coli. 4 samples. Type: Other.
Evaluation of novel computational methods that identify RNA-binding protein footprints from structural data
GEO Series GSE262542. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
The Role of the RNA-binding protein HuR in MPNST growth and metastasis [RIP-chip]
GEO Series GSE120684. Homo sapiens. 40 samples. Type: Other.
The RNA-binding protein PNPase regulates biofilm formation and virulence in Listeria monocytogenes
GEO Series GSE210097. Listeria monocytogenes. 4 samples. Type: Expression profiling by high throughput sequencing.
RNA-binding protein quaking (Qk) promotes switching from neurogenesis to gliogenesis of neural stem cells during brain development
GEO Series GSE117018. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
Photoactivatable-Ribonucleotide-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) of RNA-binding proteins (RBPs), IGF2BP1 and IGF2BP3 in the K562 CML-derived cell line
GEO Series GSE138063. Homo sapiens. 4 samples. Type: Other.
The landscape of alternative polyadenylation during EMT and its regulation by the RNA-binding protein Quaking [PAT-seq]
GEO Series GSE228929. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.