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454 results for “non-coding RNAs”
PARN and TOE1 constitute a 3′ end maturation module for nuclear non-coding RNAs
<p>HeLa cells were cultured in DMEM (Welgene) supplemented with 9% fetal bovine serum (Welgene). HeLa cells were transfected with 20 nM of siRNAs for four days using Lipofectamine 3000 (Thermo Fisher Scientific). Equal amounts of four different siRNAs were used for each knockdown. In the combinatorial knockdown, we mixed multiple siRNA pools to have a final concentration of 20 nM per siRNA pool. Total RNAs were extracted from siRNA-transfected HeLa cells using TRIzol reagent (Thermo Fisher Scientific) according to the manufacturer’s protocol and treated with DNase I (Takara). mTAIL-seq libraries were prepared as previously described (Lim et al., 2016). Amplified cDNA libraries were sequenced on an Illumina MiSeq platform with 50% of the PhiX control library (Illumina).</p> <p>The uploaded file includes both intensity and sequence information for spike-ins and libraries used in the mTAIL-seq analysis. These data can be processed with Tailseeker 3.1.7 (Chang, 2017) according to the standard workflow of the software. The source codes and container images are available from Zenodo (https://zenodo.org/record/887547; doi:10.5281/zenodo.887546).</p>
Data from: Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep
<p>This dataset contains additional files from the manuscript: "Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep".</p> <p>The files included are:</p> <p>- All novel lncRNA transcript annotation GTF file ( lncrnas.gtf )</p> <p>- High-confidence lncRNA gene annotation GTF file ( lncrnas_evidence.gtf )</p> <p>- All novel lncRNA transcript annotation GTF file remapped to the ARS-UI_Ramb_v2.0 genome ( lncrnas_remapped_v2.gtf )</p> <p>- Raw count estimates of the extended annotation ( rawcounts.csv )</p> <p>- TPM values of the extended annotation ( tpmcounts.csv )</p> <p>- Supplementary data to the published article (.xlsx, .pdf)</p> <p> </p>
Training data for 'Long non-coding RNAs (lncRNAs) annotation with FEELnc' tutorial (Galaxy Training Material)
<p>Data needed for the 'Long non-coding RNAs (lncRNAs) annotation with FEELnc' tutorial (Galaxy Training Material).<br>The assembly was generated following the 'Genome assembly using PacBio data' tutorial.<br>The annotation was generated following the 'Genome annotation with Funannotate ' tutorial.</p> <p>The bam file is RNASeq SRR8534859_1.fastq.gz and SRR8534859_2.fastq.gz mapping on the genome assembly.</p>
195 original studies of non-coding RNAs in sarcoma
<p><strong>Background</strong>: Sarcomas comprise approximately 1% of all human malignancies, and treatment resistance is one of the main reasons for poor prognosis. Accumulating evidence suggests that non-coding RNAs, including microRNAs, long non-coding RNAs, and circular RNAs, are important molecules involved in the crosstalk between resistance to chemotherapy, targeted therapy, and radiotherapy via various pathways.</p> <p><strong>Methods</strong>: We searched PubMed (MEDLINE) databases for articles on non-coding RNAs relevant to sarcoma from inception to November 30, 2021. Studies investigating the roles of host-derived microRNAs, long non-coding RNAs, and circular RNAs in sarcoma were included. Data on the roles of ncRNAs in therapeutic regulation and their applicability as biomarkers of sarcomas were extracted. Two independent researchers assessed the quality of the studies using modified guidelines from the Systematic Review Center for Laboratory Animal Experimentation (SYRCLE), a tool based on the Cochrane Collaboration Risk of Bias tool.</p> <p><strong>Results</strong>: Observational studies revealed ectopic expression of non-coding RNAs in sarcoma patients with different responses to antitumor treatments. Experimental studies have confirmed crosstalk between cellular pathways pertinent to chemotherapy, targeted therapy, and radiotherapy resistance. Of the included studies, the SYRCLE scores ranged from 3 to 6 (average score = 4.51). This finding suggested a moderate risk of bias. Of the ten articles that investigated non-coding RNAs as biomarkers, none included a validation cohort. Selective reporting of the sensitivity, specificity, and receiver operating curves was common.</p> <p><strong>Conclusion</strong>: Although non-coding RNAs appear to be good candidates as biomarkers and therapeutics for sarcoma, their differential expression across tissues complicates their application. Further investigation of the potential for inhibition or activation of these regulatory molecules to reverse treatment resistance may be useful.</p>
Research data supporting "Nanozyme-catalysed CRISPR assay for preamplification-free detection of non-coding RNAs"
<p>Raw research data supporting: Broto, M., Kaminski, M.M., Adrianus, C. <em>et al.</em> Nanozyme-catalysed CRISPR assay for preamplification-free detection of non-coding RNAs. <em>Nat. Nanotechnol.</em> (2022). https://doi.org/10.1038/s41565-022-01179-0</p>
195 original studies of non-coding RNAs in sarcoma
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Transcriptional kinetics and molecular functions of long non-coding RNAs
<p>R code, R markdowns and data (count tables) for the analysis described in 'Transcriptional kinetics and molecular functions of long non-coding RNAs'. Code and data is also available at https://github.com/sandberg-lab/lncRNAs_bursting.</p>
Supplementary Materials for Evaluation role of ferroptosis long non-coding RNAs for immune microenvironment and microsatellite instability in colon cancer
<p>Supplementary Materials for "Evaluation role of ferroptosis long non-coding RNAs for immune microenvironment and microsatellite instability in colon cancer"</p>
Supplementary Data: Discovery of natural non-circular permutations in non-coding RNAs
<p><strong>Supplementary File 1</strong>. RNAMotif Rnamotif / DARN! search patterns for the new permutations of the hammerhead ribozyme and analogous twister ribozyme permutations.</p> <p><strong>Supplementary File 2</strong>. Alignments of novel RNA motifs detected in this work (Supplementary Table 2). The alignments are stored in Stockholm format, which is a text format that can be viewed with a fixed-width font and can also be interpreted by several computer programs. Only unique sequences are supplied.</p> <p><strong>Supplementary File 3</strong>. Printable alignments of novel RNA motifs detected in this work (Supplementary Table 2), in PDF format. Only unique sequences are shown.</p> <p><strong>Supplementary File 4</strong>. Frequencies of RNA motifs in different environmental datasets. This data is a basis for relevant columns in Supplementary Table 2. The first worksheet describes the data format.</p> <p> </p>
Non-coding RNAs Analysis of Eosinophil Subtypes in Asthma
ClinicalTrials.gov study NCT04542902. IPD Sharing: NO. Countries: 1. Publications: 10.
Study of Peripheral Blood Non-coding RNAs as Diagnosis and Prognosis Biomarker for Acute Pancreatitis
ClinicalTrials.gov study NCT02602808. IPD Sharing: Not stated. Countries: 1. Publications: 2.
The Role of Long Non-coding RNAs WRAP53 and UCA-1 as Potential Biomarkers in Diagnosis of Hepatocellular Carcinoma
ClinicalTrials.gov study NCT05088811. IPD Sharing: NO. Countries: 1. Publications: 4.
De novo profiling of long non-coding RNAs involved in MC-LR–induced liver injury in whitefish: discovery and perspectives
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Datasets accompanying Deep generative AI models analyzing circulating orphan non-coding RNAs enable accurate detection of early-stage non-small cell lung cancer
<p>These datasets accompany the manuscript "Deep generative AI models analyzing circulating orphan non-coding RNAs enable accurate detection of early-stage non-small cell lung cancer".</p> <p>The datasets include:</p> <ul> <li> <p><code>metadata.tsv.gz</code> Phenotype information of the samples</p> </li> <li> <p><code>mirna_counts.tsv.gz</code> MicroRNA count matrix (for data normalization)</p> </li> <li> <p><code>oncrna_counts.tsv.gz</code> oncRNA count matrix</p> </li> <li> <p><code>orion/</code> Ensemble of Orion models</p> </li> </ul>
Alzheimer's disease-induced phagocytic microglia express specific profile of coding and non-coding RNAs
<p>This repository contains the data and the code used in Flavia's project.</p> <p>A folder can contain the starting raw data in "data", the R scripts in order of execution (op1, op2 ..) and the "output" folder that contains the final processed data of each operation.</p> <ul> <li>"BV2_analysis" contains the processing and analysis of the bulk RNA sequencing data with the different miRNAs in study</li> <li>"BV2_fastq_nfcore" contains the results of processing the fasta sequences with nfcore rnaseq workflow</li> <li>"Proinf_analysis" contains the analysis of the external bulk RNA sequencing data with the different studies of proinflammatory microglial cells</li> <li>"Spatial_data_analysis" contains the raw data of the spatial sequencing, the assempbly of the spatial expression profiles starting from the DAPI images and the transcripts coordinates, the analysis performed on the data</li> <li>"Spatial_U_scRNA_analysis" contains the code that integrates the spatial sequencing with the scRNA sequencing, plus the code of the analysis</li> </ul>
Data from: Genome-wide discovery and characterization of maize long non-coding RNAs
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Data from: Genome-wide differential expression of synaptic long non-coding RNAs in autism spectrum disorder
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Total RNA sequencing in multiple Sus Scrofa tissues reveals novel long non-coding RNAs functioning in skeletal muscle development
GEO Series GSE73763. Sus scrofa. 13 samples. Type: Expression profiling by high throughput sequencing.
Differential Expression of Super-enhancer-associated Long Non-coding RNAs in Uterine Leiomyomas
GEO Series GSE193320. Homo sapiens. 16 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
Analysis of long non-coding RNAs highlights tissue-specific expression patterns and epigenetic profiles in normal and psoriatic skin
GEO Series GSE63979. Homo sapiens. 42 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.