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Dataset results
5,031 results for “MiRNA”
Could miRNAs be Used as Markers for Distinguishing Undescended Testicles From Retractile Testicles
ClinicalTrials.gov study NCT07315737. IPD Sharing: NO. Countries: 1. Publications: 24.
Improving Autism Screening With Brain-Related miRNA
ClinicalTrials.gov study NCT02712853. IPD Sharing: NO. Countries: 1. Publications: 3.
Activation of miRNA and Proteins After Acute Experimental Pain. (miRNA: Micro RNA)
ClinicalTrials.gov study NCT04439994. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Investigation of the Diagnostic Role of miRNAs in PCa and BPH
ClinicalTrials.gov study NCT06726070. IPD Sharing: YES. Countries: 1. Publications: 8.
Study During Pregnancy of Expression of miRNAs in RA or SLE
ClinicalTrials.gov study NCT02350491. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluation of miRNAs in Endometriosis
ClinicalTrials.gov study NCT04728152. IPD Sharing: NO. Countries: 1. Publications: 1.
Changes in the Lipidomic, Immunological and miRNA Profile in Patients Undergoing a Dietary Program or Bariatric Surgery
ClinicalTrials.gov study NCT06372015. IPD Sharing: NO. Countries: 1. Publications: 5.
miRNA in Pediatric Gastritis
ClinicalTrials.gov study NCT07300787. IPD Sharing: NO. Countries: 1. Publications: 1.
miRNA Machinery in Melanoma, Melanoma Metastases and Benign Melanocytic Naevi
ClinicalTrials.gov study NCT01444560. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Immune- and miRNA-response to Recombinant Interferon Beta in Healthy Volunteers and Patients With Relapsing Remitting Multiple Sclerosis
ClinicalTrials.gov study NCT02364986. IPD Sharing: Not stated. Countries: 1. Publications: 1.
miRNA Profile in Aneurysmal Subarachnoid Haemorrhage
ClinicalTrials.gov study NCT03344744. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
MiRNA-103/107 in primary high-grade serous ovarian cancer and its clinical significance
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Supplemental Figure for Circulating MiRNA 887 is Differentially Expressed in ARDS and Modulates Endothelial Function
<p>Supplemental Figure 1. A) Among subjects who developed ARDS, there was no difference in plasma miR-887-3p levels between subjects who were (n = 9) or were not (n = 10) receiving mechanical ventilation when the sample was collected. B) Similarly, among subjects who did not develop ARDS, there was no difference in plasma-887-3p levels between subjects who were (n = 13) or were not (n = 9) receiving mechanical ventilation when the sample was collected. Standard deviation shown.</p>
Data sets v.1.1 for Perspective: SARS-CoV-2 may regulate cellular responses through depletion of specific host miRNAs
<p>The list of potential (bioinformatic predictions) interactions of human miRNA with 7 coronavirus genomes that include 3 pathogenic and 4 non-pathogenic coronaviruses. (<strong>Data Set 1</strong>). <em>The HCoVs' RNA genomes of pathogenic strains were SARS-CoV-2 (NC_045512.2), SARS-CoV (NC_004718.3), MERS-CoV (NC_019843.3). </em>The non-pathogenic strains were HCoV-OC43 (KU131570.1), HCoV-229E (NC_002645.1), HCoV-HKU1 (KF686346.1), and HCoV-NL63 (NC_005831.2). These coronaviruses were tested against the set of 896 confident mature human miRNA sequences that were obtained from the miRBbase v2.21 using the RNA22 v2 microRNA target discovery tool web-server. In order to reduce the false discovery rate of the MTS predictions, the most strict parameters were applied to the default computation workflow using a specificity of 92% versus a sensitivity of 22%.</p> <p><strong>Data set 2.</strong> The potential targets of miRNA that could be bound to either the pathogenic, the non-pathogenic or both groups of HCoVs. Predicted base on miRDIP database (with only top 1% of the most probable targets considered),</p> <p><strong>Data set 3. </strong> Pre-miRNA sequences in the <em>SARS-CoV-2</em> RNA sequence that could potentially enter the human RNAi pathway, base on miRNAFold webserver.</p>
Integrated analysis of circRNA-miRNA-mRNA of Sebastes schlegelii following Edwardsiella tarda challenge
<p><i>Sebastes schlegelii</i>, an important aquaculture species, has been widely cultured in East Asian countries. With the increase in the cultivation scale, various diseases have become major threats to the industry. Evidence has shown that non-coding RNAs (ncRNAs) have remarkable functions in the interactions between pathogens and their hosts. However, little is known about the mechanisms of circular RNAs (circRNAs) and coding RNAs in the process of preventing pathogen infection in the intestine in teleosts. In this study, we aimed to uncover the global landscape of mRNAs, circRNAs, and microRNAs (miRNAs) in response to <i>Edwardsiella tarda</i> infection at different time points (0, 2, 6, 12, and 24 h), and to construct regulatory networks for exploring the immune regulatory mechanism in the intestine of <i>S. schlegelii. </i>In total, 1,794 mRNAs, 87 circRNAs, and 79 miRNAs were differentially expressed. Some of the differentially expressed RNAs were quantitatively validated using qRT-PCR. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that most of the differentially expressed mRNA genes and the target genes of ncRNAs were related to immune signaling pathways, such as the NF-κB signal pathway, pathogen recognition receptors related to signaling pathways (Toll-like receptors and Nod-like receptors), and the chemokine signaling pathway. Based on these differentially expressed genes, 624 circRNA-miRNA pairs and 2,694 miRNA-mRNA pairs were predicted using the miRanda software. Integrated analyses generated 25 circRNA-miRNA-mRNA interaction networks. In a novel_circ_0004195/novel-530/<i>IκB</i> interaction network, novel_530 was upregulated, while its two targets, novel_circ_0004195 and <i>IκB</i>, were downregulated after <i>E. tarda</i> infection. In addition, two circRNA-miRNA-mRNA networks related to <span>apoptosis</span> (novel_circ_0003210/novel_152/apoptosis-stimulating of p53 protein 1) and interleukin (novel_circ_0001907/novel_127/interleukin-1 receptor type 2) were also identified in our study. We thus speculated that the downstream NF-κB signaling pathway, p53 signaling pathway, and apoptosis might play vital roles in the immune response in the intestine of <i>S. schlegelii.</i> This study revealed a landscape of RNAs in the intestine of <i>S. schlegelii</i><i> </i>during <i>E. tarda </i>infection<b> </b>and provided clues for further study on the immune mechanisms and signaling networks based on <i>the circRNA-miRNA-mRNA axis in </i><i>S. schlegelii</i>.</p>
Data from: Prognostic and clinical significance of miRNA-205 in endometrioid endometrial cancer
Endometrial cancer is one of the most common malignancies of the reproductive female tract, with endometrioid endometrial cancer being the most frequent type. Despite the relatively favourable prognosis in cases of endometrial cancer, there is a necessity to evaluate clinical and prognostic utility of new molecular markers. MiRNAs are small, non-coding RNA molecules that take part in RNA silencing and post-transcriptional regulation of gene expression. Altered expression of miRNAs may be associated with cancer initiation, progression and metastatic capabilities. MiRNA-205 seems to be one of the key regulators of gene expression in endometrial cancer. In this study, we investigated clinical and prognostic role of miRNA-205 in endometrioid endometrial cancer. After total RNA extraction from 100 archival formalin-fixed paraffin-embedded tissues, real-time quantitative RT-PCR was used to define miRNA-205 expression levels. The aim of the study was to evaluate miRNA-205 expression levels in regard to patients' clinical and histopathological features, such as: survival rate, recurrence rate, staging, myometrial invasion, grading and lymph nodes involvement. Higher levels of miRNA-205 expression were observed in tumours with less than half of myometrial invasion and non-advanced cancers. Kaplan-Maier analysis revealed that higher levels of miRNA-205 were associated with better overall survival (p = 0,034). These results indicate potential clinical utility of miRNA-205 as a prognostic marker.
Automatic learning of pre-miRNAs from different species – Supplemental Material
<p>In this study we investigated to which degree the features used to represent the sequences, the learning algorithm and the training and test species impacted the performance of pre-miRNA classification.</p> <p>The folder <em>multispecies.tar.xz </em>contains the data and the scripts necessary to reproduce the results in the publication in BMC Bioinformatics.</p>
Datasets of positive and negative miRNA-target interactions
<p>MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally via base-pairing with complementary sequences on messenger RNAs (mRNAs). Computational approaches that predict miRNA target interactions (MTIs) facilitate the process of narrowing down potential targets for experimental validation. The availability of new datasets of high-throughput, direct MTIs has led to the development of machine learning (ML) based methods for MTI prediction. To train an ML algorithm, there is a need to supply entries from all class labels (i.e., positive and negative). Currently, no high-throughput assays exist for capturing negative examples, hindering effective classifier construction. Therefore, current ML approaches must rely on artificially generated negative examples for training. Moreover, the lack of uniform standards for generating such data leads to biased results and hampers comparisons between studies. We investigated the impact of different methods to generate negative data on the classification of true MTIs. The study relies on training ML models on a fixed positive dataset in combination with different negative datasets and evaluating their intra- and cross-dataset performance. As a result, we were able to examine each method independently and evaluate ML models’ sensitivity to the methodologies utilized in negative data generation.</p> <p>This data includes all the negative datasets generated by the different methods and the positive data that was used (folder Data.zip).<br>In addition, we uploaded all the train and test files that we used to train and test the models (folder Train_Test_files.zip).</p>
Reciprocal Communication between FAPs and Muscle Cells via Distinct Extracellular Vesicle miRNAs in Muscle Regeneration
<p>1 Metadata: Introduction of experimental design and protocol</p> <p>2 Known-miRNA map-MCEVPs: Small RNA reads from MC-EVPs were mapped against miRBase to identify known miRNAs.</p> <p>3 Known-miRNA map-FAPEVPs: Small RNA reads from FAP-EVPs were mapped against miRBase to identify known miRNAs.</p> <p>4 Readcount-TMP-1: The expression of known miRNAs was normalized by TPM (Transcripts per million).</p> <p>5 FAP-EVPs vs MCEVPs. differential analysis: Differential expressed miRNAs were analyzed by DEGseq and filtered with qvalue<0.01 &&|log2(foldchange)|>1.</p>
Interactome Analysis: miRNA-gene networks from Austrolebias charrua exposed to Roundup (Cytoscape files .cys)
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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.