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5,031 results for “mirnas”
MiRNA-103/107 in primary high-grade serous ovarian cancer and its clinical significance
<p>High levels of miRNA-103/107 are associated with poor outcome in case of breast cancer patients. MiRNA-103/107- DICER axis may be one of the key regulators of cancer aggressiveness. MiRNA-103/107 expression levels have never been related to patients' survival in epithelial ovarian cancer. We aimed to assess miRNA-103/107 expression levels in high grade serous ovarian cancer tissues. Expression levels of both miRNAs were related to the clinicopathological features and survival. We also evaluated expression levels of miRNA-103/107 and DICER in selected ovarian cancer cell lines (A2780, A2780cis, SK-OV-3, OVCAR3).</p> <p>We assessed relative expression of miRNA-103/107 (quantitative reverse transcription polymerase chain reaction) in fifty archival formalin-fixed paraffin embedded tissue samples of primary high grade serous ovarian cancer . Then, miRNA-103/107 and DICER expression levels were evaluated in selected ovarian cancer cells lines. Additionally, DICER, N-/E-cadherin protein levels were assessed with the use of western blot.</p> <p>We identified miRNA-107 up-regulation in ovarian cancer in comparison to healthy tissues (p=0.0005). In case of miRNA-103, we did not observe statistically significant differences between cancerous and healthy tissues (p=0.07). We did not find any correlations between miRNA-103/107 expression levels and clinicopathological features. Kaplan-Meier survival (disease free and overall survival) analysis revealed that both miRNAs cannot be considered as prognostic factors. SK-OV-3 cancer cell lines was characterised by high expression of miRNA-103/107, relatively low expression of DICER (western-blot) and relatively high N-cadherin levels in comparison to other ovarian cancer cell lines.</p> <p>Clinical and prognostic significance of miRNA-103/107 was not confirmed in our study.</p>
Stage-specific expression patterns and co-targeting relationships among miRNAs in the developing mouse cortex
<p>Analysis of expression patterns and co-targeting relationships between miRNAs in the embryonic mouse cortex. The following supplementary data are uploaded:</p><p>Supplementary_table_1.xlsx: Differentially expressed miRNAs between E14, E17 and P0 cortical samples as well as in NPCs isolated from the mouse cortex and differentiated into neurons in vitro.</p><p>Supplementary_table_2.xlsx: Weighted co-expression gene network analysis of miRNAs in the embryonic mouse cortex.</p><p>Supplementary_table_3.xlsx: Gene ontology terms of miRNA targets of the black and green modules from the WCGNA analysis.</p><p>Supplementary_table_4.xlsx: Significant co-targeting relationships between miRNAs in the embryonic mouse cortex.</p><p> </p><p> </p>
Biogenesis, conservation and function of miRNA in liverworts
<p>Recent findings on liverwort micro RNA repertoire, <em>Marchantia </em>miRNA gene organization, biogenesis, functions, and microprocessor, auxiliary, and regulatory proteins involved in miRNA biogenesis</p>
Supplementary file of A comprehensive atlas of pig RNA editome across 23 tissues reveals RNA editing affecting interaction mRNA-miRNAs
<p><span>Figure S1: PCA analysis of all tissues. Figure S2: wild(CFLAR-A) and experimental(CFLAR-G) plasmid by PCR and Sanger sequencing. Figure S3: Validation of RNA editing by PCR and Sanger sequencing. Table S1: Information of all samples. Table S2: The filtering criteria for sites of all tissues. Table S3: Primer information for RNA editing validation. Table S4: Primer information of vector construction. Table S5: Information of all RNA editing sites. Table S6: Specificity of A-to-I editing sites in skeletal muscle of pigs. Table S7: Specificity of A-to-I editing sites of skeletal muscle with GO annotation. Table S8: The result of the prediction of miRNA target regions by miranda software.</span></p>
Raw data of "Identification of two unannotated miRNAs in classic Hodgkin lymphoma cell lines" article.
<p>This is the dataset with used raw data regarding preparation of the manuscript entitled "Identification of two unannotated miRNAs in classic Hodgkin lymphoma cell lines" (10.1371/journal.pone.0283186).</p>
A comprehensive atlas of pig RNA editome across 23 tissues reveals RNA editing affecting interaction mRNA-miRNAs
<p>Figure S1: PCA analysis of all tissues. Figure S2: wild(CFLAR-A) and experimental(CFLAR-G) plasmid by PCR and Sanger sequencing. Figure S3: Validation of RNA editing by PCR and Sanger sequencing. Table S1: Information of all samples. Table S2: The filtering criteria for sites of all tissues. Table S3: Primer information for RNA editing validation. Table S4: Primer information of vector construction. Table S5: Information of all RNA editing sites. Table S6: Information of all RNA-seq expression data. Table S7: Specificity of A-to-I editing sites in skeletal muscle of pigs. Table S8: Specificity of A-to-I editing sites of skeletal muscle with GO annotation. Table S9: The result of the prediction of miRNA target regions by miranda software.</p>
Dataset of miRNA-Disease Relations Extracted from Textual Data using Transformer-based Neural Networks
<p>Supplementary Data.</p>
miRNAs targeting AQP3 - miRNet
<div> <p>This data was collected via the miRNet website when searching for miRNAs that would target AQP3.</p> <p> </p> </div>
miRNA expression in THP1 cell line
<p><span>This data was collected via a databank on human cellular microRNAome built by McCall MN et al when searching for the expression of different miRNAs in the THP1 cell line</span></p>
Dataset for Improved differential expression analysis of miRNA-seq data by modeling competition to be counted
Open the record for dataset details and reuse information.
miRNA gene regulatory networks for 38 human tissues
<p>We reconstructed miRNA regulatory networks for 38 tissues from the Genotype-Tissue Expression project (GTEx) using two different prior networks, one obtained with target predictions from TargetScan and one with target predictions from miRanda.</p> <p>We used these networks to investigate gene expression and regulation by miRNAs across these tissues. In the RData file, we share the following objects:</p> <p>- <strong>exp</strong>: a 16,161 by 9,435 data frame including normalized expression data for each sample.</p> <p>- <strong>expTS</strong>: a 16,161 by 38 matrix including the tissue-specificity scores for each gene in each tissue.</p> <p>- <strong>netT</strong>: a 10,391,523 by 41 data frame that includes the miRNA regulatory networks. The column "miRNA" includes the name of the regulating miRNA, the column "Gene" includes the target gene (HGNC symbol), and the column "Prior" the prior regulatory network based on target predictions from TargetScan, with 1 for edges that are canonical and 0 for edges that are non-canonical. The remaining 38 columns contain the PUMA network edge weights for each of the 38 tissues.</p> <p>- <strong>netT_TS</strong>: a 10,391,523 by 38 matrix that includes the tissue-specificity scores of the miRNA regulatory networks that were modeled on the TargetScan prior. Edges are not labelled, but edge order corresponds to the edges in "netT".</p> <p>- <strong>netM</strong>: a 10,391,523 by 41 data frame that includes the miRNA regulatory networks. The column "miRNA" includes the name of the regulating miRNA, the column "Gene" includes the target gene (HGNC symbol), and the column "Prior" the prior regulatory network based on target predictions from miRanda, with 1 for edges that are canonical and 0 for edges that are non-canonical. The remaining 38 columns contain the PUMA network edge weights for each of the 38 tissues.</p> <p>- <strong>netM_TS</strong>: a 10,391,523 by 38 matrix that includes the tissue-specificity scores of the miRNA regulatory networks that were modeled on the miRanda prior. Edges are not labelled, but edge order corresponds to the edges in "netT".</p> <p>- <strong>samples</strong>: a 9,435 by 2 data frame that includes sample identifiers (matching the identifiers in "exp") and the tissue to which these samples belong.</p> <p>- <strong>mirnames</strong>: a 694 by 3 data frame that contains miRNA names of regulators and their matching target miRNA names. The first column "base_miRNA" contains the "base" miRNA, the name of the miRNA without any extensions. The second column "reg_miRNA" contains the 643 regulator miRNA, which may have -3P/-5P extensions, and which matches the miRNAs that are present as regulators in the networks. The third columns "tar_miRNA" contains the 621 target miRNAs, which may have numbered suffix extensions, and for which we have expression data available.</p>
miRNA sequencing raw data_cgmuro
Open the record for dataset details and reuse information.
FIGURES 5–12 in Psoralis mirnae sp. nov., the first species of the skipper genus from Central America (Lepidoptera: Hesperiidae)
FIGURES 5–12. Male genitalia of Psoralis mirnae sp. nov. 5. Tegumen and uncus, dorsal view. 6–7. Juxta, posterior and left lateral views. 8. Succus, dorsal view. 9. Tegumen + vinculum, lateral view. 10. Right valvae, internal view. 11–12. Aedeagus, lateral and dorsal views. Scale bar = 1 mm.
FIGURES 17–18 in Psoralis mirnae sp. nov., the first species of the skipper genus from Central America (Lepidoptera: Hesperiidae)
FIGURES 17–18. Female genitalia of Psoralis mirnae sp. nov., lateral and ventral views. Scale bar = 1 mm.
FIGURES 13–16 in Psoralis mirnae sp. nov., the first species of the skipper genus from Central America (Lepidoptera: Hesperiidae)
FIGURES 13–16. Male genitalia of Psoralis degener 13. Tegumen + vinculum, lateral view. 14. Right valvae, internal view. 15–16. Aedeagus, lateral and dorsal views. Scale bar = 1 mm.
FIGURES 1–4 in Psoralis mirnae sp. nov., the first species of the skipper genus from Central America (Lepidoptera: Hesperiidae)
FIGURES 1–4. Psoralis mirnae sp. nov. 1–2. Holotype male, dorsal and ventral views. 3–4. Allotype female, dorsal and ventral views. Scale bar = 1 cm.
Intermediary and supplemental data for publication "Heterogenous circulating miRNA changes in ME/CFS converge on a unified cluster of target genes and may be a result of modulation by latent herpesviruses: A computational analysis"
<p>Intermediary and supplemental data for publication "Heterogenous circulating miRNA changes in ME/CFS converge on a unified cluster of target genes and may be a result of modulation by latent herpesviruses: A computational analysis"</p>
miRCoop-v2: Detecting synergistic miRNA interactions in cancer
<p>miRCoop-v2: Detecting synergistic miRNA interactions in cancer</p>
Validation of a Salivary miRNA Diagnostic Test for ASD
ClinicalTrials.gov study NCT05418023. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Liquid Biopsy Using Exosomal miRNA Enables Risk Stratification of Potential Metastasis in Patients With Intrahepatic Cholangiocarcinoma.
ClinicalTrials.gov study NCT07224737. IPD Sharing: NO. Countries: 1. Publications: 13.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.