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5,328 results for “MicroRNAs”
Plasma circulating microRNA-expression quantitative trait loci (eQTLs) data in the Rotterdam Study
<p>The dataset contains GWAS summary statistics for 2,083 plasma circulating microRNAs, obtained from nearly 2,178 participants of the Rotterdam Study. The dataset includes three files, as outlined below:</p> <p><strong>File1: SNP_reference_file_maf0.01_Rsq0.7.txt</strong></p> <p>A reference file for SNPs with good imputation quality (Rsq > 0.7) and minor allele frequency > 0.01 among participants included in our GWAS in the Rotterdam Study (N=2,178). The headers are:</p> <p>SNP: rsID</p> <p>chr: chromosome number according to GRCh37</p> <p>bp: basepair position according to GRCh37</p> <p>effect_allele: effect allele</p> <p>other_allele: other allele</p> <p>eaf: effect allele frequency</p> <p><strong>File2: miReQTLs_1e-5_maf0.01_Rsq0.7.txt</strong></p> <p>Summary statistics for all SNPs significantly associated with 2083 miRNAs (p-value < 1e-5), filtered by minor allele frequency > 0.01 and Rsq > 0.7. The headers are:</p> <p>SNP: rsID</p> <p>beta: effect estimate</p> <p>se: standard error</p> <p>pval: p-value</p> <p>miRNA: miRNA ID</p> <p><strong>File3: miReQTLs_nominal_sig.csv.gz</strong></p> <p>Summary statistics for all SNPs nominally associated with 2083 miRNAs (p-value < 0.05). The headers are:</p> <p>RSID: SNP ID</p> <p>p-value: p-value</p> <p>phenotype: miRNA</p> <p>SE: standard error</p> <p>BETA: effect estimate</p> <p> </p> <p>The SNP allelic information and frequency can be found in the reference file (<strong>File1</strong>). </p> <p><br>For more information, please contact: m.ghanbari@erasmusmc.nl</p>
predicted microRNA target sites (miRanda)
<p>Target predictions based on the miRanda algorithm. The target sites are scored for likelihood of mRNA downregulation using mirSVR, a regression model that is trained on sequence and contextual features of the predicted miRNA::mRNA duplex. Expression profiles are derived from a comprehensive sequencing project of a large set of mammalian tissues and cell lines of normal and disease origin.</p> <p>This collection contains the following datasets from the August 2010 release of <a href="http://www.microrna.org">microRNA.org</a>:</p> <ul> <li>16228619 predicted microRNA target sites in 34911 distinct 3'UTR from isoforms of 19898 human genes</li> <li>7459149 predicted microRNA target sites in 28287 distinct 3'UTR from isoforms of 19231 mouse genes</li> <li>586068 predicted microRNA target sites in 6865 distinct 3'UTR from isoforms of 6256 rat genes</li> <li>345671 predicted microRNA target sites in 12285 distinct 3'UTR from isoforms of 10532 fruitfly genes</li> </ul>
The expression level of microRNA in invasive and nonivasive gonadotrph pituitary tumors
<p>The data include the normalized read counts from smallRNA sequencing of 20 RNA samples from gonadotroph pituitary tumors<br>The quality of small RNA fractions was assessed using Agilent 2100 Bioanalyzer with Small RNA Kit Chip (Agilent) and measured with Qubit RNA HS Assay Kits (Thermo Fisher Scientific). One μg of total RNA was used for sequencing library construction with an Ion Total RNA-Seq Kit v2 (Thermo Fisher Scientific), according to the manufacturer’s protocol. Ion Xpress™ RNA-Seq Barcode Kit was used for hybridization and ligation of RNA adapters that allows for multiplexed sequencing. RNA reverse transcription and subsequent cDNA purification and library size selection were performed using Nucleic Acid Binding Beads. cDNA was PCR-amplified, followed by DNA purification and size selection. The amount and size distribution of the amplified DNA was determined using Bioanalyzer 2100 using a High Sensitivity DNA Kit (Agilent). The length of miRNA ligation products in barcoded libraries ranged between 94 and114 bp. Template preparation for clonal amplification of up to four<br>miRNA libraries at a concentration of 18pM and loading of the PI chip were performed using Ion Chef Instrument, with Ion PI™ Hi-Q™ Chef Kit (Thermo Fisher Scientific). Ion Proton Sequencer (Thermo Fisher Scientific) was used for sequencing. Unmapped bam files were converted into fastq files with a bamToFastq script from bedtools. Read mapping to known human miRNAs (according to miRBase v.22) and reads quantification were performed using miRDeep2.14. Data normalization was performed using DESeq2. </p>
Human pancreatic islet microRNAs implicated in diabetes and related traits by large-scale genetic analysis
<p>Genetic studies have identified ≥240 loci associated with risk of type 2 diabetes (T2D), yet most of these loci lie in non-coding regions, masking the underlying molecular mechanisms. Recent studies investigating mRNA expression in human pancreatic islets have yielded important insights into the molecular drivers of normal islet function and T2D pathophysiology. However, similar studies investigating microRNA (miRNA) expression remain limited. Here, we present data from 63 individuals, the largest sequencing-based analysis of miRNA expression in human islets to date. We characterize the genetic regulation of miRNA expression by decomposing the expression of highly heritable miRNAs into <em>cis</em>- and <em>trans</em>-acting genetic components and mapping <em>cis</em>-acting loci associated with miRNA expression (miRNA-eQTLs). We find (i) 84 heritable miRNAs, primarily regulated by <em>trans</em>-acting genetic effects, and (ii) 5 miRNA-eQTLs. We also use several different strategies to identify T2D-associated miRNAs. First, we colocalize miRNA-eQTLs with genetic loci associated with T2D and multiple glycemic traits, identifying one miRNA, miR-1908, that shares genetic signals for blood glucose and glycated hemoglobin (HbA1c). Next, we intersect miRNA seed regions and predicted target sites with credible set SNPs associated with T2D and glycemic traits and find 32 miRNAs that may have altered binding and function due to disrupted seed regions. Finally, we perform differential expression analysis and identify 14 miRNAs associated with T2D status—including miR-187-3p, miR-21-5p, miR-668, and miR-199b-5p—and 4 miRNAs associated with a polygenic score for HbA1c levels—miR-216a, miR-25, miR-30a-3p, and miR-30a-5p.</p>
MicroRNA-target pathways in acute myeloid leukaemia
<p>Pathway map of 17 microRNAs (miRs) in acute myeloid leukaemia (AML). Details over- and under-expressed miRs, the impact on relevant targets, interaction of targets with other proteins and/or pathways, and the overall impact on AML onset, progression and/or maintenance. All miR targets identified and verified via miRTarBase, KEGG and relevant literature. </p>
MicroRNA Panel Predicts Lung Adenocarcinoma in Patients Presenting with Ground-Glass Nodules
<p>MicroRNA (miRNA) expression is correlated with tumor histology, differentiation, invasiveness and treatment outcome. We aimed to identify miRNAs whose differential expression might enable early diagnosis of lung adenocarcinoma in patients presenting with ground-glass nodules (GGNs). To identify potential miRNAs of interest, we analyzed the miRNA expression profile of tumor and adjacent non para-tumor tissue in 3 participants by next-generation sequencing (NGS). We then assessed the expression levels of the miRNAs of interest in 73 lung adenocarcinoma presenting with GGNs with matched adjacent non-tumor tissue by quantitative real-time polymerase chain reaction (qRT-PCR). Target genes of our selected miRNA panel were predicted using Miranda with default parameters. Twenty-three miRNAs showed differential expression between tumor and adjacent non-tumor tissue by NGS. Five miRNAs exhibited higher expression in tumor tissue compared to adjacent non-tumor tissue (P＜0.05), eighteen miRNAs demonstrated lower expression in tumor tissue versus adjacent non-tumor tissue (P＜0.05). When qRT-PCR was performed for the 23 miRNAs identified by NGS in the pilot stage, seven were found to have statistically significant expression in tumor versus adjacent non-tumor tissue (P＜0.05). The predicted targets of our miRNAs of interest are frequently associated with cancer signaling pathways. We developed a miRNA panel that could potential predict the presence of lung adenocarcinoma in patients presenting with GGNs. </p>
Research data supporting "MicroRNA Detection by DNA-Mediated Liposome Fusion"
<p>Raw research data supporting the publication:</p> <p>Jumeaux C., et al., 2017, "MicroRNA detection by DNA-mediated liposome fusion", ChemBioChem</p> <p>DOI: 10.1002/cbic.201700592</p>
Research data supporting "Duplex-Specific Nuclease-Amplified Detection of MicroRNA Using 2 Compact Quantum Dot−DNA Conjugates"
<p>Raw research data supporting the publication:</p> <p>Wang, Y. et al., 2018, ACS Applied Materials & Interfaces, "Duplex-Specific Nuclease-Amplified Detection of MicroRNA Using 2 Compact Quantum Dot−DNA Conjugates", DOI: 10.1021/acsami.8b07250.</p>
Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis
<p>This dataset contains the results of 2 related analyses, described in "Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis" (European Heart Journal Supplements, OUP). Link to the article: https://www.hal.inserm.fr/inserm-02310241</p> <p>1) In the directory "miRNAs_MARTHA_GWAS" : GWAS summary statistics for 162 circulating miRNAs in 344 VTE patients from the MARTHA cohort.</p> <p>Header for each summary file:</p> <p>Trait: miRNA id<br> chr: Chromosome<br> pos.hg19: Position of the variant in hg19/GRCh37 coordinates<br> SNP: rsid<br> A1: Reference allele on the forward strand<br> A2: Alternate allele on the forward strand<br> freq_A1: Frequency of reference allele<br> rsqr: Imputation quality defined by MACH<br> beta_A1: Estimated effect size (beta regression coefficient) of reference allele<br> se_A1: Estimated standard error of beta<br> p: p-value (significance of estimated beta)<br> z.score: Z-score</p> <p> </p> <p>2) In the directory "meta_analysis": Random effect meta-analysis combining the results of our GWAS on the MARTHA cohort, and the results from a similar analysis conducted by Nikpay et al. (doi: 10.1093/cvr/cvz030). Summary statistics of 142 microRNAs, common to both datasets, were processed (and combine 1054 samples).</p> <p>Header for each summary file:</p> <p>chr: Chromosome<br> pos.hg19: Position of the variant in hg19/GRCh37 coordinates<br> SNP: rsid<br> A1: Reference allele on the forward strand<br> A2: Alternate allele on the forward strand<br> N: Sample size<br> Q: Cochran's heterogeneity statistic<br> Q.p: p-value of Cochran's Q<br> beta_A1: Estimated effect size (beta regression coefficient) of reference allele<br> se_A1: Estimated standard error of beta<br> p: p-value (significance of estimated beta)</p> <p> </p>
Supplementary Data for 'A sensitive array for microRNA expression profiling (miChip) based on locked nucleic acids (LNA).'
<p>Supplementary Data for 'Castoldi, M., Schmidt, S., Benes, V., Noerholm, M., Kulozik, A.E., Hentze, M.W. and Muckenthaler, M.U., 2006. A sensitive array for microRNA expression profiling (miChip) based on locked nucleic acids (LNA). <em>Rna</em>, <em>12</em>(5), pp.913-920.'</p>
Characterizing and classifying neuroendocrine neoplasms through microRNA sequencing and data mining
<p>Neuroendocrine neoplasms (NENs) are clinically diverse and incompletely characterized cancers that are challenging to classify. MicroRNAs (miRNAs) are small regulatory RNAs that can be used to classify cancers. Recently, a morphology-based classification framework for evaluating NENs from different anatomic sites was proposed by experts, with the requirement of improved molecular data integration. Here, we compiled 378 miRNA expression profiles to examine NEN classification through comprehensive miRNA profiling and data mining. Following data preprocessing, our final study cohort included 221 NEN and 114 non-NEN samples, representing 15 NEN pathological types and five site-matched non-NEN control groups. Unsupervised hierarchical clustering of miRNA expression profiles clearly separated NENs from non-NENs. Comparative analyses showed that miR-375 and miR-7 expression is substantially higher in NEN cases than non-NEN controls. Correlation analyses showed that NENs from diverse anatomic sites have convergent miRNA expression programs, likely reflecting morphologic and functional similarities. Using machine learning approaches, we identified 17 miRNAs to discriminate 15 NEN pathological types and subsequently constructed a multi-layer classifier, correctly identifying 217 (98%) of 221 samples and overturning one histologic diagnosis. Through our research, we have identified common and type-specific miRNA tissue markers and constructed an accurate miRNA-based classifier, advancing our understanding of NEN diversity.</p>
Dataset - PONE-D-20-09507 - Usefulness of circulating miR-146a and miR-16-5p microRNAs as prognostic biomarkers in community-acquired pneumonia
<p>This dataset shows a prospective observational study performed in a cohort of 153 patients admitted to hospital with CAP.</p> <p>Clinical and analytical variables were collected, and the main outcome variable was 30-day mortality.</p> <p>Small RNA was purified from patients´ plasma samples by column-based protocol, and retrotranscribed to cDNA (Exiqon's miRCURY ™ series kits), adding synthetic RNA controls (spike-in). The quality of the process was evaluated (QC control) and only 117 samples passed the test.</p> <p>FIRST STEP: Eight samples paired by age and gender were selected (4 patients who had suffered a cardiovascular event or death during follow-up and 4 who had not) and a panel of 752 human miRNAs was tested (miRCURY LNA ™ Universal - Ready-to-Use Human Panel , Exiqon), in order to determine a preliminary pattern of differential miRNA expression between patients with different CAP evolution.</p> <p>SECOND STEP: According to the preliminary data obtained, 25 candidate miRNAs were selected: 5 intended to be used as normalizers, 5 selected by statistical criteria (univariate association with mortality) and 15 selected from an exhaustive bibliographic search on miRNAs, sepsis, inflammation and / or cardiovascular disease, prioritizing those that appeared in a greater number of publications and those related to respiratory diseases. RT-PCR was carried out by hybridization with double-stranded flurochrome (ExiLENT SYBR® Green Master Mix) using the C1000 Touch CFX384 thermocycler (Bio-Rad).</p> <p>The relative amount of each miRNA was calculated with ∆Ct = CtmiRNA - CtUniSp2, and it was later normalized using the GeNorm algorithm. The final data was calculated with the formula 2<sup>-∆Ct</sup> and the values were expressed as the fold change (FC) of each miRNA with respect to UniSP2<em>.</em></p>
Interactive Web-based Annotation of Plant MicroRNAs with iwa-miRNA
<p>MicroRNAs (miRNAs) are important regulators of gene expression. The large-scale detection and profiling of miRNAs has accelerated with the development of high-throughput small RNA sequencing (sRNA-Seq) techniques and bioinformatics tools. However, generating high-quality comprehensive miRNA annotations remains challenging, due to the intrinsic complexity of sRNA-Seq data and inherent limitations of existing miRNA predictions. Here, we present iwa-miRNA, a Galaxy-based framework that can facilitate miRNA annotation in plant species by combining computational analysis and manual curation. iwa-miRNA is specifically designed to generate a comprehensive list of miRNA candidates, bridging the gap between already annotated miRNAs provided by public miRNA databases and new predictions from sRNA-Seq datasets. It can also assist users to select promising miRNA candidates in an interactive mode through the automated and manual steps, contributing to the accessibility and reproducibility of genome-wide miRNA annotation. iwa-miRNA is user-friendly and can be easily deployed as a web application for researchers without programming experience. With flexible, interactive, and easy-to-use features, iwa-miRNA is a valuable tool for annotation of miRNAs in plant species with reference genomes. We illustrated the application of iwa-miRNA for miRNA annotation of plant species with varying complexity. The sources codes and web server of iwa-miRNA is freely accessible at: <a href="http://iwa-miRNA.omicstudio.cloud">http://iwa-miRNA.omicstudio.cloud</a>/.</p>
Pieris napi microRNA mirDeep2 output pdfs
<p>miRDeep2 pdf outputs for diapausing <em>Pieris napi.</em></p> <p>Code used for miRNA identification and mapping (miRNA_identification_code.txt)</p> <p>Code for identification of nearest feature to miRNAs (closest_featuress_code.txt)</p> <p>Code for identifying differentially expressed genes and clustering (clustering.R)</p> <p>Code for cluster gene set enrichment analysis (GSEA_diapause_all.R)</p> <p>Code for ideification of differentially expressed genes in diapause termination (DEG_specific_comps.R)</p> <p> </p> <p>From the paper "A time course analysis through diapause reveals dynamic temporal patterns of microRNAs associated with endocrine regulation in the butterfly <em>Pieris napi</em>"</p> <p> </p> <p> </p>
Associated code and data for "A Practical Guideline for MicroRNA Sequencing Data Analysis in Chronic Lymphocytic Leukemia (doi: 10.1007/978-1-0716-4290-0_18)".
<p>This deposit contains the data, code, and analysis to recreate the results in the manuscript - Tuulikki Suomela, Liang Zhang, Julio Vera, Heiko Bruns, Xin Lai. A Practical Guideline for MicroRNA Sequencing Data Analysis in Chronic Lymphocytic Leukemia. Methods Mol. Biol., 2883, 403–426. <a href="https://www.researchgate.net/publication/387267721_A_Practical_Guideline_for_MicroRNA_Sequencing_Data_Analysis_in_Chronic_Lymphocytic_Leukemia">https://doi.org/10.1007/978-1-0716-4290-0_18</a>.</p> <p>The pipeline allows users to perform end-to-end analysis of bulk miRNA sequencing data, including quality control of FastQ files, mapping of read counts to miRNA genes using miRBase or Reference genome, quantification of miRNA read counts, differential gene expression analysis using DEseq2, gene set enrichment analysis using curated cancer hallmark gene sets, and identification of miRNA targets.</p> <p>If you have used the code for your research, please cite the original publication. Thank you very much.</p>
Analysis of microRNA expression profiles in peri-miniscrew implant crevicular fluid in orthodontics
<p> This study systematically evaluated microRNA (miRNA) expression patterns in peri-miniscrew implant crevicular fluid (PMICF) in orthodontic patients.</p>
Raw Data for the article: Extracellular Vesicle-Derived microRNAs of Human Wharton's Jelly Mesenchymal Stromal Cells May Activate Endogenous VEGF-A to Promote Angiogenesis
<p>Despite low levels of vascular endothelial growth factor (VEGF)-A, the secretome of human Wharton's jelly (WJ) mesenchymal stromal cells (MSCs) effectively promoted proangiogenic responses in vitro, which were impaired upon the depletion of small (~140 nm) extracellular vesicles (EVs). The isolated EVs shared the low VEGF-A profile of the secretome and expressed five microRNAs, which were upregulated compared to fetal dermal MSC-derived EVs. These upregulated microRNAs exclusively targeted the <em>VEGF-A</em> gene within 54 Gene Ontology (GO) biological processes, 18 of which are associated with angiogenesis. Moreover, 15 microRNAs of WJ-MSC-derived EVs were highly expressed (Ct value ≤ 26) and exclusively targeted the thrombospondin 1 (<em>THBS1</em>) gene within 75 GO biological processes, 30 of which are associated with the regulation of tissue repair. The relationship between predicted microRNA target genes and WJ-MSC-derived EVs was shown by treating human umbilical-vein endothelial cells (HUVECs) with appropriate doses of EVs. The exposure of HUVECs to EVs for 72 h significantly enhanced the release of VEGF-A and THBS1 protein expression compared to untreated control cells. Finally, WJ-MSC-derived EVs stimulated in vitro tube formation along with the migration and proliferation of HUVECs. Our findings can contribute to a better understanding of the molecular mechanisms underlying the proangiogenic responses induced by human umbilical cord-derived MSCs, suggesting a key regulatory role for microRNAs delivered by EVs.</p>
Research data supporting "Detection of microRNA biomarkers via inhibition of DNA-mediated liposome fusion"
<p>Raw research data supporting Jumeaux, C. et al., Nanoscale (2018), DOI: 10.1039/C8NA00331A .</p>
microRNAs are abundant and stable in Platelet-rich fibrin and other autologous blood products of canines
<p>Regenerative properties and clinical adaptability of platelet-rich fibrin have been discussed in previous human research, but information about its composition in animals is lacking. It has been reported that microRNAs play an essential role in wound healing and inflammatory process. In this study, we reported the presence of various microRNAs in the platelet-rich fibrin and other autologous blood products of canines, including confirmed stability properties. We believe this research might be a step forward in understanding the regenerative mechanisms and adapting new therapy methods that can be used in humans and animals based on One Health principles. </p>
microRNA profiling of amniotic fluid samples from CAKUT fetuses
<p><strong>Summary</strong></p> <p>Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) is the leading cause of childhood end-stage renal disease and a significant cause of chronic kidney disease in adults. Genetic and environmental factors have been shown to influence CAKUT development, but the known disease mechanism remains incomplete. Our goal is to identify affected pathways and networks in CAKUT through multi-omics analysis, including miRNome, peptidome and proteome data. The peptidome and proteome data of the corresponding samples were previously published [pmid: 32750455, pmid: 33987838].</p> <p><strong>Study design</strong></p> <p>Amniotic fluid samples were collected in a prospective multicenter observational study focusing on fetal bilateral CAKUT as part of a <a title="clinical trial" href="https://clinicaltrials.gov/ct2/show/NCT02675686">clinical trial</a>. In addition, 21 samples were collected from non-CAKUT individuals. The severity was defined based on the renal status after 2 years of postnatal clinical follow-up.</p> <p>The total RNA was isolated using the Agilent RNA 6000 Pico kit protocol (5067-1513) and microRNAs were profiled using Agilent microRNA slides (Sanger miRBase release 21). The samples were labelled and hybridized according to the Agilent's microRNA Complete Labeling and Hybridization Kit protocol (5190-0456), followed by Spike-ins with the Agilent's microRNA Spike-In Kit protocol (5190-1934) and analyzed using Aligent's High-Resolution Microarray Scanner GS2505_C. Features were called using the Agilent Feature Extraction software (version 11.0.1.1) and sample intensities were normalized using quantile normalization (RMA).</p>
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