Ontology highlight
ABSTRACT:
PROVIDER: PRJNA575803 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR10230500.fastq.gz | Fastqsanger.gz | |||
| SRR10230501.fastq.gz | Fastqsanger.gz | |||
| SRR10230502.fastq.gz | Fastqsanger.gz | |||
| SRR10230503.fastq.gz | Fastqsanger.gz | |||
| SRR10230504.fastq.gz | Fastqsanger.gz |
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Project description:A post-processing algorithm for miRNA microarray data
| PRJNA575757 | ENA
Project description:One of the main disadvantages of using DNA microarrays for miRNA expression profiling is inability of adequate comparison of expression values across different miRNAs. This leads to a large amount of miRNAs with high scores which are actually not expressed in examined samples, i.e. false positives. We propose a post-processing algorithm which performs scoring of miRNAs in the results of microarray analysis based on expression values, time of discovery of miRNA represented by MIMAT accession number and correlation level between the expressions of miRNA and corresponding pre-miRNA in considered samples. The algorithm was successfully validated by the comparison of the results of its application to miRNA microarray breast tumor samples with publicly available miRNA-seq breast tumor data.
2019-10-04 | GSE138411 | GEO
Project description:The size and scope of microarray experiments continue to increase. However, datasets generated on different platforms or at different centres contain biases. Improved techniques are needed to remove platform- and batch-specific biases. One experimental control is the replicate hybridization of a subset of samples at each site or on each platform to learn the relationship between the two platforms. To date, no algorithm exists to specifically use this type of control. LTR is a linear-modelling-based algorithm that learns the relationship between different microarray batches from replicate hybridizations. LTR was tested on a new benchmark dataset of 20 samples hybridized to different Affymetrix microarray platforms. Before LTR, the two platforms were significantly different; application of LTR removed this bias. LTR was tested with six separate data pre-processing algorithms, and its effectiveness was independent of the pre-processing algorithm. Sample-size experiments indicate that just three replicate hybridizations can significantly reduce bias. An R library implementing LTR is available.
2009-11-24 | GSE16458 | GEO
Project description:Intervention type:DRUG. Intervention1:Huaier, Dose form:GRANULES, Route of administration:ORAL, intended dose regimen:20 to 60/day by either bulk or split for 3 months to extended term if necessary. Control intervention1:None.
Primary outcome(s): For mRNA libraries, focus on mRNA studies. Data analysis includes sequencing data processing and basic sequencing data quality control, prediction of new transcripts, differential expression analysis of genes. Gene Ontology (GO) and the KEGG pathway database are used for annotation and enrichment analysis of up-regulated genes and down-regulated genes.
For small RNA libraries, data analysis includes sequencing data process and sequencing data process QC, small RNA distribution across the genome, rRNA, tRNA, alignment with snRNA and snoRNA, construction of known miRNA expression pattern, prediction New miRNA and Study of their secondary structure Based on the expression pattern of miRNA, we perform not only GO / KEGG annotation and enrichment, but also different expression analysis.. Timepoint:RNA sequencing of 240 blood samples of 80 cases and its analysis, scheduled from June 30, 2022..
| 2612481 | ecrin-mdr-crc
Project description:The present study is aimed at profiling the miRNA expression pattern in oral squamous cell carcinoma (OSCC) and adjacent oral mucosa to develop a new miRNA signature for oral cancer. Agilent Human miRNA Microarray v2.0 (G4470B, Agilent Technologies) was used to identify miRNAs differentially expressed in OSCC. MicroRNA processing was carried out according to the manufacturer’s instructions. Hybridized microarrays were scanned with a DNA microarray scanner (Agilent G2565BA) and features were extracted using the Agilent Feature Extraction (AFE) image analysis tool (version A.9.5.3) with default protocols and settings. Data pre-processing and differential expression analysis were done in R Studio using the Bioconductor AgiMicroRna package.12 The Total Gene Signal provided by the AFE image analysis software was used for data analysis. Data were normalized between arrays using the quantile method. Microarray profiling identified a set of 105 miRNAs to be differentially expressed in OSCC, out of which a subset of 19 most dysregulated miRNAs were considered to formulate the miRNA signature for oral cancer.
2021-03-05 | GSE168227 | GEO
Project description:Long non-coding RNAs (lncRNAs), new star ncRNA class of mRNA-like transcripts, play complicate and critical roles in regulating various key biological processes including chromatin modification, transcription and post-transcriptional processing. Remarkably, some lncRNAs serve as a miRNA “sponge” to inhibit mediation of the differentiation of miRNA target in post-transcriptional regulation. Here, we firstly constructed the putative ceRNA network by integrating lncRNAs, miRNAs and mRNAs expression in compared with SHEE and SHEEC cell lines based on the high-though RNA sequencing data and microarray data. Though the biology function analysis, the result showed that ceRNA network mainly participate in PI3K-Akt pathway and may play a modulating role in regulation of essential signal molecules including SYK, FGF11, IL7R, MET, LAMB3, PRS6KB1, ITGB6, ITGB4, ITGA2, ITGA6, EPHA2, SOS2, VEGFA, GRB2, KRAS, CCDN1 and TP53 in primary ESCC. These results could provide us more essential clues for the development of novel therapeutic strategies and efficient drugs target in primary ESCC.
2017-06-12 | GSE72138 | GEO
Project description:Purpose: We aimed to investigate in depth the regulation of microRNA expression by hypoxia in the breast cancer cell line MCF-7, establish the relationship between microRNA expression and HIF binding sites, pri-miRNA transcription and microRNA processing gene expression. Methods: microRNA sequencing data and gene expression microarray data were generated from MCF-7 cells submitted to an hypoxia timecourse (16h, 32h and 48h at 1% Oxygen). Data was integrated to 500 published high-stringency HIF binding sites identified in MCF-7 cells. Results: We identified 41 microRNAs significantly up- and 28 down- regulated, of which 38 mature and 20 star forms are reported in conjunction with hypoxia for the first time. HIF-1α and HIF-2α binding sites within 50kb distance of microRNA loci were found by integration of HIF ChIP-seq data, showing overall association between binding sites and up-regulation. Gene expression profiling analysis showed no full coordination between pri-miRNA and microRNA expression, pointing towards additional levels of regulation. Several transcripts playing a role in microRNA processing were found regulated by hypoxia, of which two were HIF dependent. Conclusions: The data support the hypothesis that microRNA expression under hypoxia is regulated at transcriptional and post-transcriptional level. HIF is involved at both levels, regulating the transcription of certain microRNAs and also the expression of key elements of the microRNA processing pathway.
2014-02-13 | GSE47602 | GEO
Project description:Purpose: We aimed to investigate in depth the regulation of microRNA expression by hypoxia in the breast cancer cell line MCF-7, establish the relationship between microRNA expression and HIF binding sites, pri-miRNA transcription and microRNA processing gene expression. Methods: microRNA sequencing data and gene expression microarray data were generated from MCF-7 cells submitted to an hypoxia timecourse (16h, 32h and 48h at 1% Oxygen). Data was integrated to 500 published high-stringency HIF binding sites identified in MCF-7 cells. Results: We identified 41 microRNAs significantly up- and 28 down- regulated, of which 38 mature and 20 star forms are reported in conjunction with hypoxia for the first time. HIF-1α and HIF-2α binding sites within 50kb distance of microRNA loci were found by integration of HIF ChIP-seq data, showing overall association between binding sites and up-regulation. Gene expression profiling analysis showed no full coordination between pri-miRNA and microRNA expression, pointing towards additional levels of regulation. Several transcripts playing a role in microRNA processing were found regulated by hypoxia, of which two were HIF dependent. Conclusions: The data support the hypothesis that microRNA expression under hypoxia is regulated at transcriptional and post-transcriptional level. HIF is involved at both levels, regulating the transcription of certain microRNAs and also the expression of key elements of the microRNA processing pathway.
2014-02-13 | GSE47532 | GEO
Project description:Maturation of canonical microRNA (miRNA) is initiated by DROSHA that cleaves the primary transcript (pri-miRNA). Over 1,800 miRNA loci are annotated in humans, but it remains largely unknown if and at which sites the pri-miRNAs are cleaved by DROSHA. Here we performed in vitro processing on a full set of human pri-miRNAs (miRBase v21) followed by sequencing. This comprehensive profiling enabled us to classify miRNAs based on DROSHA-dependence and map their cleavage sites with respective processing efficiency measures. Only 758 pri-miRNAs are confidently processed by DROSHA, while the majority may be non-canonical or false entries. Analyses of the DROSHA-dependent pri-miRNAs show key cis-elements for processing. We observe widespread alternative processing as well as unproductive cleavage events such as “nick” or “inverse” processing. SRSF3 is a broad-acting auxiliary factor modulating alternative processing and suppressing unproductive processing. The profiling data and methods developed in this study will allow systematic analyses of miRNA regulation.
2021-07-28 | PXD025767 | Pride