Ontology highlight
ABSTRACT:
PROVIDER: PRJNA605876 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11071200_1.fastq.gz | Fastqsanger.gz | |||
| SRR11071200_2.fastq.gz | Fastqsanger.gz | |||
| SRR11071201_1.fastq.gz | Fastqsanger.gz | |||
| SRR11071201_2.fastq.gz | Fastqsanger.gz | |||
| SRR11071146_1.fastq.gz | Fastqsanger.gz |
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Project description:Myotonic dystrophy type 1 (DM1) is caused by the nuclear accumulation of mutant DMPK mRNA containing CUG-repeat expansions, resulting in a trans-dominant effect on RNA processing by sequestration of MBNL1 and activation of CELF1 splicing regulators. Here, we present a comprehensive study of the MBNL1 and CELF1-regulated splicing in the HeLa cell line that may participate in the complex phenotype of the DM1 disease. We have performed human GeneChip Exon array experiments with RNAs extracted from HeLa cells in which MBNL1 or CELF1 were silenced or over-expressed. MBNL1 or CELF1-silenced HeLa cells showed changes in the expression of 170 probe sets (150 genes) and 893 probe sets (613 genes), whereas MBNL1 or CELF1 over-expression on these cells had 812 probe sets (589 genes) and 684 probe sets (531 genes) altered, respectively. In MBNL1-silenced cells we have found and validated by RT-qPCR the exclusion of RASIP1 exon 4 and of KIF13A exon 26 and the inclusion of MBNL2 exon 5. Furthermore, we have found exclusion of LCOR exon 6 and PIP4K2C exon 1, and inclusion TCF12 exon 16, with dependence on the silencing degree of MBNL1, In MBNL1 over-expressed HeLa cells we have found and validated by RT-qPCR a potent inclusion of CD44 exon 8, CD44 exon 11 and the 3´UTR of TRAF2. We have then mimicked the misregulation of MBNL1 and CELF1 protein levels of DM1 in HeLa cells, finding new altered splicing events. These alterations were found in genes that encode proteins involved in myoblast differentiation and migration (CD44, RASIP1) and muscle development (TCF12 transcription factor), estrogen and thyroid receptor interactor (LCOR), as well as proteins involved in transduction signaling pathways (PIP4K2C, TRAF2) and intracellular trafficking (KIF13A). These results provide potential contributing genes that could help to explain the complex phenotype of the DM1 disease.
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Project description:Analysis of differential expression using the 3’ expression module revealed 34% probe sets showed evidence for a sex bias and alternative exon sex bias was identified for 164 genes. Intriguingly, allelic imbalance was detected for 37% of 6,579 probe sets examined which contained heterozygous SNP loci.
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Project description:Compared to other fish models, miRNAs are currently most extensively studied and identified in zebrafish. Approximately 415 dre-miRNAs have been identified and several articles have studied some aspect of miRNA function in zebrafish such as their role in basic development and in disease pathways. However, this field of research is in its infancy and the function of several dre-miRNAs, as well as their tissue-specific expression profile, are yet to be defined. In this study, the liver and gut were dissected (wildtype/untreated fish), total and small RNA were extracted, mRNA and miRNA libraries constructed and subjected to high throughput sequencing (HTS) using standard approaches. We carried out differential expression (DE) analysis and compared liver miRNA expression to gut using established bioinformatics pipelines. Through bioinformatics analysis, known and putative novel miRNAs were identified. Finally, we constructed a “miRNA matrix” that connects both total RNA-Seq and miRNA-Seq.
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Project description:Compared to other fish models, miRNAs are currently most extensively studied and identified in zebrafish. Approximately 415 dre-miRNAs have been identified and several articles have studied some aspect of miRNA function in zebrafish such as their role in basic development and in disease pathways. However, this field of research is in its infancy and the function of several dre-miRNAs, as well as their tissue-specific expression profile, are yet to be defined. In this study, the liver and gut were dissected (wildtype/untreated fish), total and small RNA were extracted, mRNA and miRNA libraries constructed and subjected to high throughput sequencing (HTS) using standard approaches. We carried out differential expression (DE) analysis and compared liver miRNA expression to gut using established bioinformatics pipelines. Through bioinformatics analysis, known and putative novel miRNAs were identified. Finally, we constructed a “miRNA matrix” that connects both total RNA-Seq and miRNA-Seq.
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Project description:Abstract The proper identification of differentially methylated CpGs is central in most epigenetic studies. The Illumina Human Methylation 450k BeadChip is widely used to quantify DNA methylation, nevertheless the design of an appropriate analysis pipeline faces severe challenges due to the convolution of biological and technical variability and the presence of a signal bias between Infinium I and II probe design types. Despite recent attempts to investigate how to analyze DNA methylation data with such an array design, it has not been possible to perform a comprehensive comparison between different bioinformatics pipelines due to the lack of appropriate datasets having both large sample size and sufficient number of technical replicates. Here we perform such a comparative analysis, targeting the problems of reducing the technical variability, eliminating the probe design bias and reducing the batch effect by exploiting two unpublished datasets, which included technical replicates and were profiled for DNA methylation either on peripheral blood, monocytes or muscle biopsies. We evaluated the performance of different analysis pipelines and demonstrated that a) it is critical to correct for the probe design type, since the amplitude of the measured methylation change depends on the underlying chemistry; b) the effect of different normalization schemes is mixed, and the most effective method in our hands were quantile normalization and Beta Mixture Quantile dilation (BMIQ); c) it is beneficial to correct for batch effects. In conclusion, our comparative analysis using a comprehensive dataset suggests an efficient pipeline for proper identification of differentially methylated CpGs using the Illumina 450k arrays. DNA samples from peripheral blood or CD14+ monocytes were included in the study. DNA methylation levels were profiled using Illumina 450K arrays. Specifically, 50 biological sample replicates from PB and 36 biological sample replicates from monocytes were randomly assigned to 8 BeadChips with technical replicates and processed in one run (a total of 96 DNA samples). Eight samples were technically replicated in pairs, while one sample was represented in a trio of replicates. Different analysis pipelines were compared, however, the file uploaded refers to the best scored. In our publication we used this one to make all analyses and conclusions.
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Project description:Genome-wide DNA methylation profiling of Standardized control samples were use to evaluate whether measurement reliability can be improved at probe level by various preprocessing pipelines
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Project description:The Affymetrix oligonucleotide microarrays measure gene expression by quantifying intensity of fluorescently labeled gene fragments that bind to sets of 25-mer oligonucleotide probes on the chip with specific sequences tailored to be complementary to the target genes. Each gene is associated with a "probe set" containing several pairs (usually 11) of "perfect match" (perfectly complementary to target sequence) and "mismatch" (different base at position 13 of 25) probes. The raw measurements of each probe set consist of a set of intensities from the probes, which require in silico preprocessing by (1) correcting for background variability, (2) normalizing intensities across samples, and (3) summarizing intensities across the probe set into a single expression value. The output of the summarization step corresponds to the background-adjusted value for the mRNA of interest. We preprocess using GCRMA, which corrects for background variability by accounting for optical noise, probe affinity, and mismatch probe adjustment; normalizes intensities by quantile normalization; and summarizes intensities using a median polish method. To minimize preprocessing batch effects, it is desirable to preprocess all samples in the dataset together. However, preprocessing across multple platforms requires a consolidation of probes with identical sequences, precluding global preprocessing on datasets with multiple platforms using the standard preprocessing pipelines. To address this problem, we have developed and applied a custom preprocessing pipeline to combine the raw .CEL files from multiple platforms that share the same probe sets.
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Project description:This series includes the four major subtypes of pituitary adenomas and normal post-mortem pituitary tissue Data Transformation Using Affymetrix Microarray Suite 5.0 global scaling was applied to the quantification data to adjust the average recorded to a target intensity of 100. Data were then exported into the bioinformatics software GeneSpring 6.0 (Silicon Genetics, Redwood City, CA) for further analysis. Data normalization was performed to scale the data so that the average intensity value on each array was 1 by dividing each expression value by the median of the expression levels on each chip. The individual gene expression levels for each of the 4 pituitary adenoma subtype arrays was divided by the expression level in the normal pituitary array. Thus, the data are presented as relative to the expression in normal pituitary tissue. Filtering was then performed to identify genes over-expressed or under-expressed at least 2.0 fold in tumours compared to normal pituitary. TABLE 1: The genes / ESTs differentially overexpressed >= 2-fold in at least one pituitary adenoma subtype compared to normal pituitary. Negative values represent underexpression. Where genes are represented by more than one probe set, individual probe data sets are given. TABLE 2: The genes / ESTs differentially underexpressed >= 2-fold in at least one pituitary adenoma subtype compared to normal pituitary. Negative values represent underexpression. Where genes are represented by more than one probe set, individual probe data sets are given. Keywords = pituitary tumor Keywords: other
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