Transcriptome Profiling of Ten Porcine Tissues at the Gene, Isoform and TSS level
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR208926.fastq.gz | Fastqsanger.gz | |||
| ERR208927.fastq.gz | Fastqsanger.gz | |||
| ERR208928.fastq.gz | Fastqsanger.gz | |||
| ERR208929.fastq.gz | Fastqsanger.gz | |||
| ERR208930.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 100 |
Similar Datasets
Project description:In this study we have employed RNA seq on ten different tissues including four brain tissues from two boars to gain a understanding of the differential variations in transcriptional profiles for these tissues consisting of occipital cortex, frontal cortex, hypothalamus and cerebellum along with such diverse tissues as heart, spleen, liver, kidney, lung and musculus longissimus dorsi. This has enabled us to perform comparative gene expression analysis of brain regions versus non-brain tissues along with inter-brain tissue comparisons. Hence, we have tested for differentially expressed genes and isoforms, differential splicing, transcription start sites (TSS), and differential promoter usage between all ten porcine tissues.
2013-07-22 | E-MTAB-1405 | biostudies-arrayexpress
Project description:Cryptococcus neoformans is a life-threatening basidiomycete fungal pathogen responsible for meningoencephalitis in immunocompromised patients. This yeast can adapt to diverse habitats, efficiently produces virulence factors, and escapes immune surveillance. This implies intricate mechanisms underlying its gene regulation networks, which are yet to be comprehensively understood. Alternative transcription usage regulation has been identified as the major mean for gene expression regulation in metazoans. However, in fungi, its impact remains elusive as its study has thus far been restricted to model yeasts. We here re-analysed transcription start site (TSS)-seq data to define genuine TSS clusters in two species of pathogenic Cryptococcus. We identified two types of TSS clusters associated with specific DNA sequence motifs. Our analysis also revealed that alternative TSS usage regulation in response to environmental cues is widespread in Cryptococcus, altering gene expression and protein targeting. Importantly, we performed a forward genetic screen to identify a unique transcription factor (TF) named Tur1, which regulates aTSS usage genome-wide when cells switch from exponential phase to stationary phase. Tur1 has been previously shown to be essential for virulence in C. neoformans. Accordingly, we demonstrated here that a tur1Δ mutant strain is more sensitive to superoxide stress and phagocytosed more efficiently by macrophages than the Wild-type (WT) strain.
2024-07-03 | PXD045272 | Pride
Project description:We developed a new single cell sequencing method to simultaneously identify RNA TSS and TES at single-cell level.
2020-08-24 | GSE134311 | GEO
Project description:Sus scrofa (pig, or swine) is one of the most important economic animals and a close biological model for complex human diseases such as obesity and diabetes. It is therefore utterly important to decode the porcine microRNAome (miRNAome) as in the literature only a small portion of it is known. In this work, a comprehensive search for porcine microRNAs (miRNAs) by Illumina sequencing was performed in ten small RNA libraries prepared from mixtures of assorted tissues, which included those collected from fetuses to adult pigs. The millions of the sequencing reads were analyzed with reference to 77 known porcine miRNA precursors (pre-miRNAs) and 3,443 distinct pre-miRNAs of other mammals listed in miRBase 13.0, and the most updated porcine genome (Sscrofa9, April 2009) and available EST sequences. Additionally, miRNA candidates specific to pig are predicated by genome & EST match and hairpin folding. Our search found 72 out of 78 (~92%) known porcine miRNAs and miRNA*s, and 36 previously unannotated miRNA*s are also indentified. Furthermore, we discovered 397 novel miRNAs by mapping to the sequencing transcripts to other mammalian pre-miRNAs and 493 candidate miRNAs which do not map to other mammalian miRNAomes and could be pig-specific. We constructed sequence- and genome-position clusters for the total of 998 miRNA candidates originating from 862 pre-miRNAs, which represent 777 unique miRNA sequences. Together with the six known porcine miRNAs that not been observed in our study, we report herein the sequence families of 783 unique miRNAs and genomic distribution patterns of 622 pre-miRNAs. We preformed q-PCR experiments for selected 30 miRNAs in 47 tissue-specific samples and found agreement between the sequencing data and the q-PCR data. We envision that our report will serve as a valuable resource for future studies aimed at understanding miRNAome of pig Ten small RNA libraries from samples of porcine fetuses (30 days after insemination) to mixed tissues (180 days after birth) at ten developmental stages were sequenced.
2010-02-27 | E-GEOD-17885 | biostudies-arrayexpress
Project description:Exiguobacterium profundum strain:TSS-3 Genome sequencing
| PRJNA887767 | ENA
Project description:High-density gene expression profile of 70,000+ unique genes for 224 normal porcine samples from 28 different tissues of 32 different animals using Super deepSAGE sequencing technology.
2019-07-19 | GSE134461 | GEO
Project description:Background:Alternative splicing and isoform level expression profiling is an emerging field of interest within genomics. Splicing sensitive microarrays, with probes targeted to individual exons or exon-junctions, are becoming increasingly popular as a tool capable of both expression profiling and finer scale isoform detection. Despite their intuitive appeal, relatively little is known about the performance of such tools, particularly in comparison with more traditional 3’ targeted microarrays. Here, we use the well studied Microarray Quality Control (MAQC) dataset to benchmark the Affymetrix Exon Array, and compare it to two other popular platforms: Illumina, and Affymetrix U133. Results:We show that at the gene expression level, the Exon Array performs comparably with the two 3’ targeted platforms. However, the interplatform correlation of the results is slightly lower than between the two 3’ arrays. We show that some of the discrepancies stem from the RNA amplification protocols, e.g. the Exon Array is able to detect expression of non-polyadenylated transcripts. More importantly, we show that many other differences result from the ability of the Exon Array to monitor more detailed isoform-level changes; several examples illustrate that changes detected by the 3’ platforms are actually isoform variations, and that the nature of these variations can be resolved using Exon Array data. Finally, we show how the Exon Array can be used to detect alternative isoform differences, such as alternative splicing, transcript termination, and alternative promoter usage. We discuss the possible pitfalls and false positives resulting from isoform-level analysis. Conclusions:The Exon Array is a valuable tool that can be used to profile gene expression while providing important additional information regarding the types of gene isoforms that are expressed and variable. However, analysis of alternative splicing requires much more hands on effort and visualization of results in order to correctly interpret the data, and generally results in considerably higher false positive rates than expression analysis. One of the main sources of error in the MAQC dataset is variation in amplification efficiency across transcripts, which is not adequately corrected using existing statistical methods. We outline approaches to reduce such errors by filtering out potentially problematic data. Keywords: Compare the Affymetrix Exon Array 1.0 ST to Illumina, and Affymetrix U133 platforms
2008-10-07 | GSE13069 | GEO
Project description:Background:Alternative splicing and isoform level expression profiling is an emerging field of interest within genomics. Splicing sensitive microarrays, with probes targeted to individual exons or exon-junctions, are becoming increasingly popular as a tool capable of both expression profiling and finer scale isoform detection. Despite their intuitive appeal, relatively little is known about the performance of such tools, particularly in comparison with more traditional 3’ targeted microarrays. Here, we use the well studied Microarray Quality Control (MAQC) dataset to benchmark the Affymetrix Exon Array, and compare it to two other popular platforms: Illumina, and Affymetrix U133. Results:We show that at the gene expression level, the Exon Array performs comparably with the two 3’ targeted platforms. However, the interplatform correlation of the results is slightly lower than between the two 3’ arrays. We show that some of the discrepancies stem from the RNA amplification protocols, e.g. the Exon Array is able to detect expression of non-polyadenylated transcripts. More importantly, we show that many other differences result from the ability of the Exon Array to monitor more detailed isoform-level changes; several examples illustrate that changes detected by the 3’ platforms are actually isoform variations, and that the nature of these variations can be resolved using Exon Array data. Finally, we show how the Exon Array can be used to detect alternative isoform differences, such as alternative splicing, transcript termination, and alternative promoter usage. We discuss the possible pitfalls and false positives resulting from isoform-level analysis. Conclusions:The Exon Array is a valuable tool that can be used to profile gene expression while providing important additional information regarding the types of gene isoforms that are expressed and variable. However, analysis of alternative splicing requires much more hands on effort and visualization of results in order to correctly interpret the data, and generally results in considerably higher false positive rates than expression analysis. One of the main sources of error in the MAQC dataset is variation in amplification efficiency across transcripts, which is not adequately corrected using existing statistical methods. We outline approaches to reduce such errors by filtering out potentially problematic data. Keywords: Compare the Affymetrix Exon Array 1.0 ST to Illumina, and Affymetrix U133 platforms
2008-10-07 | GSE13066 | GEO
Project description:Comparison of gene expression level by Illumina sequencing of ten different guinea pig tissue from breeders and non-breeders.
2018-06-14 | GSE98653 | GEO
Project description:Expression profiles of porcine liver and tracheobronchial lung lymph node tissues were studied to identify genes being significantly affected by bacterial infection of the lungs. Samples were taken from liver and tracheobronchial lung lymph node tissues in pigs experimentally infected with A. pleuropneumoniae, serotype 5b, and from healthy non-inoculated control pigs from the same herd. Samples were investigated for changes in gene expression by means of global cDNA microarrays. Expression profiling of samples of liver and lung lymph node tissues from ten pigs challenged with Actinobacillus pleuropneumoniae and from five healthy control pigs was conducted by hybridising all 2x15 samples against a tissue specific common reference consisting of a pool of equal amount of total-RNA from all samples of each tissue type and were balanced with respect to control and challenged animals.
2013-01-01 | E-GEOD-16038 | biostudies-arrayexpress