Human skeletal muscle transcriptome
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA412170 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Human skeletal muscle transcriptome (39 independent analyses)
| PRJNA541438 | ENA
Project description:Transcriptome profiling of human skeletal muscle organoids
| PRJNA615088 | ENA
Project description:The amount of RNA sequencing data on skeletal muscle is very limited. We have analyzed a large set of human muscle biopsy samples and provide extensive information on the baseline skeletal muscle transcriptome, including completely novel protein-coding transcripts. Analyze of transcriptome in 24 skeletal muscle biopsy samples, 12 individuals and one biopsy per leg per individual. This experiment is linked to E-GEOD-58387.
2014-07-15 | E-GEOD-58608 | biostudies-arrayexpress
Project description:The amount of RNA sequencing data on skeletal muscle is very limited. We have analyzed a large set of human muscle biopsy samples and provide extensive information on the baseline skeletal muscle transcriptome, including completely novel protein-coding transcripts.
2014-07-15 | GSE58608 | GEO
Project description:The annotation of the Affymetrix HTA 2.0 array was updated to optimise the detection of both coding and non-coding RNA in human skeletal muscle biopsy samples by removing invalid and low signal-high-variance probes. A transcript level CDF specific to skeletal muscle is provided to use within the standard aroma.affymetrix pipeline.
2018-07-31 | GSE104235 | GEO
Project description:The few investigations on exercise-induced global gene expression responses in human skeletal muscle haves typically focused at one specific mode of exercise and few such studies have implemented control measures. However, interpretation on distinct phenotype regulation necessitate comparison between essentially different modes of exercise and the ability to identify true exercise effects, necessitate implementation of independent non-exercise control subjects. Furthermore, muscle transkriptometranscriptome data made available through previous exercise studies can be difficult to extract and interpret by individuals that are inexperienced with bioinformatic procedures. In a comparative study, we; (1) investigated the human skeletal muscle transcriptome response to differentiated exercise and non-exercise control intervention, and; (2) aimed to develop a straightforward search tool to allow for easy extraction and interpretation of our data. We provide a simple spreadsheet containing transcriptome data allowing other investigators to see how mRNA of their interest behave in skeletal muscle following exercise, both endurance, strength and non-exercise. Our approach, allow investigators easy access to information on genuine transcriptome effects of differentiated exercise, to better aid hyporthesis-driven question in this particular field of research.
2014-11-19 | GSE59088 | GEO
Project description:Transcriptome Stim1 R304W skeletal muscle
| PRJNA743888 | ENA
Project description:The few investigations on exercise-induced global gene expression responses in human skeletal muscle haves typically focused at one specific mode of exercise and few such studies have implemented control measures. However, interpretation on distinct phenotype regulation necessitate comparison between essentially different modes of exercise and the ability to identify true exercise effects, necessitate implementation of independent non-exercise control subjects. Furthermore, muscle transkriptometranscriptome data made available through previous exercise studies can be difficult to extract and interpret by individuals that are inexperienced with bioinformatic procedures. In a comparative study, we; (1) investigated the human skeletal muscle transcriptome response to differentiated exercise and non-exercise control intervention, and; (2) aimed to develop a straightforward search tool to allow for easy extraction and interpretation of our data. We provide a simple spreadsheet containing transcriptome data allowing other investigators to see how mRNA of their interest behave in skeletal muscle following exercise, both endurance, strength and non-exercise. Our approach, allow investigators easy access to information on genuine transcriptome effects of differentiated exercise, to better aid hyporthesis-driven question in this particular field of research. 18 subjects were divided into 3 groups, performing 12 weeks of Endurance or Strength training or no training. Biopsies for microarray were take before (Pre) and 2½ and 5 hours after the last training session. Isolated RNA from these biopsies were then measured with the Affymetrix Human Gene 1.0 ST arrays.
2014-11-19 | E-GEOD-59088 | biostudies-arrayexpress
Project description:Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell-specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples.
| EGAS00001005730 | EGA
Project description:Skeletal muscle is a complex heterogeneous tissue comprised of diverse muscle fiber and non-fiber cell types that, in addition to movement, influences other systems such as immunity, metabolism and cognition. We investigated gene expression patterns of resident human skeletal muscle cells using single-cell RNA-seq of dissections from vastus lateralis. We generate transcriptome profiles of 11 mononuclear human skeletal muscle mononuclear cell types, including immune, endothelial, pericyte and satellite cells. We delineate two fibro-adipogenic progenitor cell subtypes that may contribute to heterotopic ossification and muscular dystrophy fibrosis under pathological conditions. An important application of cell type signatures is for computational deconvolution of cell type specific changes using data from bulk transcriptome experiments. Analysis of transcriptome data from a 12 week resistance training study using the human skeletal muscle cell-type signatures revealed significant changes in specific mononuclear cell-type proportions related to age, sex, acute exercise and training. This characterization of human skeletal muscle cell subtypes will resolve cell type specific changes in large-scale physical activity muscle transcriptome studies and can further the understanding of the diverse effects of exercise and the pathophysiology of muscle disease.
2020-01-16 | GSE130646 | GEO