Micro RNA expression in human skeletal muscle mitochondria
RNA-seq (gene expression) · human · 33 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 33 samples: 33 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
- Paper
- A Distinct Population of Mitochondrial miRNA in Human Male Skeletal Muscle Assessed Before and After ExerciseFASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
Read the full summary ↓
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE220280_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA909414 and SRA study SRP411743. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 33 sample titles: 33 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM6797790 human mito pre exercise participant 1 [hmt-01-1]
- GSM6797791 human mito post exercise participant 1 [hmt-01-2]
- GSM6797792 human mito 3hpost exercise participant 1 [hmt-01-3]
- GSM6797793 human mito pre exercise participant 2 [hmt-02-1]
- GSM6797794 human mito post exercise participant 2 [hmt-02-2]
- GSM6797795 human mito 3hpost exercise participant 2 [hmt-02-3]
- GSM6797796 human mito post exercise participant 3 [hmt-03-2]
- GSM6797797 human mito 3hpost exercise participant 3 [hmt-03-3]
- GSM6797798 human mito pre exercise participant 4 [hmt-04-1]
- GSM6797799 human mito post exercise participant 4 [hmt-04-2]
- GSM6797800 human mito 3hpost exercise participant 4 [hmt-04-3]
- GSM6797801 human mito post exercise participant 5 [hmt-05-2]
- GSM6797802 human mito 3hpost exercise participant 5 [hmt-05-3]
- GSM6797803 human mito pre exercise participant 6 [hmt-06-1]
- GSM6797804 human mito post exercise participant 6 [hmt-06-2]
- GSM6797805 human mito 3hpost exercise participant 6 [hmt-06-3]
- GSM6797806 human mito pre exercise participant 7 [hmt-07-1]
- GSM6797807 human mito post exercise participant 7 [hmt-07-2]
- GSM6797808 human mito 3hpost exercise participant 7 [hmt-07-3]
- GSM6797809 human mito pre exercise participant 8 [hmt-08-1]
- GSM6797810 human mito post exercise participant 8 [hmt-08-2]
- GSM6797811 human mito 3hpost exercise participant 8 [hmt-08-3]
- GSM6797812 human mito pre exercise participant 9 [hmt-09-1]
- GSM6797813 human mito post exercise participant 9 [hmt-09-2]
- GSM6797814 human mito 3hpost exercise participant 9 [hmt-09-3]
- GSM6797815 human mito pre exercise participant 10 [hmt-10-1]
- GSM6797816 human mito post exercise participant 10 [hmt-10-2]
- GSM6797817 human mito 3hpost exercise participant 10 [hmt-10-3]
- GSM6797818 human mito post exercise participant 11 [hmt-11-2]
- GSM6797819 human mito 3hpost exercise participant 11 [hmt-11-3]
- GSM6797820 human mito pre exercise participant 12 [hmt-12-1]
- GSM6797821 human mito post exercise participant 12 [hmt-12-2]
- GSM6797822 human mito 3hpost exercise participant 12 [hmt-12-3]
- GSE254877 Molecular Subtypes of Human Skeletal Muscle in Cancer Cachexia 84 samples shares skeletal, muscle
- GSE174106 Biological Aging of Human Skeletal Muscle 72 samples shares skeletal, muscle
- GSE247455 Single-nucleus chromatin and gene expression profiling across hundreds of skeletal muscle samples reveals context-specific regulation 8 samples shares skeletal, muscle
- GSE268953 Cellular senescence in skeletal muscle aging: new mechanisms and therapeutic potential for sarcopenia [snMultiome_ATAC+GEX] 20 samples shares skeletal, muscle
- GSE227548 Gene expression data at the single cell level of human hamstring, deltoid, supraspinatus muscle; as well as, cultured human muscle fibroadipogenic cells (FAPs) 17 samples shares muscle
- GSE330697 Delayed molecular aging, preservation of energy metabolism and enhanced exercise response in exercise-trained human muscle 93 samples shares muscle
- GSE309004 Molecular determinants of neoadjuvant chemotherapy resistance in breast cancer: An analysis of gene expression and tumor microenvironment 54 samples shares micro
- GSE309595 Longitudinal Analysis of Multiple Myeloma: Therapeutic Response and Immune Microenvironment Dynamics in the Context of Tumor Heterogeneity [scRNA-seq] 270 samples shares micro
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.