Transcriptome profiling of non-islet metabolic tissues in ~500 DO mouse liver, adipose, and skeletal muscle tissue from mice fed high-fat, high-sugar diet. [Adipose]
RNA-seq (gene expression) · mouse · 468 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
- Mus musculus (mouse)
- Samples
- 468 samples: 468 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
- Transcripts with high distal heritability mediate genetic effects on complex metabolic traitsbioRxiv : the preprint server for biology · 2024
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 GSE266549_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 PRJNA1107567 and SRA study SRP505574. 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 the first 400 of 468 sample titles: 400 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.
- GSM8249753 DO021_GT20_00377_adipose
- GSM8249754 DO022_GT20_00475_adipose
- GSM8249755 DO023_GT20_00504_adipose
- GSM8249756 DO024_GT20_00649_adipose
- GSM8249757 DO025_GT20_00592_adipose
- GSM8249758 DO026_GT20_00407_adipose
- GSM8249759 DO027_GT20_00670_adipose
- GSM8249760 DO028_GT20_00638_adipose
- GSM8249761 DO030_GT20_00238_adipose
- GSM8249762 DO031_GT20_00417_adipose
- GSM8249763 DO032_GT20_00538_adipose
- GSM8249764 DO033_GT20_00256_adipose
- GSM8249765 DO034_GT20_00369_adipose
- GSM8249766 DO035_GT20_00684_adipose
- GSM8249767 DO036_GT20_00612_adipose
- GSM8249768 DO037_GT20_00630_adipose
- GSM8249769 DO038_GT20_00533_adipose
- GSM8249770 DO039_GT20_00543_adipose
- GSM8249771 DO040_GT20_00397_adipose
- GSM8249772 DO041_GT20_00547_adipose
- GSM8249773 DO042_GT20_00347_adipose
- GSM8249774 DO043_GT20_00655_adipose
- GSM8249775 DO044_GT20_00418_adipose
- GSM8249776 DO046_GT20_00257_adipose
- GSM8249777 DO047_GT20_00409_adipose
- GSM8249778 DO048_GT20_00626_adipose
- GSM8249779 DO049_GT20_00354_adipose
- GSM8249780 DO050_GT20_00358_adipose
- GSM8249781 DO051_GT20_00274_adipose
- GSM8249782 DO052_GT20_00406_adipose
- GSM8249783 DO053_GT20_00699_adipose
- GSM8249784 DO054_GT20_00679_adipose
- GSM8249785 DO055_GT20_00698_adipose
- GSM8249786 DO056_GT20_00505_adipose
- GSM8249787 DO057_GT20_00611_adipose
- GSM8249788 DO058_GT20_00442_adipose
- GSM8249789 DO059_GT20_00340_adipose
- GSM8249790 DO060_GT20_00258_adipose
- GSM8249791 DO061_GT20_00571_adipose
- GSM8249792 DO062_GT20_00585_adipose
+ 428 more — browse all 468 samples with per-sample file links →
- GSE267727 Transcriptome profiling of non-islet metabolic tissues in ~500 DO mouse liver, adipose, heart, and skeletal muscle tissue from mice fed high-fat, high-sugar diet [heart] 482 samples shares high-sugar, non-islet, skeletal, high-fat, adipose, muscle, liver, diet
- GSE266921 RNA-seq expression analysis of liver, adipose, skeletal muscle, heart, and pancreatic islets from DO founder strain mice fed high-fat, high-sugar diet. [Islet] 95 samples shares high-sugar, skeletal, high-fat, adipose, muscle, liver, diet
- GSE266918 RNA-seq expression analysis of liver, adipose, skeletal muscle, heart, and pancreatic islets from DO founder strain mice fed high-fat, high-sugar diet. [heart] 63 samples shares high-sugar, skeletal, high-fat, adipose, muscle, liver, diet
- GSE266923 RNA-seq expression analysis of liver, adipose, skeletal muscle, heart, and pancreatic islets from DO founder strain mice fed high-fat, high-sugar diet. [liver] 61 samples shares high-sugar, skeletal, high-fat, adipose, muscle, liver, diet
- GSE266920 RNA-seq expression analysis of liver, adipose, skeletal muscle, heart, and pancreatic islets from DO founder strain mice fed high-fat, high-sugar diet. [Adipose] 61 samples shares high-sugar, skeletal, high-fat, adipose, muscle, liver, diet
- GSE237747 Transcriptomic profiling across 20 CC-RIX genetic backgrounds with and without high-fat high-sugar (HFHS) diet, and with and without metformin [skeletal muscle] 703 samples shares high-sugar, skeletal, high-fat, muscle, diet
- GSE253735 Loss of skeletal muscle Bmal1 +/- Exercise Training [Mouse liver, heart, inguinal white adipose and lung tissues 64 samples shares skeletal, adipose, muscle, liver
- GSE308174 Bulk mRNA-seq analysis of mouse skeletal muscle, heart and liver expressing wild-type and mutant H2B 75 samples shares skeletal, muscle, liver
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.