Linear ubiquitination prevents lipodystrophy and obesity-associated metabolic syndrome
RNA-seq (gene expression) · mouse · 12 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
- 12 samples: 3 × WT_CD vs 3 × WT_HFD vs 3 × KO_CD vs 3 × KO_HFDGroups 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.
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 GSE293034_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 PRJNA1242380 and SRA study SRP573368. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 12 samples.
How we read this: 4 replicated groups read from 12 sample titles; they account for 12 of them. 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.
- GSM8874343 WT_CD_REP1 WT_CD
- GSM8874344 WT_CD_REP2 WT_CD
- GSM8874345 WT_CD_REP3 WT_CD
- GSM8874346 WT_HFD_REP1 WT_HFD
- GSM8874347 WT_HFD_REP2 WT_HFD
- GSM8874348 WT_HFD_REP3 WT_HFD
- GSM8874349 KO_CD_REP1 KO_CD
- GSM8874350 KO_CD_REP2 KO_CD
- GSM8874351 KO_CD_REP3 KO_CD
- GSM8874352 KO_HFD_REP1 KO_HFD
- GSM8874353 KO_HFD_REP2 KO_HFD
- GSM8874354 KO_HFD_REP3 KO_HFD
- GSE298555 Linear ubiquitination prevents lipodystrophy and obesity-associated metabolic syndrome 19 samples shares obesity-associated, ubiquitination, lipodystrophy, prevents, linear
- GSE294868 Loss of EGFR prevents the synergistic early-phase impact of obesity-associated metabolic and humoral stress on primary VSMC – stressor synergy, sex-dependence 69 samples shares obesity-associated, prevents
- GSE279595 A-to-I RNA editing prevents an inflammation-mediated lipodystrophy in adipose tissue 32 samples shares lipodystrophy, prevents
- GSE272268 Intestinal stearoyl-CoA desaturase-inhibition improves obesity-associated metabolic disorders 30 samples shares obesity-associated
- GSE248299 A ketogenic diet prevents metabolic switching durng time restricted feeding 15 samples shares prevents
- GSE309415 Bile acid receptor Tgr5 prevents macrophage hyperinflammation during bacterial sepsis through metabolic and epigenetic silencing 12 samples shares prevents
- GSE271034 GV-971 prevents severe acute pancreatitis by remodeling the microbiota-metabolic-immune axis 12 samples shares prevents
- GSE304734 Macrophage PD-1 protects against metabolic dysfunction via ULK1-dependent PD-1 phosphorylation by blocking FBXO38-mediated ubiquitination and IRE1α-mediated ER stress 6 samples shares ubiquitination
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.