ZMAT3 alleviates cell death in Fanconi anemia via adaptation of sphingolipid metabolism
RNA-seq (gene expression) · human · 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
- Homo sapiens (human)
- Samples
- 12 samples: 12 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.
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 GSE308873_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 PRJNA1333308 and SRA study SRP625455. 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 12 sample titles: 12 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.
- GSM9254819 M1_ishRen_DOX_MMC_S2
- GSM9254820 M1_ishRen_DOX_S1
- GSM9254821 M1_ishZMAT3_DOX_MMC_S8
- GSM9254822 M1_ishZMAT3_DOX_S7
- GSM9254823 M2_ishRen_DOX_MMC_S4
- GSM9254824 M2_ishRen_DOX_S3
- GSM9254825 M2_ishZMAT3_DOX_MMC_S10
- GSM9254826 M2_ishZMAT3_DOX_S9
- GSM9254827 M3_ishRen_DOX_MMC_S6
- GSM9254828 M3_ishRen_DOX_S5
- GSM9254829 M3_ishZMAT3_DOX_MMC_S12
- GSM9254830 M3_ishZMAT3_DOX_S11
- GSE280756 Identification of RNAs regulated by ZMAT3 and p53 10 samples shares zmat3
- GSE311014 Cytokine-driven glycosphingolipid metabolism modulates endoplasmic reticulum calcium homeostasis in primary human renal mesangial cells 27 samples shares sphingolipid
- GSE324335 Multi-omics characterization of SIRT3 metabolism and its adaptation to the presence of amyloid-beta oligomers in nasal epithelial cells 12 samples shares adaptation
- GSE280017 Transcriptomic analysis of iPSC-derived endothelium reveals adaptations to high altitude hypoxia in energy metabolism and inflammation 10 samples shares adaptation
- GSE342040 Eliglustat-Induced Remodeling of Sulfo-Glycosphingolipid Metabolism and Gene Expression in hepatocellular carcinoma cells 9 samples shares sphingolipid
- GSE311827 AP-1 Mediates Cellular Adaptation and Memory Formation [Bulk RNA-seq] 192 samples shares adaptation
- GSE234181 Long-term multi-modal recording reveals epigenetic adaptation routes in dormant breast cancer cells [scRNA-Seq] 184 samples shares adaptation
- GSE279256 RAS pathway activation and microenvironmental adaptation as hallmarks of myeloid sarcoma 108 samples shares adaptation
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.