GALNT3-deletion suppresses inflammation-associated fibrosis to ameliorates chronic kidney disease via inactivating the TNF-α/NF-kB/MAPK signaling pathway
RNA-seq (gene expression) · mouse · 9 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
- 9 samples: 3 × A vs 3 × B vs 3 × CGroups 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.
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 GSE330842_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 PRJNA1465776 and SRA study SRP700075. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 9 samples.
How we read this: 3 replicated groups read from 9 sample titles; they account for 9 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.
- GSM9733542 A_1 A
- GSM9733543 A_2 A
- GSM9733544 A_3 A
- GSM9733545 B_1 B
- GSM9733546 B_2 B
- GSM9733547 B_3 B
- GSM9733548 C_1 C
- GSM9733549 C_2 C
- GSM9733550 C_3 C
- GSE276325 Cardiomyocyte-specific knockout of ADAM17 alleviates doxorubicin-induced cardiomyopathy via inhibiting TNFα-TRAF3-TAK1-MAPK axis 12 samples shares mapk, tnf
- GSE298240 Inhibition of p38 MAPK after repetitive mild TBI ameliorates immune signaling and behavioral deficit 58 samples shares mapk, ameliorates
- GSE299517 Curcumin Attenuates Liver Fibrosis and Inflammation by Modulating Slamf8-Mediated TNF/NF-κB Signaling 12 samples shares tnf, fibrosis
- GSE239946 Signatures of IL1b, IL6 and TNFa in immune cell-types 209 samples shares tnf
- GSE307773 Oncogenic MAPK signalling induces epithelial cell state change and therapeutic resistance in vivo [RNA-Seq] 175 samples shares mapk
- GSE232746 The GPR84 molecule is a mediator of a subpopulation of retinal microglia that promote TNF/IL-1α expression via the rho-ROCK pathway after optic nerve injury II 144 samples shares tnf
- GSE318351 IL1A enhances TNF-induced retinal ganglion cell death 109 samples shares tnf
- GSE234245 Multiomic Profile Integration Reveals Early Signatures of Amyotrophic Lateral Sclerosis and Suggests the MAPK Pathway as a Therapeutic Target [RNA-Seq] 79 samples shares mapk
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.