Targeting CDK7 function in the RNA Polymerase II transcription cycle in inflammatory arthritis
RNA-seq (gene expression) · mouse · 64 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
- 64 samples: 21 conditions, mostly in triplicateGroups 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
- Disrupting the RNA polymerase II transcription cycle through CDK7 inhibition ameliorates inflammatory arthritisScience translational medicine · 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 GSE255108_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 PRJNA1073533 and SRA study SRP488247. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 60 samples.
How we read this: 21 replicated groups read from 64 sample titles; they account for 60 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.
- GSM8064230 DMSO_0h_Rep1 DMSO_0h
- GSM8064231 LPS_1h_Rep1 LPS_1h
- GSM8064232 LPS_6h_Rep1 LPS_6h
- GSM8064233 LPS_12h_Rep1 LPS_12h
- GSM8064234 YKL_0h_Rep1 YKL_0h
- GSM8064235 YKL_LPS_1h_Rep1 YKL_LPS_1h
- GSM8064236 YKL_LPS_6h_Rep1 YKL_LPS_6h
- GSM8064237 YKL_LPS_12h_Rep1 YKL_LPS_12h
- GSM8064238 DMSO_0h_Rep2 DMSO_0h
- GSM8064239 LPS_1h_Rep2 LPS_1h
- GSM8064240 LPS_6h_Rep2 LPS_6h
- GSM8064241 LPS_12h_Rep2 LPS_12h
- GSM8064242 YKL_0h_Rep2 YKL_0h
- GSM8064243 YKL_LPS_1h_Rep2 YKL_LPS_1h
- GSM8064244 YKL_LPS_6h_Rep2 YKL_LPS_6h
- GSM8064245 YKL_LPS_12h_Rep2 YKL_LPS_12h
- GSM8064246 DMSO_0h_Rep3 DMSO_0h
- GSM8064247 LPS_1h_Rep3 LPS_1h
- GSM8064248 LPS_6h_Rep3 LPS_6h
- GSM8064249 LPS_12h_Rep3 LPS_12h
- GSM8064250 YKL_0h_Rep3 YKL_0h
- GSM8064251 YKL_LPS_1h_Rep3 YKL_LPS_1h
- GSM8064252 YKL_LPS_6h_Rep3 YKL_LPS_6h
- GSM8064253 YKL_LPS_12h_Rep3 YKL_LPS_12h
- GSM8064254 vehicle_B2_rep1 vehicle_B2
- GSM8064255 TNFa_IFNg_24h_B2_rep1 TNFa_IFNg_24h_B2
- GSM8064256 TNFa_IFNg_YKL_24h_3h_B2_rep1 TNFa_IFNg_YKL_24h_3h_B2
- GSM8064257 TNFa_IFNg_YKL_24h_6h_B2_rep1 TNFa_IFNg_YKL_24h_6h_B2
- GSM8064258 vehicle_B1_rep2 vehicle_B1
- GSM8064259 TNFa_IFNg_24h_B1_rep2
- GSM8064260 TNFa_IFNg_YKL_24h_3h_B1_rep2 TNFa_IFNg_YKL_24h_3h_B1
- GSM8064261 TNFa_IFNg_YKL_24h_6h_B1_rep2 TNFa_IFNg_YKL_24h_6h_B1
- GSM8064262 vehicle_B1_rep3 vehicle_B1
- GSM8064263 TNFa_IFNg_24h_B3_rep3
- GSM8064264 TNFa_IFNg_YKL_24h_3h_B1_rep3 TNFa_IFNg_YKL_24h_3h_B1
- GSM8064265 TNFa_IFNg_YKL_24h_6h_B1_rep3 TNFa_IFNg_YKL_24h_6h_B1
- GSM8064266 vehicle_B2_rep4 vehicle_B2
- GSM8064267 TNFa_IFNg_24h_B2_rep4 TNFa_IFNg_24h_B2
- GSM8064268 TNFa_IFNg_YKL_24h_3h_B2_rep4 TNFa_IFNg_YKL_24h_3h_B2
- GSM8064269 TNFa_IFNg_YKL_24h_6h_B2_rep4 TNFa_IFNg_YKL_24h_6h_B2
+ 24 more — browse all 64 samples with per-sample file links →
- GSE255372 Targeting CDK7 function in the RNA Polymerase II transcription cycle in inflammatory arthritis [ChIP-seq] 69 samples shares cdk7, transcription, polymerase, targeting, arthritis, cycle
- GSE330230 Accelerated RNA polymerase II elongation drives endothelial transition and plaque vulnerability: pharmacological targeting of the super elongation complex 40 samples shares polymerase, targeting
- GSE266386 Suppressing Aberrant Hedgehog Pathway and Overcoming Resistance to Smoothened Antagonists via Targeting Super-enhancer-driven Transcriptional Dependencies [ChIP-seq] 14 samples shares transcription, targeting
- GSE202126 Single-cell Total-RNA Profiling Unveils Regulatory Hubs of Transcription Factors 1089 samples shares transcription
- GSE218715 EZH2 gain-of-function mutations distort H3K27me3 landscapes and transform transcriptional responses to PRC2 inhibition [RNA-seq] 88 samples shares transcription
- GSE102556 Sex-specific Transcriptional Signatures in Human Depression 341 samples shares transcription
- GSE281472 Targeting CD206+ macrophages disrupts the establishment of a key anti-tumor immune axis 245 samples shares targeting
- GSE260456 DOT1L provides transcriptional memory through PRC1.1 antagonism [RNA-seq] 180 samples shares transcription
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.