Oncohistone H3.3K27M-driven CREB5/ID1 Axis Mediates the Malignant Cell States of Diffuse Intrinsic Pontine Glioma (DIPG)
ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 52 samples
- What it is
- ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
- Organism
- Mus musculus (mouse)
- Samples
- 52 samples: 25 conditions, mostly in duplicateGroups 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
- An oncohistone-driven H3.3K27M/CREB5/ID1 axis maintains the stemness and malignancy of diffuse intrinsic pontine gliomaNature communications · 2025
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 GSE250467_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.
Also filed as BioProject PRJNA1054113 and SRA study SRP478777. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 52 samples.
How we read this: 25 replicated groups read from 52 sample titles; they account for 52 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.
- GSM7979410 WT_ATACseq_rep1 WT_ATACseq
- GSM7979411 WT_ATACseq_rep2 WT_ATACseq
- GSM7979412 CREB5KD_ATACseq_rep1 CREB5KD_ATACseq
- GSM7979413 CREB5KD_ATACseq_rep2 CREB5KD_ATACseq
- GSM7979468 CREB5_rep1 CREB5
- GSM7979469 CREB5_rep2 CREB5
- GSM7979470 H3K27M_rep1 H3K27M
- GSM7979471 H3K27M_rep2 H3K27M
- GSM7979472 WT_BRG1_rep1 WT_BRG1
- GSM7979473 WT_BRG1_rep2 WT_BRG1
- GSM7979474 CREB5KD_BRG1_rep1 CREB5KD_BRG1
- GSM7979475 CREB5KD_BRG1_rep2 CREB5KD_BRG1
- GSM7979476 WT_H3K9me2_rep1 WT_H3K9me2
- GSM7979477 WT_H3K9me2_rep2 WT_H3K9me2
- GSM7979478 CREB5KD_H3K9me2_rep1 CREB5KD_H3K9me2
- GSM7979479 CREB5KD_H3K9me2_rep2 CREB5KD_H3K9me2
- GSM7979480 WT_H3K27me3_rep1 WT_H3K27me3
- GSM7979481 WT_H3K27me3_rep2 WT_H3K27me3
- GSM7979482 H3K27MKD1_H3K27me3_rep1 H3K27MKD1_H3K27me3
- GSM7979483 H3K27MKD1_H3K27me3_rep2 H3K27MKD1_H3K27me3
- GSM7979484 H3K27MKD2_H3K27me3_rep1 H3K27MKD2_H3K27me3
- GSM7979485 H3K27MKD2_H3K27me3_rep2 H3K27MKD2_H3K27me3
- GSM7979486 PPC_H3K27M_rep1 PPC_H3K27M
- GSM7979487 PPC_H3K27M_rep2 PPC_H3K27M
- GSM7979488 H3WT_H3K27me3_rep1 H3WT_H3K27me3
- GSM7979489 H3WT_H3K27me3_rep2 H3WT_H3K27me3
- GSM7979490 H3K27MOE_H3K27me3_rep1 H3K27MOE_H3K27me3
- GSM7979491 H3K27MOE_H3K27me3_rep2 H3K27MOE_H3K27me3
- GSM7979492 H3K27MOE_H3K27ac_rep1 H3K27MOE_H3K27ac
- GSM7979493 H3K27MOE_H3K27ac_rep2 H3K27MOE_H3K27ac
- GSM7979502 WT_RNAseq_rep1 WT_RNAseq
- GSM7979503 WT_RNAseq_rep2 WT_RNAseq
- GSM7979504 CREB5KD_RNAseq_rep1 CREB5KD_RNAseq
- GSM7979505 CREB5KD_RNAseq_rep2 CREB5KD_RNAseq
- GSM8530731 ABBV_ATAC-seq_rep1 ABBV_ATAC-seq
- GSM8530732 ABBV_ATAC-seq_rep2 ABBV_ATAC-seq
- GSM8530733 DMSO_ATAC-seq_rep1 DMSO_ATAC-seq
- GSM8530734 DMSO_ATAC-seq_rep2 DMSO_ATAC-seq
- GSM8530759 ABBV_RNAseq_rep1 ABBV_RNAseq
- GSM8530760 ABBV_RNAseq_rep2 ABBV_RNAseq
+ 12 more — browse all 52 samples with per-sample file links →
No close match found for this topic, so these are simply the most-viewed datasets of the same kind.
- GSE248947 Spatiotemporal control of SMARCA5 by a MAPK-RUNX1 axis distinguishes mutant KRAS-driven pancreatic malignancy from tissue regeneration (RNA-Seq) 28 samples
- GSE281472 Targeting CD206+ macrophages disrupts the establishment of a key anti-tumor immune axis 245 samples
- GSE293117 GPR34 loss-of-function rescues TREM2 metabolic dysfunction and promotes responsive microglial states 56 samples
- GSE271616 A STAT5B-driven mouse model of hepatosplenic γδ T cell lymphoma reveals therapeutic efficacy of JAK inhibitors 23 samples
- GSE271857 AKAP95 condensates regulate transcription and can be targeted in MLL-fusion driven oncogenesis (RNA-Seq) 18 samples
- GSE324714 Acquired genetic and cell state changes in IDH-mutant glioma progression [coculture experiments] 16 samples
- GSE288137 Single cell multiomics unravel the transcription networks controlling the different EMT tumor states 16 samples
- GSE260997 Mutant IDH inhibitors induce lineage differentiation in IDH-mutant Oligodendroglioma III 2458 samples
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.