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Disruption of broad H3K9me3 domains and chromatin folding regulates oncogenic transcriptional milieu in ATRX-deficient gliomas [ChIP-seq]

GSE309141 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 14 samples Submitted 2026/07/01 Platform GPL19057
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 14 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
14 samples: 14 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.
Paper
ATRX inactivation disrupts global chromatin state and topology to dysregulate neurodevelopmental pathways in glioma pathogenesisNucleic acids research · 2026
Summary (written by the submitters)
To further understand the downstream epigenomic dysfunction induced by ATRX deficiency, we compared genome-wide chromatin-state maps of Atrx+ and Atrx- primary murine neuroepithelial progenitors (mNPCs). This ChIP–seq analysis revealed major differences in the localization of heterochromatin repressive marks H3K9me3 and H3K27me3. Specifically, we identified peculiar locations in the genome displaying H3K9me3 depletion and gain of H3K27me3 upon Atrx inactivation.
Published in
ATRX inactivation disrupts global chromatin state and topology to dysregulate neurodevelopmental pathways in glioma pathogenesis
Malgulwar PB, Singh A, Saw AK et al. · Nucleic acids research 2026 · PMID 42339622 · doi:10.1093/nar/gkag644
This dataset
Download · which file do I need?

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.

1 · Just have a look

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.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE309141_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

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 PRJNA1334217 and SRA study SRP626973. Searching any of these in the dataset finder brings you back here.

Study design
14 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 14 sample titles: 14 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.

Samples in this study
Datasets on a similar topic

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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.