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Changes in Histone H3 acetylation in panobinostat-treated IDH-wildtype and IDH-mutant glioma cultures

GSE308631 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 28 samples Submitted 2025/09/19 Platform GPL20301
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 28 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
Homo sapiens (human)
Topic
Lower-grade gliomaThis series is part of our curated briefing on it.
Samples
28 samples: 28 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.
Summary (written by the submitters)
IDH1/2 mutations (IDHmut) increase methylation of DNA and histones in gliomas. IDHmut inhibitors are effective against low-grade IDHmut gliomas, but new strategies against high grade IDHmut gliomas are needed. Although histone deacetylase inhibitors (HDACi) are ineffective against IDHwt glioblastoma (GBM), their potential in IDHmut gliomas has not been extensively studied.
Read the full summary ↓
We previously established that IDHmut gliomas are more sensitive to HDACi than IDHwt GBM. Here we show that IDHmut is associated with greater sensitivity to HDACi in glioma. While HDACi induced more histone acetylation and gene regulation in IDHmut glioma than in IDHwt GBM, ChIP-Seq studies show such acetylation was mostly within gene deserts, whereas IDHmut glioma promoters paradoxically lost histone acetylation.
This dataset
Open GSE308631 in finder → Find more human ChIP / ATAC / CUT&Tag datasets → Lower-grade glioma landscape →
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.

Series matrix ↓
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 GSE308631_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA1331758 and SRA study SRP622540. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
28 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 28 sample titles: 28 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
More Lower-grade glioma datasets

Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

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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.