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Ezh2 Delays Activation of Differentiation Genes During Normal Cerebellar Granule Neuron Development and in Medulloblastoma [ChIP-seq]

GSE279347 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 32 samples Submitted 2025/01/20 Platform GPL17021
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 32 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
32 samples: 11 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.
Summary (written by the submitters)
This study explores the role of Ezh2 in delaying the activation of differentiation genes during the development of cerebellar granule neurons (GNPs) and in SHH subtype medulloblastoma. To investigate the epigenetic landscape, we performed chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) on GNPs and medulloblastoma cells derived from Ptch1 heterozygous mice.
Read the full summary ↓
We profiled histone modifications and key regulatory proteins, including H3K27me3, H3K27ac, H3K36me3, H3K4me3, H3K4me1, H2Aubi119, Ring1b, and Pol2S5. Additionally, we conducted ATAC-seq to assess chromatin accessibility and MIRA-seq to study DNA methylation. This dataset aims to compare the epigenetic state of normal granule neuron progenitors with that of medulloblastoma cells, providing insight into how epigenetic mechanisms contribute to neuronal differentiation. We found that the inhibition of Ezh2-mediated H3K27 methylation represses differentiation in both GNPs and medulloblastoma cells.
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 GSE279347_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 PRJNA1172125 and SRA study SRP538155. Searching any of these in the dataset finder brings you back here.

Study design
11 conditions, mostly in duplicate
GNP input ×6 GNP MIRAseq ×3 GNP MIRAseq input ×3 GNP H3K4me1 ChIPseq ×2 GNP H3K27me3 ChIPseq ×2 GNP H3K27ac ChIPseq ×2 GNP H2Aubi119 ChIPseq ×2 GNP RNA pol2 Ser5-phospho ×2 +7 more

Supports a between-group comparison across 28 samples.

How we read this: 11 replicated groups read from 32 sample titles; they account for 28 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.

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.