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EOMES establishes mesoderm and endoderm differentiation potential through SWI/SNF-mediated global enhancer remodeling [ChIP-Seq]

GSE272549 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 40 samples Submitted 2024/11/08 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 40 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
40 samples: 20 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)
In this study we analysed the dynamic changes in the epigenetic landscape during first embryonic lineage decision following pluripotency exit. Here, mouse pluripotent epiblast cells segregate towards mesoderm and endoderm (ME) or neuroectoderm (NE). The analysis of chromatin accessibility, histone modifications and accompanied gene expression confirmed a bias of the epigenetic landscape towards NE fate while ME cis regulatory elements (CREs) are becoming accessible and active during lineage specification.
Read the full summary ↓
We show that the default state of NE differentiation is actively overcome by the activity of the Tbx transcription factor (TF) EOMES binding to closed ME CREs. We found the ATP-dependent chromatin remodelling complex SWI/SNF is recruited by EOMES to target ME enhancers. The recruitment of the complex by EOMES leads to the remodelling of the enhancer landscape at ME genes. The ME enhancer landscape is remodelled by the activity of EOMES independent on gene expression genome wide.
This dataset
Open GSE272549 in finder → Find more mouse ChIP / ATAC / CUT&Tag datasets →
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 GSE272549_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 PRJNA1137327 and SRA study SRP520767. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
20 conditions, mostly in duplicate
ChIP_dKO_d3_anti-Brg1 ×2 ChIP_dKO_d3_anti-H3K27ac ×2 ChIP_dKO_d3_anti-H3K4me1 ×2 ChIP_dKO_d3_anti-H3K4me3 ×2 ChIP_dKOEoGFP_d2_anti-H3K27ac ×2 ChIP_dKOEoGFP_d3_dox_ActA_anti-H3K27ac ×2 ChIP_dKOEoGFP_d3_dox_anti-Brg1 ×2 ChIP_dKOEoGFP_d3_dox_BRM014_24h_anti-GFP ×2 +12 more

Supports a between-group comparison across 40 samples.

How we read this: 20 replicated groups read from 40 sample titles; they account for 40 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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