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H3K4me3 and H3K9me3 histone modification profiles of uterine stromal cells from young and aged mouse females

GSE267479 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 18 samples Submitted 2024/09/13 Platform GPL17021
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 18 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
18 samples: 18 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
REPRODUCTIVE AGEING: Altered histone modification landscapes underpin defects in uterine stromal cell decidualization in aging femalesReproduction (Cambridge, England) · 2024
Summary (written by the submitters)
Epigenetic profiling of uterine stromal cells (UtSCs) was performed for the active and repressive histone modifications H3K4me3 and H3K9me3, respectively. We find that UtSCs from aged females are epigenetically distinct, as determined by genomic enrichment profiling for H3K4me3 and H3K9me3, respectively. We find that many pregnancy hormone-inducible genes exhibit a profound lack of promoter-associated H3K4me3 in aged UtSCs, implying that a significant enrichment of active histone marks prior to gene stimulation is required to enable the elicitation of a rapid transcriptional response.
Published in
REPRODUCTIVE AGEING: Altered histone modification landscapes underpin defects in uterine stromal cell decidualization in aging females
Woods L, Dean W, Hemberger M · Reproduction (Cambridge, England) 2024 · PMID 38995736 · doi:10.1530/REP-24-0171
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 GSE267479_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 PRJNA1111566 and SRA study SRP507653. Searching any of these in the dataset finder brings you back here.

Study design
18 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 18 sample titles: 18 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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