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Tissue-specific non-heritable influence drive uterine immune system variation (ATAC-seq)

GSE225040 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 48 samples Submitted 2024/04/12 Platform GPL21697
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 48 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)
Samples
48 samples: 48 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
Tissue-specific nonheritable influences drive endometrial immune system variationScience immunology · 2024
Summary (written by the submitters)
Human twin studies have revealed the combined contribution of heritable and non-heritable (environmental) factors in shaping peripheral blood immune system variability. However, the heterogeneity within tissue immune compartments and the contribution of tissue-specific external factors remain unexplored. The human uterus is a tissue under constant regeneration exposed to distinct environmental factors.
Read the full summary ↓
To assess uterine immune system variation, we have performed a transcriptomic and epigenetic analysis of uterine and peripheral blood immune cells in monozygotic twins and longitudinal samples in relation to heritable versus non-heritable factors.
Published in
Tissue-specific nonheritable influences drive endometrial immune system variation
Bister J, Filipovic I, Sun D et al. · Science immunology 2024 · PMID 38579017 · doi:10.1126/sciimmunol.adj7168
This dataset
Open GSE225040 in finder → Find more human 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 GSE225040_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 PRJNA934582 and SRA study SRP422520. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38579017) ↗
Study design
48 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 48 sample titles: 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

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.