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Mapping the female mouse chromatin accessibility landscape by UUATAC-seq

GSE249077 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 23 samples Submitted 2025/07/14 Platform GPL28330
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 23 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
23 samples: 23 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
Modeling the vertebrate regulatory sequence landscape by UUATAC-seq and deep learningCell · 2025
Summary (written by the submitters)
The current understanding of regulatory sequences in the vertebrate genome is still incomplete. By introducing an adapter switching strategy, we developed an ultra-throughput ultra-sensitivity single-nucleus ATAC-seq (UUATAC-seq) protocol that enables construction of high-quality chromatin accessibility landscape from one species in a one-day experiment. By using UUATAC-seq, we mapped open chromatin regions for five representative vertebrate species at the landscape scale.
Published in
Modeling the vertebrate regulatory sequence landscape by UUATAC-seq and deep learning
Han X, Wu H, Wang X et al. · Cell 2025 · PMID 40633538 · doi:10.1016/j.cell.2025.06.020
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 GSE249077_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 PRJNA1047138 and SRA study SRP475158. Searching any of these in the dataset finder brings you back here.

Study design
23 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 23 sample titles: 23 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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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.