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Optimized ChIP-exo for mammalian cells and patterned sequencing flow cells

GSE305217 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 133 samples Submitted 2025/08/14 Platform GPL30173Platform GPL30172
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 133 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
133 samples: 133 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
Optimized ChIP-exo for mammalian cells and patterned sequencing flow cellsG3 (Bethesda, Md.) · 2026
Summary (written by the submitters)
By combining chromatin immunoprecipitation (ChIP) with an exonuclease that digests the ends of protein-bound DNA fragments, ChIP-exo characterizes genome-wide protein-DNA interactions at near base-pair resolution. However, the widespread adoption of ChIP-exo has been hindered by several technical challenges, including lengthy protocols, the need for multiple custom reactions, and incompatibilities with recent Illumina sequencing platforms.
Read the full summary ↓
To address these barriers, we systematically optimized and adapted the ChIP-exo library construction protocol for the unique requirements of mammalian cells and current sequencing technologies. We introduce a Mammalian-Optimized ChIP-exo (MO-ChIP-exo) protocol that builds upon previous ChIP-exo protocols with systematic optimization of crosslinking, harvesting, and library construction. We validate MO-ChIP-exo by comparing it to previously published ChIP-exo protocols and demonstrate its adaptability to both suspension (K562) and adherent (HepG2, mESC) cell lines. This improved protocol provides a more robust and efficient method for generating high-quality ChIP-exo libraries from mammalian cells.
Published in
Optimized ChIP-exo for mammalian cells and patterned sequencing flow cells
James DQ, Mukherjee S, Cannon CC et al. · G3 (Bethesda, Md.) 2026 · PMID 41206693 · doi:10.1093/g3journal/jkaf270
This dataset
Open GSE305217 in finder → Find more 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 GSE305217_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 PRJNA1304777 and SRA study SRP608325. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41206693) ↗
Study design
133 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 133 sample titles: 133 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

+ 93 more — browse all 133 samples with per-sample file links →

Datasets on a similar topic

Search all ChIP / ATAC / CUT&Tag datasets in GEO →

Share this dataset

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.