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Embryonic stem cell factors DPPA2/4 amplify active H3K4me3-H2AK119ub chromatin domains in non-small cell lung cancer

GSE310173 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 300 samples Submitted 2026/01/20 Platform GPL18573Platform GPL30173
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 300 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)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
300 samples: 108 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.
Paper
Embryonic stem cell factors DPPA2/4 amplify active H3K4me3-H2AK119ub chromatin domains in non-small cell lung cancerGenes & development · 2026
SuperSeries — this record groups several sub-series.
Summary (written by the submitters)
This SuperSeries is composed of the SubSeries listed below.
Published in
Embryonic stem cell factors DPPA2/4 amplify active H3K4me3-H2AK119ub chromatin domains in non-small cell lung cancer
Seneviratne JA, Crisp CL, Glancy E et al. · Genes & development 2026 · PMID 41862192 · doi:10.1101/gad.353102.125
This dataset
Open GSE310173 in finder → Find more human RNA-seq datasets → Lung cancer landscape →
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 GSE310173_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 PRJNA1365224 and SRA study SRP645397. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41862192) ↗
Study design
108 conditions, mostly in duplicate
NCIH661_control ×3 NCIH661_Dppa2 ×3 NCIH661_Dppa4 ×3 NCIH661_Dppa2_4 ×3 Cov318_control ×3 Cov318_Dppa4 ×3 A485-K27ME3 ×3 DMSO-K27ME3 ×3 +145 more

Supports a between-group comparison across 255 samples.

How we read this: 108 replicated groups read from 300 sample titles; they account for 255 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

+ 260 more — browse all 300 samples with per-sample file links →

More Lung cancer datasets

Search all human RNA-seq 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.