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A bifunctional, moonlighting RNA coordinates pathways to regulate human embryonic development [ATAC-seq]

GSE275691 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 51 samples Submitted 2026/02/20 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 51 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
51 samples: 51 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
Functional landscape of non-canonical open reading frames in coordinating cell fateMolecular cell · 2026
Summary (written by the submitters)
Early embryogenesis and cellular differentiation require complex choreography between nuclear and cytoplasmic pathways. Through integrative analysis and a short open reading frame functional screen, sORF-seq, we identified lncPRESS1, a bifunctional long-non-coding RNA that also encodes a microprotein. In the nucleus, lncPRESS1 binds BRG1 to regulate chromatin remodeling and modulate the expression of pluripotency and differentiation genes.
Read the full summary ↓
In the cytoplasm, lncPRESS1 is translated into a microprotein that binds to IFT57, regulating ciliogenesis and SHH signaling pathways to control lineage commitment. Together, these results reveal a novel mechanism where the dual functions of lncPRESS1 simultaneously coordinate chromatin remodeling and signaling pathways. This coordination co-regulates the initial developmental decisions specifying neuroectoderm and mesoderm fates during human embryonic development, underscoring the underappreciated complexity of multifunctional RNAs.
Published in
Functional landscape of non-canonical open reading frames in coordinating cell fate
Zhu K, Li WC, Liu J et al. · Molecular cell 2026 · PMID 41785859 · doi:10.1016/j.molcel.2026.02.010
This dataset
Open GSE275691 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 GSE275691_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 PRJNA1152578 and SRA study SRP528683. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41785859) ↗
Study design
51 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 51 sample titles: 51 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

+ 11 more — browse all 51 samples with per-sample file links →

Datasets on a similar topic

Search all human 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.