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Extensive binding of uncharacterized human transcription factors to genomic dark matter [NewChIP_ERP163147]

GSE280165 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 1028 samples Submitted 2024/11/11 Platform GPL24676Platform GPL11154
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 1028 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
1028 samples: 1028 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
Extensive binding of uncharacterized human transcription factors to genomic dark matterbioRxiv : the preprint server for biology · 2024
Summary (written by the submitters)
Most of the human genome is thought to be non-functional, and includes large segments often referred to as “dark matter” DNA. The genome also encodes hundreds of putative and poorly characterized transcription factors (TFs). We determined genomic binding locations of 166 uncharacterized human TFs in living cells. Nearly half of them associated strongly with known regulatory regions such as promoters and enhancers, often at conserved motif matches and co-localizing with each other.
Read the full summary ↓
Surprisingly, the other half often associated with genomic dark matter, at largely unique sites, via intrinsic sequence recognition. Dozens of these, which we term “Dark TFs” mainly bind within regions of closed chromatin. Dark TF binding sites are rarely under purifying selection, and are enriched for transposable elements. Many Dark TFs are KZNFs, which contain the repressive KRAB domain, but many are not, and may represent potential pioneer TFs: based on compiled literature information, the Dark TFs exert diverse functions ranging from early development to tumor suppression. Thus, a large fraction of previously uncharacterized human TFs may have unappreciated activities within the dark matter genome.
Published in
Extensive binding of uncharacterized human transcription factors to genomic dark matter
Razavi R, Fathi A, Yellan I et al. · bioRxiv : the preprint server for biology 2024 · PMID 39605320 · doi:10.1101/2024.11.11.622123
This dataset
Open GSE280165 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 GSE280165_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 PRJEB78913 and SRA study ERP163147. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39605320) ↗
Study design
1028 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 the first 400 of 1028 sample titles: 400 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

+ 988 more — browse all 1028 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.