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Direct DNA-RNA interactions dynamically decorate core functional elements within the human genome

GSE102093 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 40 samples Submitted 2026/07/29 Platform GPL16791
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 40 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
40 samples: 19 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.
Summary (written by the submitters)
Half a century ago the rules that govern DNA-RNA interactions were thoroughly crafted. However, our understanding of the role played by these structures within the cell remains fragmentary. In that regard, early observations suggested that the human genome is decorated with RNA molecules in vivo, and though we cannot ascertain that those interactions are built through direct DNA-RNA contact, a large body of circumstantial evidence supports that DNA-RNA platforms play key roles in genome control.
Read the full summary ↓
Conceptually, RNA molecules provide an exquisite targeting scaffold to interfere with biological processes or target protein complexes to specific genomic locations. However, due to the lack of a technological leap, only a handful of documented examples describing endogenous DNA-interacting RNAs have been reported. To tackle this issue we developed DNA-interacting RNA – Identification (DiR-ID) and DNA-interacting RNA – Footprint (DiR-F), versatile methods enabling the identification of DNA-interacting RNAs and retrieval of DNA-RNA interaction footprints at single nucleotide resolution and genome-wide scale. Notably, we observed that thousands of DNA-RNA platforms are scattered throughout the genome displaying a large overlap with core functional elements (active/inactive TSS/TTS, enhancers, and promoters, facultative and constitutive heterochromatin among others) suggesting that DNA-interacting RNA molecules play a role in chromatin organization and functionality. Indeed, by interfering with JHDM1D-AS1 non-coding RNA -a bona fide DNA-interacting RNA that binds to specific sites contained within facultative heterochromatin- we altered the local epigenetic landscape, thus indicating that DNA-RNA platforms contribute to the establishment/maintenance of functional chromatin states. Finally, we identified a large number of transcripts engaged into DNA-RNA platforms and demonstrated that their recruitment to DNA respond to biological cues. All in all, we show that coding and non-coding RNAs establish direct interactions with DNA decorating hitherto identified regulatory elements throughout the genome and glimpse at a genome-wide remodeling of the chromatin landscape as a result of the recruitment/dismissal of DNA-interacting RNAs to its specific binding sites.
This dataset
Open GSE102093 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 GSE102093_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 PRJNA396567 and SRA study SRP114452. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
19 conditions, mostly in duplicate
H3K27Ac ×2 H3K27me3 ×2 H3K36me3 ×2 H3K4me1 ×2 H3K4me3 ×2 H3K9Ac ×2 H3K9me3 ×2 Input ×2 +13 more

Supports a between-group comparison across 38 samples.

How we read this: 19 replicated groups read from 40 sample titles; they account for 38 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
Datasets on a similar topic

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