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Charting the regulatory landscape of TP53 on transposable elements in cancer [ATAC-seq]

GSE262051 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 24 samples Submitted 2024/10/20 Platform GPL18573
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 24 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
24 samples: 12 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
Charting the regulatory landscape of TP53 on transposable elements in cancerGenome research · 2025
Summary (written by the submitters)
Comprehensive profiling of transposable element (TE)-derived transcripts dynamics under the regulation of p53 provides valuable resources for more clarity in p53 pleiotropic roles in cancer. In this project, we created three cancer cell lines with p53 genetic status as the only variable and used long-read RNA-seq to profile TE and TE-derived transcripts’ regulatory dynamics.
Read the full summary ↓
By using both short-read RNA-seq and long-read RNA-seq, novel TE-derived transcripts as a function of p53 status were accurately profiled for the first time. We identified in total 2503 transcripts that use TE as potential promoters, among which, 141 to 210 are activated by p53 across three cancer cell lines. We found ERVs to serve as potential driving promoters, followed by LINEs. Epigenomic information from chromatin accessibility and DNA methylation provided additional support for active promoter potential for p53 upregulated TE-derived transcripts. Short-term restoration of p53 partially recovered chronic p53-regulated TE-derived transcriptional profile; whereas gain of function TP53 mutations, R175H and R273H, did not show evidence to act via TE network. Overall, in this paper, we provide a controlled isogenic cancer cell line system with TP53 mutation status as the only genetic variable, deliver a high confidence TE and TE-derived transcript atlas, and comprehensively identify active TE promoters that are direct and indirect targets of p53.
Published in
Charting the regulatory landscape of TP53 on transposable elements in cancer
Qu X, Liang Y, McCornack C et al. · Genome research 2025 · PMID 40360186 · doi:10.1101/gr.279398.124
This dataset
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.

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 GSE262051_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1090105 and SRA study SRP496774. Searching any of these in the dataset finder brings you back here.

Study design
12 conditions, mostly in duplicate
A549_KO_DMSO_ATAC-seq_B ×2 A549_KO_DOX_ATAC-seq_B ×2 A549_WT_DMSO_ATAC-seq_B ×2 A549_WT_DOX_ATAC-seq_B ×2 HCT116_KO_DMSO_ATAC-seq_B ×2 HCT116_KO_DOX_ATAC-seq_B ×2 HCT116_WT_DMSO_ATAC-seq_B ×2 HCT116_WT_DOX_ATAC-seq_B ×2 +4 more

Supports a between-group comparison across 24 samples.

How we read this: 12 replicated groups read from 24 sample titles; they account for 24 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
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