← BioTransfer GEO Dataset Finder
GEO series

ChIP-seq of trascription factors after SMARCA2/4 degrader treatment

GSE280803 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 21 samples Submitted 2025/02/01 Platform GPL24676Platform GPL30173
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 21 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
21 samples: 6 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)
Transcription factors (TFs) and transcriptional coregulators represent an emerging and exciting class of targets. By quantifying target gene modulation, gene regulatory networks (GRNs) delineate disease biology and evaluate pharmacological agents targeting these regulators. However, none of the existing methods are explicitly designed to measure the effects of perturbations in which TF expression is decoupled from its activity.
Read the full summary ↓
We present Epiregulon, a method that constructs GRNs from single-cell ATAC and RNA data for accurate prediction of TF activity. Our weight estimation, based on co-occurrence of TF expression and chromatin accessibility, avoids erroneous inflation of TF activity as seen with TF expression only approaches. Furthermore, our utilization of ChIP-seq data expands inference to include transcriptional coregulators lacking defined motifs. Our extensive network of regulators facilitates identification of cell-state specific interaction partners. Using Epiregulon, we uncover divergent cell fate transitions of prostate cancer cells driven by NKX2-1 and GATA6 expression. We accurately predicted the effects of AR inhibition across various drug modalities. Finally, Epiregulon was able to infer the context-dependent activity of a chromatin remodeler lacking a defined motif sequence and recapitulate the unique etiologies of prostate cancer. By mapping out the network of key regulators across a multitude of perturbations, Epiregulon can accelerate the discovery of new therapeutics targeting transcription factors.
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 GSE280803_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 PRJNA1180564 and SRA study SRP542404. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in duplicate
MDA-PCa-2b, DMSO, SMARCA4, ×2 MDA-PCa-2b, SMARCA2/4 degrader, SMARCA4, ×2 MDA-PCa-2b, DMSO, AR, ×2 MDA-PCa-2b, SMARCA2/4 degrader, AR, ×2 MDA-PCa-2b, DMSO, FOXA1, ×2 MDA-PCa-2b, SMARCA2/4 degrader, FOXA1, ×2 DU145, DMSO, input ×1 DU145, DMSO, SMARCA ×1 +7 more

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 21 sample titles; they account for 12 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

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.