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IRF2BP2 affects T-ALL cell proliferation by cooperating with master transcription factors [Cut&Tag]

GSE267451 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 10 samples Submitted 2024/10/26 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 10 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
10 samples: 5 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
Super-Enhancer-Driven IRF2BP2 is Activated by Master Transcription Factors and Sustains T-ALL Cell Growth and SurvivalAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
Summary (written by the submitters)
Knocking down IRF2BP2 led to decreased T-cell acute lymphoblastic leukemia (T-ALL) cell survival and growth both in vitro and in vivo. To explore IRF2BP2-dependent gene regulation, RNA-seq analysis was conducted and the differently expressed genes were revealed in the IRF2BP2-knockdown J.gamma1 cells in comparison to control group. Cleavage Under Targets and Tagmentation (CUT&Tag) showed the co-localization of IRF2BP2 with several master transcription factors on chromatin in J.gamma1 cells.
Published in
Super-Enhancer-Driven IRF2BP2 is Activated by Master Transcription Factors and Sustains T-ALL Cell Growth and Survival
Yu J, Zhang Z, Chen Y et al. · Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2025 · PMID 39454110 · doi:10.1002/advs.202407113
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 GSE267451_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 PRJNA1111543 and SRA study SRP507635. Searching any of these in the dataset finder brings you back here.

Study design
5 conditions, mostly in duplicate
J.gamma1,ELF1,CUT&Tag, ×2 J.gamma1,ERG,CUT&Tag, ×2 J.gamma1,IRF2BP2,CUT&Tag, ×2 J.gamma1,RUNX1,CUT&Tag, ×2 J.gamma1,ETS1,CUT&Tag, ×2

Supports a between-group comparison across 10 samples.

How we read this: 5 replicated groups read from 10 sample titles; they account for 10 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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