Roles of super-enhancer-driven genes in T-ALL cells
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 16 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
- 16 samples: 16 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
- Super-Enhancer-Driven IRF2BP2 is Activated by Master Transcription Factors and Sustains T-ALL Cell Growth and SurvivalAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE267758_RAW.tar (it can run to tens of gigabytes).
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 PRJNA1112807 and SRA study SRP508383. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 16 sample titles: 16 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.
- GSM8275216 J.gamma1, H3K27ac
- GSM8275217 J.gamma1, Input
- GSM8275218 T-ALL_1, H3K27ac
- GSM8275219 T-ALL_1, Input
- GSM8275220 T-ALL_2, H3K27ac
- GSM8275221 T-ALL_2, Input
- GSM8275222 T-ALL_3, H3K27ac
- GSM8275223 T-ALL_3, Input
- GSM8275224 T-ALL_4, H3K27ac
- GSM8275225 T-ALL_4, Input
- GSM8275226 T-ALL_5, H3K27ac
- GSM8275227 T-ALL_5, Input
- GSM8275228 T-ALL_6, H3K27ac
- GSM8275229 T-ALL_6, Input
- GSM8275230 T-ALL_7, H3K27ac
- GSM8275231 T-ALL_7, Input
- GSE193713 Genome-wide maps of chromatin state for histone modifications and chromatin remodeling factors in WT and PHF6-KO conditions in Jurkat T-ALL cells. 20 samples shares t-all
- GSE243772 Identification and targeting of extremely high-risk gamma delta T-ALL in children [HiChIP, ChIP and ATAC] 62 samples shares t-all
- GSE253267 ChIP-seq analysis of ELK3 in T-ALL cells 6 samples shares t-all
- GSE252992 Enhancer looping protein LDB1 affects T-ALL cell proliferation by cooperating with master transcription factors [CUT&Tag] 16 samples shares t-all
- GSE267451 IRF2BP2 affects T-ALL cell proliferation by cooperating with master transcription factors [Cut&Tag] 10 samples shares t-all
- GSE230586 ATAC-seq analysis for T-ALL cell lines 6 samples shares t-all
- GSE263977 Chromatin accessibility landscapes across T-ALL 5 samples shares t-all
- GSE26713 Integrated transcript and genome analyses reveal NKX2-1 and MEF2C as potential oncogenes in T-ALL 124 samples shares t-all
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.