A stem cell model identifies two alternative cell fates in CBFA2T3::GLIS2-driven acute megakaryoblastic leukemia initiation
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 82 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
- 82 samples: 16 conditions, mostly in triplicateGroups 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
- A stem cell differentiation model reveals two alternative fates in CBFA2T3::GLIS2-driven acute megakaryoblastic leukemia initiationCommunications biology · 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 GSE277004_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 PRJNA1160199 and SRA study SRP536465. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 43 samples.
How we read this: 16 replicated groups read from 82 sample titles; they account for 43 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.
- GSM8510836 CG_22_INPUT_R1
- GSM8510837 CG_22_HA_IP_R1
- GSM8510838 CG_23_INPUT_R1
- GSM8510839 CG_23_HA_IP_R1
- GSM8510840 CG_32_INPUT_R1
- GSM8510841 CG_32_HA_IP_R1
- GSM8510842 M07E_INPUT_R1
- GSM8510843 M07E_CBFA2T3_IP_R1
- GSM8510844 CG_22_HA_IP_R1.1
- GSM8510845 CG_22_INPUT_R1.1
- GSM8510846 sub_M07E_CBFA2T3_IP
- GSM8510847 sub_M07E_INPUT
- GSM8510884 WT_UNTREATED_D13 WT_UNTREATED_D
- GSM8510885 WT_DTAG13_D13 WT_DTAG13_D
- GSM8510886 WT_UNTREATED_D25 WT_UNTREATED_D
- GSM8510887 WT_DTAG13_D25 WT_DTAG13_D
- GSM8510888 CG_UNTREATED_D13_REP1 CG_UNTREATED_D13
- GSM8510889 CG_DTAG13_D13_REP1 CG_DTAG13_D13
- GSM8510890 CG_UNTREATED_D25_REP1 CG_UNTREATED_D25
- GSM8510891 CG_DTAG13_D25_REP1 CG_DTAG13_D25
- GSM8510892 CG_UNTREATED_D13_REP2 CG_UNTREATED_D13
- GSM8510893 CG_DTAG13_D13_REP2 CG_DTAG13_D13
- GSM8510894 CG_UNTREATED_D25_REP2 CG_UNTREATED_D25
- GSM8510895 CG_DTAG13_D25_REP2 CG_DTAG13_D25
- GSM8510896 CG_UNTREATED_D13_REP3 CG_UNTREATED_D13
- GSM8510897 CG_DTAG13_D13_REP3 CG_DTAG13_D13
- GSM8510898 CG_UNTREATED_D25_REP3 CG_UNTREATED_D25
- GSM8510899 CG_DTAG13_D25_REP3 CG_DTAG13_D25
- GSM8510900 CG_UNTREATED_LONGTERM_REP1 CG_UNTREATED_LONGTERM
- GSM8510901 CG_UNTREATED_LONGTERM_REP2 CG_UNTREATED_LONGTERM
- GSM8510902 CG_UNTREATED_LONGTERM_REP3 CG_UNTREATED_LONGTERM
- GSM8510903 M07E
- GSM8522879 [RNA-Seq] WT_UNTREATED_D13_REP1 [RNA-Seq] WT_UNTREATED_…
- GSM8522880 [RNA-Seq] WT_DTAG13_D13_REP1 [RNA-Seq] WT_DTAG13_D13
- GSM8522881 [RNA-Seq] CG_UNTREATED_D13_REP1 [RNA-Seq] CG_UNTREATED_…
- GSM8522882 [RNA-Seq] CG_DTAG13_D13_REP1 [RNA-Seq] CG_DTAG13_D13
- GSM8522883 [RNA-Seq] WT_UNTREATED_D22_REP1
- GSM8522884 [RNA-Seq] WT_DTAG13_D22_REP1
- GSM8522885 [RNA-Seq] CG_UNTREATED_D22_REP1 [RNA-Seq] CG_UNTREATED_…
- GSM8522886 [RNA-Seq] CG_DTAG13_D22_REP1 [RNA-Seq] CG_DTAG13_D22
+ 42 more — browse all 82 samples with per-sample file links →
- GSE277468 A stem cell model identifies two alternative cell fates in CBFA2T3::GLIS2-driven acute megakaryoblastic leukemia initiation (RNA-seq) 22 samples shares cbfa2t3, megakaryoblastic, glis2-driven, alternative, identifies, initiation, leukemia, fates
- GSE278751 A stem cell model identifies two alternative cell fates in CBFA2T3::GLIS2-driven acute megakaryoblastic leukemia initiation (Multiome scRNA-seq/scATAC-seq) 14 samples shares cbfa2t3, megakaryoblastic, glis2-driven, alternative, identifies, initiation, leukemia, fates
- GSE277485 A stem cell model identifies two alternative cell fates in CBFA2T3::GLIS2-driven acute megakaryoblastic leukemia initiation (scRNA-seq) 14 samples shares cbfa2t3, megakaryoblastic, glis2-driven, alternative, identifies, initiation, leukemia, fates
- GSE115656 Alternative splicing analysis of pediatric B-cell acute lymphoblastic leukemia compared to normal bone marrow derived pro-B cells 38 samples shares alternative, leukemia
- GSE250435 High-density resolution of the Kaposi’s Sarcoma associated Herpesvirus transcriptome identifies novel transcript isoforms generated by long-range transcription and alternative splicing 23 samples shares alternative, identifies
- GSE281881 Proteomic study identifies Aurora-A mediated regulation of alternative splicing through multiple splicing factors 20 samples shares alternative, identifies
- GSE330338 Multi-Omics Integration Identifies Critical Molecular Drivers and Alternative Splicing Events in Small Cell Lung Cancer 16 samples shares alternative, identifies
- GSE268961 Heterochromatin Protein ERH represses alternative cell fates during early mammalian differentiation [CUT&RUN] 10 samples shares alternative, fates
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.