DNA methylation stochasticity is linked to transcriptional variability and identifies convergent epigenetic disruption across genetically-defined subtypes of AML
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)
- Topic
- Acute myeloid leukemiaThis series is part of our curated briefing on it.
- Samples
- 82 samples: 82 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
- DNA Methylation Stochasticity Is Linked to Transcriptional Variability and Convergent Epigenetic Disruption across Genetic Subtypes of Acute Myeloid LeukemiaCancer research · 2026
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 GSE319461_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 PRJNA1423316 and SRA study SRP676889. Searching any of these in the dataset finder brings you back here.
Supports a case/control comparison: 3 samples read as cases, 3 as controls.
How we read this: Read from 82 sample titles: 82 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.
- GSM8610680 AML JH4198, scRNA-seq
- GSM8610681 AML JH4316, scMultiome GEX
- GSM8610682 AML JH4639, scMultiome GEX
- GSM8610683 AML JH5034, scMultiome GEX
- GSM8610684 AML JH5205, scMultiome GEX
- GSM8610685 AML JH5236, scRNA-seq
- GSM8610686 AML JH6048, scMultiome GEX
- GSM8610687 AML SU048, scRNA-seq
- GSM8610688 AML SU050, scRNA-seq
- GSM8610689 AML SU070, scRNA-seq
- GSM8610690 AML SU075, scMultiome GEX
- GSM8610691 AML SU204, scRNA-seq
- GSM8610692 AML SU209, scRNA-seq
- GSM8610693 AML SU214 Replicate 1, scRNA-seq
- GSM8610694 AML SU214 Replicate 2, scMultiome GEX
- GSM8610695 AML SU227, scMultiome GEX
- GSM8610696 AML SU270, scRNA-seq
- GSM8610697 AML SU344, scRNA-seq
- GSM8610698 AML SU462, scRNA-seq
- GSM8610699 AML SU494, scMultiome GEX
- GSM8610700 AML SU504 Replicate 1, scRNA-seq
- GSM8610701 AML SU504 Replicate 2, scMultiome GEX
- GSM8610702 AML SU758, scMultiome GEX
- GSM8610703 CD34-1, scRNA-seq
- GSM8610704 CD34-2, scRNA-seq
- GSM8610705 GMP-1, scRNA-seq
- GSM9518079 AML JH4198, WGBS
- GSM9518080 AML JH4316, WGBS
- GSM9518081 AML JH4639, WGBS
- GSM9518082 AML JH5034, WGBS
- GSM9518083 AML JH5205, WGBS
- GSM9518084 AML JH5236, WGBS
- GSM9518085 AML SU048, WGBS
- GSM9518086 AML SU070, WGBS
- GSM9518087 AML SU075, WGBS
- GSM9518088 AML SU204, WGBS
- GSM9518089 AML SU209, WGBS
- GSM9518090 AML SU214, WGBS
- GSM9518091 AML SU270, WGBS
- GSM9518092 AML SU344, WGBS
+ 42 more — browse all 82 samples with per-sample file links →
- GSE122515 Bonn Dataset 3 of meta-analysis on AML classification, RNAseq dataset 0 samples same disease briefing · shares aml
- GSE137851 The long non-coding RNA HOXB-AS3 regulates ribosomal RNA transcription in NPM1-mutated acute myeloid leukemia 387 samples same disease briefing
- GSE270185 Repurposing CD19-directed immunotherapies for pediatric t(8;21) acute myeloid leukemia 249 samples same disease briefing
- GSE137574 Kevetrin induces apoptosis in TP53 wild-type and mutant acute myeloid leukemia cells 24 samples same disease briefing
- GSE129437 Genome-wde mapping of BRD9 binding sites and gene expression changes upon BRD9 knockdown 6 samples same disease briefing
- GSE303795 Halofuginone exerts broad-spectrum cytotoxic effects by regulating p-eIF2α-S100A8/A9-calcium signaling, inhibiting global protein synthesis, and reversing the resistance of idarubicin in acute myeloid leukemia 6 samples same disease briefing
- GSE319444 DNA methylation stochasticity is linked to transcriptional variability and identifies convergent epigenetic disruption across genetically-defined subtypes of AML [PrimaryAML_WGBS] 20 samples shares aml, genetically-defined, stochasticity, methylation, variability, identifies, convergent, epigenetic
- GSE319448 DNA methylation stochasticity is linked to transcriptional variability and identifies convergent epigenetic disruption across genetically-defined subtypes of AML [CellCulture_WGBS] 12 samples shares aml, genetically-defined, stochasticity, methylation, variability, identifies, convergent, epigenetic
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.