HDAC2-Mediated Recruitment of METTL3 to Chromatin Regulates Human Embryonic Stem Cell Differentiation
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 74 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
- 74 samples: 74 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
- HDAC2-mediated recruitment of METTL3 to chromatin regulates human embryonic stem cell differentiationCell reports · 2025
Read the full summary ↓
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 GSE288140_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 PRJNA1216271 and SRA study SRP560226. 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 74 sample titles: 74 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.
- GSM8759730 hESCs, WT-rep1, RNA-seq
- GSM8759731 hESCs, WT-rep2, RNA-seq
- GSM8759732 hESCs, HDAC2-KO#1-rep1, RNA-seq
- GSM8759733 hESCs, HDAC2-KO#1-rep2, RNA-seq
- GSM8759734 hESCs, HDAC2-KO#2-rep1, RNA-seq
- GSM8759735 hESCs, HDAC2-KO#2-rep2, RNA-seq
- GSM8759736 hESCs, METTL3-KO#1-dox-_rep1, RNA-seq
- GSM8759737 hESCs, METTL3-KO#1-dox-_rep2, RNA-seq
- GSM8759738 hESCs, METTL3-KO#2-dox-_rep1, RNA-seq
- GSM8759739 hESCs, METTL3-KO#2-dox-_rep2, RNA-seq
- GSM8759740 hESCs, METTL3-KO#1-dox+_rep1, RNA-seq
- GSM8759741 hESCs, METTL3-KO#1-dox+_rep2, RNA-seq
- GSM8759742 hESCs, METTL3-KO#2-dox+_rep1, RNA-seq
- GSM8759743 hESCs, METTL3-KO#2-dox+_rep2, RNA-seq
- GSM8759744 hESCs, WT-rep1, m6A-seq, Input
- GSM8759745 hESCs, WT-rep2, m6A-seq, Input
- GSM8759746 hESCs, WT-rep1, m6A-seq, IP
- GSM8759747 hESCs, WT-rep2, m6A-seq, IP
- GSM8759748 hESCs, HDAC2-KO#1, m6A-seq, Input
- GSM8759749 hESCs, HDAC2-KO#2, m6A-seq, Input
- GSM8759750 hESCs, HDAC2-KO#1, m6A-seq, IP
- GSM8759751 hESCs, HDAC2-KO#2, m6A-seq, IP
- GSM8759752 hESCs, METTL3-KO#1-dox-, m6A, Input
- GSM8759753 hESCs, METTL3-KO#1-dox-, m6A, IP
- GSM8759754 hESCs, METTL3-KO#1-dox+, m6A, Input
- GSM8759755 hESCs, METTL3-KO#1-dox+, m6A, IP
- GSM8759756 hESCs, WT-rep1, 0h, RNA-seq
- GSM8759757 hESCs, WT-rep2, 0h, RNA-seq
- GSM8759758 hESCs, WT-rep1, 2h, RNA-seq
- GSM8759759 hESCs, WT-rep2, 2h, RNA-seq
- GSM8759760 hESCs, WT-rep1, 4h, RNA-seq
- GSM8759761 hESCs, WT-rep2, 4h, RNA-seq
- GSM8759762 hESCs, HDAC2-KO#1, 0h, RNA-seq
- GSM8759763 hESCs, HDAC2-KO#2, 0h, RNA-seq
- GSM8759764 hESCs, HDAC2-KO#1, 2h, RNA-seq
- GSM8759765 hESCs, HDAC2-KO#2, 2h, RNA-seq
- GSM8759766 hESCs, HDAC2-KO#1, 4h, RNA-seq
- GSM8759767 hESCs, HDAC2-KO#2, 4h, RNA-seq
- GSM8759768 hESCs, WT-rep1, polysome, RNA-seq
- GSM8759769 hESCs, WT-rep2, polysome, RNA-seq
+ 34 more — browse all 74 samples with per-sample file links →
- GSE268091 Mechano-osmotic signals control chromatin state and fate transitions in pluripotent stem cells 61 samples shares chromatin, stem
- GSE310696 Systematic Benchmarking of CUT&Tag Improves the Reliability and Reproducibility of Chromatin Analysis [Crosslinking] 31 samples shares chromatin, stem
- GSE216483 METTL3-dependent m6A RNA methylation regulates activation of the NLRP3 inflammasome 18 samples shares mettl3
- GSE281124 Loss of METTL3 potentiates transposable element expression and drives inflammation-induced cell plasticity in the mammary epithelium 24 samples shares mettl3
- GSE313114 Inhibition of METTL3 by STC-15 induces RNA misprocessing that results in dsRNA formation and activates innate immunity [RNA-Seq dose_response] 21 samples shares mettl3
- GSE325835 BATF3 regulates chromatin accessibility during CD8+ T lymphocyte differentiation 36 samples shares chromatin
- GSE210848 Lineage specific chromatin modification guides Pax5 mediated gene expression in neuroendocrine like differentiation of prostate cancer 32 samples shares chromatin
- GSE271127 Substained DNA demethylation and discordant chromatin accessibility dynamics define enhancer regulation during neural progenitor cell differentiation [ATACme] 18 samples shares chromatin
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.