DNMT1 loss leads to hypermethylation of a subset of late replicating domains by DNMT3A.
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 42 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
- 42 samples: 42 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
- DNMT1 loss leads to hypermethylation of a subset of late replicating domains by DNMT3APLoS genetics · 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 GSE297847_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 PRJNA1266419 and SRA study SRP586837. 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 42 sample titles: 42 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.
- GSM8999215 HCT116_WGBS
- GSM8999216 DNMT1 KO WGBS
- GSM8999217 HCT116_rep_1_early_repli_seq
- GSM8999218 HCT116_rep_1_late_repli_seq
- GSM8999219 HCT116_rep_2_early_repli_seq
- GSM8999220 HCT116_rep_2_late_repli_seq
- GSM8999221 DNMT1_KO_rep_1_early_repli_seq
- GSM8999222 DNMT1_KO_rep_1_late_repli_seq
- GSM8999223 DNMT1_KO_rep_2_early_repli_seq
- GSM8999224 DNMT1_KO_rep_2_late_repli_seq
- GSM8999225 HCT116_h3k36me2_chip_rep_1_chip_seq
- GSM8999226 HCT116_h3k36me2_in_rep_1_chip_seq
- GSM8999227 HCT116_h3k36me2_chip_rep_2_chip_seq
- GSM8999228 HCT116_h3k36me2_in_rep_2_chip_seq
- GSM8999229 DNMT1_KO_h3k36me2_chip_rep_1_chip_seq
- GSM8999230 DNMT1_KO_h3k36me2_in_rep_1_chip_seq
- GSM8999231 DNMT1_KO_h3k36me2_chip_rep_2_chip_seq
- GSM8999232 DNMT1_KO_h3k36me2_in_rep_2_chip_seq
- GSM8999239 DNMT1_KO_h3k36me3_chip_rep_1_chip_seq
- GSM8999240 DNMT1_KO_h3k36me3_in_rep_1_chip_seq
- GSM8999241 DNMT1_KO_h3k36me3_chip_rep_2_chip_seq
- GSM8999242 DNMT1_KO_h3k36me3_in_rep__chip_seq
- GSM8999243 DNMT1_KO_h3k4me3_chip_rep_1_chip_seq
- GSM8999244 DNMT1_KO_h3k4me3_in_rep_1_chip_seq
- GSM8999245 DNMT1_KO_h3k4me3_chip_rep_2_chip_seq
- GSM8999246 DNMT1_KO_h3k4me3_in_rep_2_chip_seq
- GSM8999247 DNMT1_KO_h3k9me3_chip_rep_1_chip_seq
- GSM8999248 DNMT1_KO_h3k27me3_chip_rep_1_chip_seq
- GSM8999249 DNMT1_KO_h3k9me3_h3K27me3_in_rep_1_chip_seq
- GSM8999250 DNMT1_KO_h3k9me3_chip_rep_2_chip_seq
- GSM8999251 DNMT1_KO_h3k27me3_chip_rep_2_chip_seq
- GSM8999252 DNMT1_KO_h3k9me3_h3K27me3_in_rep_2_chip_seq
- GSM9525670 HCT116_rep_1_RNA-seq
- GSM9525671 HCT116_rep_2_RNA-seq
- GSM9525672 HCT116_rep_3_RNA-seq
- GSM9525673 HCT116_rep_7_RNA-seq
- GSM9525674 HCT116_rep_8_RNA-seq
- GSM9525675 HCT116_rep_9_RNA-seq
- GSM9525676 DNMT1_KO_rep_1_RNA_seq
- GSM9525677 DNMT1_KO_rep_2_RNA_seq
+ 2 more — browse all 42 samples with per-sample file links →
- GSE297781 DNMT1 loss leads to hypermethylation of a subset of late replicating domains by DNMT3A (histone ChIP-seq) 14 samples shares dnmt3a, dnmt1, hypermethylation, replicating, domains, subset, leads, loss
- GSE319745 DNMT1 loss leads to hypermethylation of a subset of late replicating domains by DNMT3A (RNA-seq) 10 samples shares dnmt3a, dnmt1, hypermethylation, replicating, domains, subset, leads, loss
- GSE297779 DNMT1 loss leads to hypermethylation of a subset of late replicating domains by DNMT3A (H3K36me2 native ChIP-seq) 8 samples shares dnmt3a, dnmt1, hypermethylation, replicating, domains, subset, leads, loss
- GSE63280 Targeted disruption of DNMT1, 3A and 3B in human embryonic stem cells [DNMT3A/3B DKO] 8 samples shares dnmt3a, dnmt1
- GSE20236 Human Aging-associated DNA Hypermethylation Occurs Preferentially at Bivalent Chromatin Domains 93 samples shares hypermethylation, domains
- GSE20242 Human Aging-associated DNA Hypermethylation Occurs Preferentially at Bivalent Chromatin Domains 50 samples shares hypermethylation, domains
- GSE297413 Genomic and transcriptomic determinants of clinical outcomes in patients with AML and DNMT3A mutations 264 samples shares dnmt3a
- GSE233730 Understanding the regulation of DNMT1 activity using unbiased functional genomics 125 samples shares dnmt1
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.