Editing DNA methylation in the mammalian genome
ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 6 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
- Mus musculus (mouse)
- Samples
- 6 samples: 6 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.
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 GSE83890_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 PRJNA327297 and SRA study SRP077610. 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 6 sample titles: 6 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.
- GSM2220928 input sample prior to IP
- GSM2220929 ChIP of V6.5 mock cells (without dCas9-Dnmt3a)
- GSM2220930 ChIP of dCas9-Dnmt3a alone
- GSM2220931 ChIP of dCas9-Dnmt3a + scrambled gRNA
- GSM2220932 ChIP of dCas9-Dnmt3a + target gRNA (miR290)
- GSM2220933 ChIP of dCas9-Dnmt3a + target gRNA (Dazl-Snrpn)
- GSE319420 Uncovering Critical Lysines in Mammalian Histone H3 with High-Throughput CRISPR Prime Editing [CUT&Tag] 26 samples shares mammalian, editing
- GSE333859 A global view of cell-type-specific temporal dynamics in mammalian aging 1496 samples shares mammalian
- GSE331488 A global view of cell-type-specific temporal dynamics in mammalian aging [I] 771 samples shares mammalian
- GSE331489 A global view of cell-type-specific temporal dynamics in mammalian aging [II] 725 samples shares mammalian
- GSE297574 H3K9 di-methylation dynamics underlies mouse minor zygotic genome activation [spike-in ChIP-seq of mESC] 64 samples shares methylation
- GSE296991 H3K9 di-methylation dynamics underlies mouse minor zygotic genome activation [CATCH-seq of early embryo] 23 samples shares methylation
- GSE311724 Effects of loss of KAT6A on histone acetylation and methylation levels, as well as MLL1 and POLR2A occupancy 134 samples shares methylation
- GSE243566 Systematic perturbations of SETD2, NSD1, NSD2, NSD3 and ASH1L reveal their distinct contributions to H3K36 methylation 125 samples shares methylation
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.