Multiplex Epigenome Editing of MECP2 to Rescue Rett Syndrome Neurons [ChIP-Seq]
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 13 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
- 13 samples: 13 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 GSE208221_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 PRJNA858807 and SRA study SRP386434. 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 13 sample titles: 13 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.
- GSM6339277 dC-T_11+3, input
- GSM6339278 dC-dT_2+3, input
- GSM6339279 dC-T_11+sc, input
- GSM6339280 dC-dT_2+sc, input
- GSM6339281 dC-T_11+3, chip
- GSM6339282 dC-dT_2+3, chip
- GSM6339283 dC-T_11+sc, chip
- GSM6339284 dC-dT_2+sc, chip
- GSM6339285 neuron_mock
- GSM6339286 neuron_edited
- GSM6339287 dCpf1-CTCF-input
- GSM6339288 dCpf1-CTCF-mock
- GSM6339289 dCpf1-CTCF
- GSE273682 Sustained and specific multiplexed immune checkpoint modulation in CAR T cells induced by targeted epigenome editing 15 samples shares multiplex, epigenome, editing
- GSE268177 Modulating alternative splicing of MECP2: A potential therapeutic strategy for Rett syndrome 93 samples shares mecp2, rett
- GSE230714 MECP2 directly interacts with RNA polymerase II to modulate transcription in human neurons [RNA-Seq] 12 samples shares mecp2, neurons
- GSE318601 Early differential impact of MeCP2 mutations on functional networks in Rett syndrome patient-derived human cortical organoids [RNA-Seq] 8 samples shares mecp2, rett
- GSE243185 Activation of the imprinted Prader-Willi Syndrome locus by CRISPR-based epigenome editing [RNA-seq] 18 samples shares epigenome, editing
- GSE291384 Multiplex base editing of BCL11A regulatory elements to treat sickle cell disease 12 samples shares multiplex, editing
- GSE303838 MECP2 Mutations Rewire Human ESC Fate and Bias Cortical Lineage Commitment [RNA-Seq] 48 samples shares mecp2
- GSE280574 mChIP-seq for high-throughput epigenomic profiling reveals a decoupling of H2A.Z and H3K4me3 in cancer 576 samples shares chip-seq
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.