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Systemic comparison of dCas9-based DNA methylation editing systems for specificity and stability [RNAseq_secondRun]

GSE297435 Homo sapiens (human) DNA methylation 42 samples Submitted 2026/01/13 Platform GPL34284
At a glance

DNA methylation · human · 42 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Homo sapiens (human)
Samples
42 samples: 36 distinct titles, 6 of them repeatedGroups 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
Comprehensive profiling of CRISPR/dCas9 epigenome editors indicates a complex link between on and off target effectsGenome biology · 2026
Summary (written by the submitters)
Manipulating epigenetic layers during transcriptional regulation poses significant challenges, yet advancements in dCas-based systems offer promising avenues for targeted manipulation. DNA methylation, a widely studied epigenetic mechanism in mammals, has benefited from recent methodological developments enabling comprehensive profiling across various tissues and conditions.
Read the full summary ↓
While traditional methods for altering methylation levels lacked specificity and often induced pleiotropic effects, the advent of CRISPR technology has revolutionized this landscape, offering precise manipulation of methylation levels at specific loci. However, despite their advantages, these techniques have limitations, underscoring the need for thorough characterization of existing tools and the development of novel approaches. This study aimed to comprehensively compare the on-target/off-target effects of several available epimodifier systems and explore the potential of a multimerization strategy for epimodifier domains. Through our investigations, we provide insights into the methylation-mediated control of human gene expression and the utility of dCas9-based methylation editors.
Published in
Comprehensive profiling of CRISPR/dCas9 epigenome editors indicates a complex link between on and off target effects
Pahlevan Kakhki M, Rangani F, Ewing E et al. · Genome biology 2026 · PMID 41620608 · doi:10.1186/s13059-026-03967-6
This dataset
Open GSE297435 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE297435_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

What the files usually look like: Look for beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA1263845 and SRA study SRP585918. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41620608) ↗
Study design
36 distinct titles, 6 of them repeated
dDNMT3A_BACH2_g8_3d ×2 Non transfected cells (NT)_3d ×2 dDNMT3A_BACH2_g8_7d ×2 Non transfected cells (NT)_7d ×2 dDNMT3A_BACH2_g8_30d ×2 Non transfected cells (NT)_30d ×2

How we read this: Read from 42 sample titles: 36 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.

Samples in this study

+ 2 more — browse all 42 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.