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ChAHP Silences SINE Retrotransposons by Inhibiting TFIIIB Recruitment [ChIP-seq]

GSE297834 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 72 samples Submitted 2026/05/01 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 72 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
72 samples: 33 conditions, mostly in duplicateGroups 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
ChAHP silences SINE retrotransposons by inhibiting TFIIIB recruitmentMolecular cell · 2026
Summary (written by the submitters)
Short interspersed nuclear elements (SINEs) are abundant non-autonomous transposable elements (TEs) derived from RNA polymerase III (Pol III)-transcribed short non-coding RNAs. SINEs retain sequence features recognized by the Pol III machinery and constitute a substantial portion of vertebrate genomes. Despite their impact on genome stability and evolution, the mechanisms governing SINE transcription remain poorly understood.
Read the full summary ↓
Although DNA methylation and heterochromatin formation have been implicated in their repression, we find these pathways play only a minor role in mouse embryonic stem cells. Instead, we identify the ChAHP complex as a key repressor of SINE B2 elements. ChAHP directly inhibits Pol III transcription by blocking TFIIIB recruitment without affecting TFIIIC binding. This selective interference prevents transcription initiation and highlights a distinct regulatory mechanism. Our findings establish ChAHP as a non-canonical repressor of Pol III-dependent SINE transcription, offering new insights into the control of this pervasive class of non-coding genomic elements.
Published in
ChAHP silences SINE retrotransposons by inhibiting TFIIIB recruitment
Schnabl-Baumgartner J, Mohn F, Schwaiger M et al. · Molecular cell 2026 · PMID 42413492 · doi:10.1016/j.molcel.2026.06.024
This dataset
Open GSE297834 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE297834_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 peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1266252 and SRA study SRP587036. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42413492) ↗
Study design
33 conditions, mostly in duplicate
ADNP ChIP in ADNP-3xFLAG mESC, ×6 ADNP ChIP in GTF3C1-FKBP12 mESC, dTAG13, ×2 ADNP ChIP in GTF3C1-FKBP12 mESC, ctrl, ×2 ADNP Input in GTF3C1-FKBP12 mESC, ctrl, ×2 POL3 ChIP in GTF3C1-FKBP12 mESC, dTAG13, ×2 POL3 ChIP in GTF3C1-FKBP12 mESC, ctrl, ×2 POL3 Input in GTF3C1-FKBP12 mESC, ctrl, ×2 ADNP ChIP in ADNP-FKBP12 mESC, dTAG13, 24h, ×2 +27 more

Supports a between-group comparison across 70 samples.

How we read this: 33 replicated groups read from 72 sample titles; they account for 70 of them. 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

+ 32 more — browse all 72 samples with per-sample file links →

Datasets on a similar topic

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