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Interpreting regulatory mechanisms of Hippo signaling through a deep learning sequence model

GSE252463 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 59 samples Submitted 2025/01/31 Platform GPL19057Platform GPL30172
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 59 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
59 samples: 20 conditions, mostly in triplicateGroups 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
Interpreting regulatory mechanisms of Hippo signaling through a deep learning sequence modelCell genomics · 2025
Summary (written by the submitters)
How specific cells respond to signaling pathways is largely encoded in the DNA sequence. However, the sequence rules result from complex interactions between signaling and cell type-specific transcription factors and are considered intractable by traditional methods. Here, we leverage interpretable deep learning on high-resolution data and extensive validation experiments to identify the sequence rules for the Hippo pathway in mouse trophoblast stem cells.
Read the full summary ↓
We show that TEAD4 and YAP1 engage in two types of cooperativity. First, their binding is enhanced by cell type-specific transcription factors, including TFAP2C, in a distance-dependent manner. Second, a strictly-spaced Tead double motif is a canonical Hippo pathway element that mediates strong Tead4 cooperativity through transient protein-protein interactions on DNA. These mechanisms occur genome-wide and allow us to predict how small sequence changes alter the activity of enhancers in vivo. This illustrates the power of interpretable deep learning to decode canonical and cell type-specific sequence rules of signaling pathways. These super series include ChIP-nexus data for TF binding (TEAD4, YAP1, CDX2, TFAP2C, GATA3), H3K27ac Chip-seq, ATAC-seq, RNA-seq, and Nascent RNA captured through the TT-seq method in mouse trophoblast stem cells.
Published in
Interpreting regulatory mechanisms of Hippo signaling through a deep learning sequence model
Dalal K, McAnany C, Weilert M et al. · Cell genomics 2025 · PMID 40174587 · doi:10.1016/j.xgen.2025.100821
This dataset
Open GSE252463 in finder → Find more mouse 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 GSE252463_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 PRJNA1060687 and SRA study SRP481115. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40174587) ↗
Study design
20 conditions, mostly in triplicate
mTSC,Yap1,nexus, ×5 mTSC,Tfap2c,nexus, ×4 mTSC,Cdx2,nexus, ×4 mTSC,Gata3,nexus, ×4 mTSC,atac, ×3 mTSC,Tead4,nexus, ×3 mTSC,PolII,nexus, ×3 mTSC,H3K27ac,seq, ×3 +13 more

Supports a between-group comparison across 58 samples.

How we read this: 20 replicated groups read from 59 sample titles; they account for 58 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

+ 19 more — browse all 59 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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