GEO series

SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes [ChIP-seq]

GSE236449 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 13 samples Submitted 2024/07/01 Platform GPL17021

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 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: Mus musculus (mouse)
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.

Summary (written by the submitters)

A common way to investigate gene regulatory mechanisms is to identify differentially expressed genes using transcriptomics, find their candidate enhancers using epigenomics, and search for over-represented transcription factor (TF) motifs in these enhancers using bioinformatics tools. A related follow-up task is to model gene expression as a function of enhancer sequences and rank TF motifs by their contribution to such models, thus prioritizing among regulators.

Read the full summary ↓

We present a new computational tool called SEAMoD that performs the above tasks of motif finding and sequence-to-expression modeling simultaneously. It trains a convolutional neural network model to relate enhancer sequences to differential expression in one or more biological conditions. The model uses TF motifs to interpret the sequences, learning these motifs and their relative importance to each biological condition from data. It also utilizes epigenomic information in the form of activity scores of putative enhancers and automatically searches for the most promising enhancer for each gene. Compared to existing neural network models of non-coding sequences, SEAMoD uses far fewer parameters, requires far less training data, and emphasizes biological interpretability. We used SEAMoD to understand regulatory mechanisms underlying the differentiation of neural stem cell (NSC) derived from mouse forebrain. We profiled gene expression and histone modifications in NSC and three differentiated cell types and used SEAMoD to model differential expression of nearly 12,000 genes with an accuracy of 81%, in the process identifying the Olig2, E2f family TFs, Foxo3, and Tcf4 as key transcriptional regulators of the differentiation process.

This dataset

Open GSE236449 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 GSE236449_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 PRJNA990822 and SRA study SRP447200. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

13 samples, each with a distinct title — no replicated groups

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.

Samples in this study

GSM7547698 318_NSC_DIV0_H3k27ac
GSM7547699 319_NSC_DIV0_H3k27ac
GSM7547700 320_NSC_DIV0_input
GSM7547702 336_NSC-NEU_DIV4_H3k27ac
GSM7547703 337_NSC-NEU_DIV4_H3k27ac
GSM7547704 338_NSC-NEU_DIV4_input
GSM7547705 494_NSC-AST_DIV4_H3k27ac
GSM7547706 495_NSC-AST_DIV4_H3k27ac
GSM7547707 497_NSC-OLG_DIV4_H3k27ac
GSM7547709 498_NSC-OLG_DIV4_H3k27ac
GSM7547710 499_NSC-OLG_DIV4_input
GSM7547711 625_NSC-AST_DIV4_input
GSM7547712 626_NSC-AST_DIV4_input

Datasets on a similar topic

GSE236450 SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes 24 samples shares cis-regulatory, differentially, interpretable, expressed, network, seamod, neural
GSE236448 SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes [RNA-seq] 11 samples shares cis-regulatory, differentially, interpretable, expressed, network, seamod, neural
GSE38377 Latent enhancers unveiled by stimulation expand and adapt the available cis-regulatory repertoire (ChIP-seq) 115 samples shares cis-regulatory, chip-seq
GSE252457 Interpretable deep learning reveals the sequence rules of Hippo signaling (ChIP-Seq) 47 samples shares interpretable, chip-seq
GSE255089 Nucleosomal and subnucleosomal organization of transcription cis-regulatory elements in mouse embryonic stem cells (chip-seq gradient) 42 samples shares cis-regulatory, chip-seq
GSE267882 Foxp3 and BATF cooperatively direct cis-regulatory programs and gene expression for effector Treg cell differentiation [ChIP-Seq] 34 samples shares cis-regulatory, chip-seq
GSE257545 Top2a Differentially Modulates Promoter Activity in Embryonic Stem Cells via Trim28 Recruitment and PRC2 Displacement [ChIP-Seq] 16 samples shares differentially, chip-seq
GSE305064 FOXP3 Expression Depends on Cell-Type Specific Cis-Regulatory Element and Transcription Factor Circuitry [mouse_Tconv_ChIP-seq] 15 samples shares cis-regulatory, chip-seq

Search all mouse ChIP / ATAC / CUT&Tag datasets in GEO →

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