SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes
ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 24 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
- 24 samples: 24 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.
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 GSE236450_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 PRJNA990819 and SRA study SRP447200. 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 24 sample titles: 24 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.
- GSM7547609 NSC_DIV0_1_RNA
- GSM7547611 NSC_DIV0_2_RNA
- GSM7547612 NSC_DIV0_3_RNA
- GSM7547613 NSC-AST_DIV4_1_RNA
- GSM7547614 NSC-AST_DIV4_2_RNA
- GSM7547615 NSC-AST_DIV4_3_RNA
- GSM7547616 NSC-NEU_DIV4_1_RNA
- GSM7547618 NSC-NEU_DIV4_2_RNA
- GSM7547619 NSC-NEU_DIV4_3_RNA
- GSM7547620 NSC-OLG_DIV4_1_RNA
- GSM7547621 NSC-OLG_DIV4_2_RNA
- 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
- GSE236449 SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes [ChIP-seq] 13 samples shares cis-regulatory, differentially, interpretable, expressed, network, seamod, neural, fully
- 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, fully
- GSE264172 The cis-regulatory logic integrating spatial and temporal patterning in the vertebrate neural tube 184 samples shares cis-regulatory, neural
- GSE279050 Transcriptional vulnerability to amyloid-β and tau pathologies differentially disrupts emotional and memory neural circuits 39 samples shares differentially, neural
- GSE301315 Differentially expressed genes in bone marrows from C57/BL6 wild-type, Cdk19null, Cdk8flox/flox VaviCe or DKO (Cdk8 flox/flox Vav-iCre Cdk19null) or iVavCre control 19 samples shares differentially, expressed
- GSE281033 A SOX2-VSX2 axis modulates a core cis-regulatory circuitry to safeguard retinal neural competence 18 samples shares cis-regulatory, neural
- GSE296378 Regulatory Effects of Smoking Cessation on the Cellular Microenvironment and Differentially Expressed Genes in Precancerous Lesions of Pulmonary Nodules in Mice based on Single-Cell RNA-Sequencing and Immune Repertoire-Sequencing 12 samples shares differentially, expressed
- GSE274747 Canonical and non-canonical PRC1 differentially contribute to the regulation of neural stem cell fate 6 samples shares differentially, neural
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.