Tracing functional (epi)genomic imprints and their evolutionary origins in human defense antiviral cellular response
ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 156 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
- 156 samples: 154 distinct titles, 2 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
- An evolutionarily conserved constellation of functional cis-elements programs the virus-responsive fate of the human (epi)genomeNucleic acids research · 2025
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 GSE229445_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 PRJNA954437 and SRA study SRP432073. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 156 sample titles: 154 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.
- GSM7163555 RNA-seq_HL_SVI_0h_exp1_2023
- GSM7163556 RNA-seq_HL_SVI_3h_exp1_2023
- GSM7163557 RNA-seq_HL_SVI_6h_exp1_2023
- GSM7163558 RNA-seq_HL_SVI_0h_exp2_2023
- GSM7163559 RNA-seq_HL_SVI_3h_exp2_2023
- GSM7163560 RNA-seq_HL_SVI_6h_exp2_2023
- GSM7163561 RNA-seq_NM_SVI_0h_exp1_2023
- GSM7163562 RNA-seq_NM_SVI_3h_exp1_2023
- GSM7163563 RNA-seq_NM_SVI_6h_exp1_2023
- GSM7163564 RNA-seq_NM_SVI_0h_exp2_2023
- GSM7163565 RNA-seq_NM_SVI_3h_exp2_2023
- GSM7163566 RNA-seq_NM_SVI_6h_exp2_2023
- GSM7163567 IRF3-ChIP-seq_HL_SVI_0h_exp1_2023
- GSM7163568 IRF3-ChIP-seq_HL_SVI_3h_exp1_2023
- GSM7163569 IRF3-ChIP-seq_HL_SVI_6h_exp1_2023
- GSM7163570 IRF3-ChIP-seq_HL_SVI_0h_exp2_2023
- GSM7163571 IRF3-ChIP-seq_HL_SVI_3h_exp2_2023
- GSM7163572 IRF3-ChIP-seq_HL_SVI_6h_exp2_2023
- GSM7163573 IRF3-ChIP-seq_NM_SVI_0h_2023
- GSM7163574 IRF3-ChIP-seq_NM_SVI_3h_2023
- GSM7163575 IRF3-ChIP-seq_NM_SVI_6h_2023
- GSM7163576 p65-ChIP-seq_HL_SVI_0h_2023
- GSM7163577 p65-ChIP-seq_HL_SVI_3h_2023
- GSM7163578 p65-ChIP-seq_HL_SVI_6h_2023
- GSM7163579 p65-ChIP-seq_NM_SVI_0h_2023
- GSM7163580 p65-ChIP-seq_NM_SVI_3h_2023
- GSM7163581 p65-ChIP-seq_NM_SVI_6h_2023
- GSM7163582 CBP-ChIP-seq_HL_SVI_0h_2023
- GSM7163583 CBP-ChIP-seq_HL_SVI_3h_2023
- GSM7163584 CBP-ChIP-seq_HL_SVI_6h_2023
- GSM7163585 CBP-ChIP-seq_NM_SVI_0h_2023
- GSM7163586 CBP-ChIP-seq_NM_SVI_3h_2023
- GSM7163587 CBP-ChIP-seq_NM_SVI_6h_2023
- GSM7163588 MED1-ChIP-seq_HL_SVI_0h_exp1_2023
- GSM7163589 MED1-ChIP-seq_HL_SVI_3h_exp1_2023
- GSM7163590 MED1-ChIP-seq_HL_SVI_6h_exp1_2023
- GSM7163591 MED1-ChIP-seq_HL_SVI_0h_exp2_2023
- GSM7163592 MED1-ChIP-seq_HL_SVI_3h_exp2_2023
- GSM7163593 MED1-ChIP-seq_HL_SVI_6h_exp2_2023
- GSM7163594 MED1-ChIP-seq_NM_SVI_0h_2023
+ 116 more — browse all 156 samples with per-sample file links →
- GSE317069 Multiscale analysis and functional validation of the cellular and genetic determinants of skeletal disease 47 samples shares cellular
- GSE246223 A Mouse Model for Metabolic Dysfunction-associated Steatotic Liver Disease and Hepatocellular Carcinoma 160 samples shares cellular
- GSE253600 ROR2 regulates cellular plasticity in pancreatic neoplasia and adenocarcinoma 48 samples shares cellular
- GSE282171 Depression and Anxiety-Induced Metabolic Reprogramming Promotes Hepatocellular Carcinoma Progression through CBX5 Lactylation: Involvement of Taurocholate and PHGDH-Mediated Ferroptosis 28 samples shares cellular
- GSE156337 Onco-fetal reprogramming of endothelial cells drives immunosuppressive macrophages in Hepatocellular Carcinoma 186 samples shares cellular
- GSE207926 Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the transcript life cycle 160 samples shares cellular
- GSE246221 A Mouse Model for Metabolic Dysfunction-associated Steatotic Liver Disease and Hepatocellular Carcinoma [RNA-seq] 148 samples shares cellular
- GSE272629 Passive shaping of intra- and intercellular m6A dynamics via mRNA metabolism 128 samples shares cellular
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.