Tracing functional (epi)genomic imprints and their evolutionary origins in human defense antiviral cellular response (ChIP-Seq)
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 85 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
- Homo sapiens (human)
- Samples
- 85 samples: 85 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.
- Paper
- An evolutionarily conserved constellation of functional cis-elements programs the virus-responsive fate of the human (epi)genomeNucleic acids research · 2025
Read the full summary ↓
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 GSE229439_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 PRJNA954441 and SRA study SRP432078. 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 85 sample titles: 85 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.
- 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
- GSM7163595 MED1-ChIP-seq_NM_SVI_3h_2023
- GSM7163596 MED1-ChIP-seq_NM_SVI_6h_2023
- GSM7163597 RNA_polII-ChIP-seq_HL_SVI_0h_2023
- GSM7163598 RNA_polII-ChIP-seq_HL_SVI_3h_2023
- GSM7163599 RNA_polII-ChIP-seq_HL_SVI_6h_2023
- GSM7163600 RNA_polII-ChIP-seq_NM_SVI_0h_2023
- GSM7163601 RNA_polII-ChIP-seq_NM_SVI_3h_2023
- GSM7163602 RNA_polII-ChIP-seq_NM_SVI_6h_2023
- GSM7163603 H3K4me3-ChIP-seq_HL_SVI_0h_exp1_2023
- GSM7163604 H3K4me3-ChIP-seq_HL_SVI_3h_exp1_2023
- GSM7163605 H3K4me3-ChIP-seq_HL_SVI_6h_exp1_2023
- GSM7163606 H3K4me3-ChIP-seq_HL_SVI_0h_exp2_2023
+ 45 more — browse all 85 samples with per-sample file links →
- GSE288595 Tracing functional (epi)genomic imprints and their evolutionary origins in human defense antiviral cellular response (ChIP-seq III) 14 samples shares evolutionary, antiviral, imprints, cellular, chip-seq, tracing, origins, defense
- GSE253111 Tracing functional (epi)genomic imprints and their evolutionary origins in human defense antiviral cellular response (RNA-Seq 2) 10 samples shares evolutionary, antiviral, imprints, cellular, tracing, origins, defense
- GSE150672 Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology 438 samples shares cellular
- GSE207924 Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the transcript life cycle [RNA-seq] 160 samples shares cellular
- GSE266919 Distinct Cellular Mechanisms Underlie Chemotherapies and Their Combinations with PD-L1 Checkpoint Inhibitor in Triple-Negative Breast Cancer [TNBC] 117 samples shares cellular
- GSE65364 Single Cell triple omics sequencing reveals genetic and epigenetic heterogeneity in hepatocellular carcinoma 95 samples shares cellular
- GSE279626 PGLYRP-1 mediated intracellular peptidoglycan detection promotes mucosal protection 82 samples shares cellular
- GSE293066 MAVS Orchestrates a Potent IFN-independent Antiviral Immunity by 2 Preserving Mitochondrial Integrity 54 samples shares antiviral
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.