Tracing functional (epi)genomic imprints and their evolutionary origins in human defense antiviral cellular response (RNA-Seq)
RNA-seq (gene expression) · human · 12 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 12 samples: 12 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 GSE229438_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA954433 and SRA study SRP432075. 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 12 sample titles: 12 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
- GSE208732 Mapping functional to morphological variation reveals the basis of regional extracellular matrix subversion and nerve invasion in pancreatic cancer [RNA-seq] 102 samples shares cellular, rna-seq
- GSE314526 Differential Cellular Mechanisms Underlie Language and Executive Decline in Amyotrophic Lateral Sclerosis [RNA-seq] 72 samples shares cellular, rna-seq
- GSE202069 RNA-seq data for tumor and adjacent non-tumor tissues from hepatocellular carcinoma patients with anti-PD-1 therapy 66 samples shares cellular, rna-seq
- GSE317207 Transcriptomic profiling of human hepatocellular carcinoma biopsy samples using bulk RNA-seq 33 samples shares cellular, rna-seq
- GSE318970 Metabolic Dependencies in Medulloblastoma Reflect Underlying Cellular Origins 33 samples shares cellular, origins
- GSE289930 Proteogenomic Characterization of Primary Oral Cancer Unveils Extracellular Matrix Remodeling and Immunosuppressive Microenvironment Linked to Lymph Node Metastasis [RNA-seq] 33 samples shares cellular, rna-seq
- GSE317068 Multiscale analysis and functional validation of the cellular and genetic determinants of skeletal disease [Human scRNA-seq] 32 samples shares cellular, rna-seq
- GSE278688 Integrated Single-Cell and Spatial Transcriptomics Uncover Distinct Cellular Subtypes Involved in Neural Invasion in PDAC [scRNA-Seq] 29 samples shares cellular, rna-seq
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.