Tracing functional (epi)genomic imprints and their evolutionary origins in human defense antiviral cellular response (RNA-Seq 2)
RNA-seq (gene expression) · human · 10 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
- 10 samples: 10 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 GSE253111_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 PRJNA1064057 and SRA study SRP483449. 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 10 sample titles: 10 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.
- GSM8014245 RNA-seq_MRC5_SVI_0h_exp1_2023
- GSM8014246 RNA-seq_MRC5_SVI_3h_exp1_2023
- GSM8014247 RNA-seq_MRC5_SVI_6h_exp1_2023
- GSM8014248 RNA-seq_MRC5_SVI_0h_exp2_2023
- GSM8014249 RNA-seq_MRC5_SVI_3h_exp2_2023
- GSM8014250 RNA-seq_MRC5_SVI_6h_exp2_2023
- GSM8014251 RNA-seq_NIH3T3_SVI_0h_exp1_2023
- GSM8014252 RNA-seq_NIH3T3_SVI_7h_exp1_2023
- GSM8014253 RNA-seq_NIH3T3_SVI_0h_exp2_2023
- GSM8014254 RNA-seq_NIH3T3_SVI_7h_exp2_2023
- GSE229439 Tracing functional (epi)genomic imprints and their evolutionary origins in human defense antiviral cellular response (ChIP-Seq) 85 samples shares evolutionary, antiviral, imprints, cellular, tracing, origins, defense
- 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, 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, rna-seq
- GSE207924 Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the transcript life cycle [RNA-seq] 160 samples shares cellular, rna-seq
- GSE204762 Multi-modal skin atlas associates a multicellular immune-stromal community with altered cornification and T cell expansion in atopic dermatitis [scRNA-seq] 43 samples shares cellular, rna-seq
- GSE324958 A cellular cross-species RNA-seq atlas captures the transcriptional dynamics of myogenesis 24 samples shares cellular, rna-seq
- GSE266919 Distinct Cellular Mechanisms Underlie Chemotherapies and Their Combinations with PD-L1 Checkpoint Inhibitor in Triple-Negative Breast Cancer [TNBC] 117 samples shares cellular
- GSE279626 PGLYRP-1 mediated intracellular peptidoglycan detection promotes mucosal protection 82 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.