Time-Resolved Multi-Omics Identify Biomarkers of Immediate Reactions to mRNA Vaccination [RNA-Seq]
RNA-seq (gene expression) · human · 187 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
- 187 samples: 187 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
- Time-Resolved Multi-Omics Identify Biomarkers of Immediate Reactions to mRNA VaccinationAllergy · 2026
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 GSE325054_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 PRJNA1438474 and SRA study SRP684379. 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 187 sample titles: 187 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.
- GSM9594885 Whole Blood, ID1, D1, Vaccine, Baseline
- GSM9594886 Whole Blood, ID1, D1, Vaccine, 2hour
- GSM9594887 Whole Blood, ID1, D1, Vaccine, 6hour
- GSM9594888 Whole Blood, ID1, D1/D2, Vaccine/Placebo, 24hour/Baseline
- GSM9594889 Whole Blood, ID1, D2, Placebo, 35min
- GSM9594890 Whole Blood, ID1, D2, Placebo, 2hour
- GSM9594891 Whole Blood, ID1, D2, Placebo, 6hour
- GSM9594892 Whole Blood, ID1, D2, Placebo, 24hour
- GSM9594893 Whole Blood, ID1, M5, Booster, Baseline
- GSM9594894 Whole Blood, ID1, M5, Booster, 35min
- GSM9594895 Whole Blood, ID1, M5, Booster, 2hour
- GSM9594896 Whole Blood, ID2, D1, Placebo, Baseline
- GSM9594897 Whole Blood, ID2, D1, Placebo, 35min
- GSM9594898 Whole Blood, ID2, D1, Placebo, 2hour
- GSM9594899 Whole Blood, ID2, D1, Placebo, 6hour
- GSM9594900 Whole Blood, ID2, D1/D2, Placebo/Vaccine, 24hour/Baseline
- GSM9594901 Whole Blood, ID2, D2, Vaccine, 2hour
- GSM9594902 Whole Blood, ID2, D2, Vaccine, 6hour
- GSM9594903 Whole Blood, ID2, D2, Vaccine, 24hour
- GSM9594904 Whole Blood, ID2, M5, Booster, Baseline
- GSM9594905 Whole Blood, ID2, M5, Booster, 35min
- GSM9594906 Whole Blood, ID2, M5, Booster, 2hour
- GSM9594907 Whole Blood, ID3, D1, Placebo, Baseline
- GSM9594908 Whole Blood, ID3, D1, Placebo, 2hour
- GSM9594909 Whole Blood, ID3, D1, Placebo, 6hour
- GSM9594910 Whole Blood, ID3, D1/D2, Placebo/Vaccine, 24hour/Baseline
- GSM9594911 Whole Blood, ID3, D2, Vaccine, 35min
- GSM9594912 Whole Blood, ID3, D2, Vaccine, 2hour
- GSM9594913 Whole Blood, ID3, D2, Vaccine, 6hour
- GSM9594914 Whole Blood, ID3, D2, Vaccine, 24hour
- GSM9594915 Whole Blood, ID3, M5, Booster, Baseline
- GSM9594916 Whole Blood, ID3, M5, Booster, 35min
- GSM9594917 Whole Blood, ID3, M5, Booster, 2hour
- GSM9594918 Whole Blood, ID4, D1, Vaccine, Baseline
- GSM9594919 Whole Blood, ID4, D1, Vaccine, 35min
- GSM9594920 Whole Blood, ID4, D1, Vaccine, 2hour
- GSM9594921 Whole Blood, ID4, D1, Vaccine, 6hour
- GSM9594922 Whole Blood, ID4, D1/D2, Vaccine/Placebo, 24hour/Baseline
- GSM9594923 Whole Blood, ID4, D2, Placebo, 35min
- GSM9594924 Whole Blood, ID4, D2, Placebo, 2hour
+ 147 more — browse all 187 samples with per-sample file links →
- GSE263083 Multi-omics data analysis for the JGOG3025-TR2 ovarian cancer cohort [RNA-seq] 291 samples shares multi-omics, rna-seq
- GSE251778 Sex-specific and multiomic integration enhance accuracy of peripheral blood biomarkers of major depressive disorder [RNA-Seq] 169 samples shares biomarkers, rna-seq
- GSE287540 Transcriptomic and proteomic characterization of cell and protein biomarkers of checkpoint inhibitor-induced liver injury [bulk RNA-seq] 128 samples shares biomarkers, rna-seq
- GSE287541 Transcriptomic and proteomic characterization of cell and protein biomarkers of checkpoint inhibitor-induced liver injury [scRNA-seq] 92 samples shares biomarkers, rna-seq
- GSE288966 Multi-omics analysis identifies progenitors of CD4-CTLs [bulk RNA-seq] 70 samples shares multi-omics, rna-seq
- GSE308020 Integrative multi-omics deciphers the potential mechanism and microbial biomarkers for lymph node metastasis in colorectal cancer 66 samples shares multi-omics, biomarkers
- GSE268776 Multi-omics approaches reveal that Diffuse Midline Gliomas are susceptible to replication stress therapy and cholesterol biosynthesis inhibition [RNA-seq] 64 samples shares multi-omics, rna-seq
- GSE273848 Definition of a multi-omics signature for Esophageal Adenocarcinoma prognosis prediction [totalRNA-seq] 40 samples shares multi-omics, 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.