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Molecular landscape of respiratory infection: A large-scale, multi-centre blood transcriptome dataset

GSE282464 Homo sapiens (human) RNA-seq (gene expression) 377 samples Submitted 2025/03/26 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 377 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
377 samples: 377 × Whole blood, COVID positive,Groups 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
Molecular landscape of respiratory infection: A large-scale, multi-centre blood transcriptome datasetScientific data · 2025
Summary (written by the submitters)
Respiratory infections pose significant challenges to global health, impacting millions of individuals annually. Understanding the molecular mechanisms underlying the pathogenicity of these infections is crucial for developing effective interventions. RNA sequencing provides insights into a patient’s global transcriptome changes, facilitating the identification of host gene signatures in response to infection and potential therapeutic targets.
Read the full summary ↓
Here we present an extensive whole blood transcriptome dataset from a demographically diverse cohort of 502 patients with infections including COVID-19, seasonal coronavirus, influenza A or influenza B, sepsis, septic shock, and co-infections (Viral/Viral, Bacterial/Viral, Bacterial/Viral/Fungal, Viral/Fungal, Viral/ Viral/Fungal).
Published in
Molecular landscape of respiratory infection: A large-scale, multi-centre blood transcriptome dataset
Chew T, Pelaia TM, Phu AL et al. · Scientific data 2025 · PMID 40640204 · doi:10.1038/s41597-025-05488-6
This dataset
Open GSE282464 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE282464_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA901461 and SRA study SRP407875. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40640204) ↗
Study design
377 × Whole blood, COVID positive,

Supports a between-group comparison across 377 samples.

How we read this: 1 replicated groups read from 377 sample titles; they account for 377 of them. 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.

Samples in this study

+ 337 more — browse all 377 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.