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Whole blood transcriptome in long-COVID patients reveals association with lung function and immune response

GSE267625 Homo sapiens (human) RNA-seq (gene expression) 111 samples Submitted 2024/05/22 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 111 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
111 samples: 111 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
Whole blood transcriptome in long-COVID patients reveals association with lung function and immune responseThe Journal of allergy and clinical immunology · 2024
Summary (written by the submitters)
It is estimated that 10% of COVID survivor still experience from complaints months after infection. Here we collected blood from 95 patients in the P4O2 COVID-19 study and performed RNA-seq at 3-6 months and 12-15 after infection. Our aim was to discover biomarkers related to long COVID and perform unsupervised clustering to potentially reveal pathological underlying mechanism for long COVID
Published in
Whole blood transcriptome in long-COVID patients reveals association with lung function and immune response
Blankestijn JM, Baalbaki N, Bazdar S et al. · The Journal of allergy and clinical immunology 2024 · PMID 38830512 · doi:10.1016/j.jaci.2024.04.032
This dataset
Open GSE267625 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 GSE267625_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 PRJNA1112027 and SRA study SRP508030. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38830512) ↗
Study design
111 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 111 sample titles: 111 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.

Samples in this study

+ 71 more — browse all 111 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.