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Multi-center Korean cohort study based on RNA-sequencing data targeting COPD patients

GSE240656 Homo sapiens (human) RNA-seq (gene expression) 294 samples Submitted 2024/04/17 Platform GPL24676Platform GPL30209
At a glance

RNA-seq (gene expression) · human · 294 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
294 samples: 245 distinct titles, 1 of them repeatedGroups 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
Multi-center Korean cohort study based on RNA-sequencing data targeting COPD patientsScientific data · 2024
Summary (written by the submitters)
To determine gene expression changes that contribute to the development of COPD among smokers using high-throughput RNA sequencing in the peripheral blood of COPD patients. Total RNA sequencing was investigated by prospectively collecting lung function test results and whole blood samples from smokers who visited the outpatient department of respiratory medicine at three hospitals since 2020.
Read the full summary ↓
This study confirmed gene expression differences in non-coding RNA in COPD patients according to the degree of airflow limitation through RNA-Seq transcriptome analysis. These results may provide valuable predictive resources for future COPD research.
Published in
Multi-center Korean cohort study based on RNA-sequencing data targeting COPD patients
Seo M, Park S, Kim W et al. · Scientific data 2024 · PMID 38844491 · doi:10.1038/s41597-024-03389-8
This dataset
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.

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 GSE240656_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1004581 and SRA study SRP454671. Searching any of these in the dataset finder brings you back here.

Study design
245 distinct titles, 1 of them repeated
CNA ×50

How we read this: Read from 294 sample titles: 245 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

+ 254 more — browse all 294 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.