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Air particulate multiomics improves functional annotation of SNPs associated with lung disease

GSE327419 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 18 samples Submitted 2026/04/30 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 18 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
18 samples: 14 distinct titles, 4 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.
Summary (written by the submitters)
Asthma and chronic obstructive pulmonary disease (COPD) are influenced by interactions between genetic background and environmental exposures such as air pollutants, yet the mechanisms linking genetic variation and pollutant response remain poorly understood. We used nascent RNA run-on sequencing to characterize transcriptional responses of lung cells exposed to wood smoke particles, urban particulate matter, and Afghan dust particles.
Read the full summary ↓
These datasets provide the first characterization of the nascent transcriptional response to urban particulate matter in primary lung cells and reveal both shared and pollutant-specific regulatory dynamics. Integration with regulatory network inference and genetic association data enables prioritization of noncoding variants and candidate regulatory mechanisms linking environmental exposures to lung disease risk.
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 GSE327419_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1450684 and SRA study SRP690264. Searching any of these in the dataset finder brings you back here.

Study design
14 distinct titles, 4 of them repeated
WSP120_Rep1_D ×2 WSP120_Rep2_D ×2 WSP30_Rep1_D ×2 WSP30_Rep2_D ×2

How we read this: Read from 18 sample titles: 14 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
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