Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ABSTRACT: Study of aerobiome on dust particles (size segregated) transported from different air-mass sources in the East Mediterranean

PROVIDER: PRJNA765143 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR16003500_1.fastq.gz	Fastqsanger.gz
	SRR16003500_2.fastq.gz	Fastqsanger.gz
	SRR16003501_1.fastq.gz	Fastqsanger.gz
	SRR16003501_2.fastq.gz	Fastqsanger.gz
	SRR16003502_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 290

Similar Datasets

Transcriptomic responses to diesel exhaust particles or World trade center dust

Project description:C57BL/6 Mouse cortex samples at 2-months of age, both sexes, exposed to one acute 5-hour dose of 100ug/m3 of air pollution from diesel exhaust particles or dust from the World Trade Center collapse. C57BL/6 female mouse cortex exposed to one acute 4-hour dose of 500ug/m3 of woodsmoke.

2025-12-22 | E-MTAB-16199 | biostudies-arrayexpress

Sequencing bacteria associated with Sahara dust in snow on high alpine site

Project description:Bacterial composition and survival on Sahara dust particles transported to the European Alps

| PRJEB9478 | ENA

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

Project description: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. 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.

2026-04-30 | GSE327419 | GEO

UPM Nascent transcription trends improves functional annotations of SNPs

2026-04-30 | GSE326905 | GEO

aerosol metagenome

Project description:Bioaerosols along transported pathway of Asian dust

| PRJNA413598 | ENA

Primary human epithelial cells exposed to coarse, fine and ultrafine particles

Project description:The purpose of this study was to characterize global gene expression in human airway epithelial cells and identify cellular pathways associated with coarse, fine and ultrafine particulate matter (PM) exposures. Ambient PM was collected in 3 different size fractions from Chapel Hill air, particles were extracted from foam or filter matrices and lyophilized. Human primary airway epithelial cells were exposed to particles at 250μg/ml or vehicle control for 6h in culture. Following exposure, RNA was isolated and hybridized to human HG U133A affymetrix chips. Keywords: particle treatment

2008-05-01 | GSE7010 | GEO

dust metagenome

Project description:International Space Station Dust Particles

| PRJNA290092 | ENA

air-conditioning system dust

Project description:air-conditioning system dust Other

| PRJNA488116 | ENA

RNA-SEQ of mouse lung tissue exposed to pollutant particles and house dust mite allergen

Project description:The experiment was designed to evaluate the airway mucosal response to sequential exposure of diesel exhaust particles (DEP) followed by house dust mite allergen (HDM) with an interval of 24hrs.

2026-09-01 | E-MTAB-17021 | biostudies-arrayexpress

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

Project description:Asthma and chronic obstructive pulmonary disease (COPD) are lung diseases strongly influenced by interactions between genetic background and environmental exposures, particularly air pollutants. However, the biological mechanisms by which genetic variation and pollutant exposure impact disease remain poorly understood. Interpretation of genome-wide association studies is limited because most disease-associated variants occur in noncoding regions and are difficult to connect to functional regulatory mechanisms and downstream gene targets. Here, we integrate nascent RNA run-on sequencing with regulatory network inference to identify pollutant-responsive transcriptional regulatory elements and their upstream regulators in lung cells. Using newly generated and published datasets, we analyze multiomics responses to multiple air pollutants, including wood smoke particles, urban particulate matter, and Afghan dust particles. These analyses 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. We then integrate pollutant-responsive regulatory networks with genetic associations for asthma and COPD to prioritize noncoding variants for experimental validation. By linking these variants to candidate transcription factors and target genes, we identify mechanisms through which environmental exposures may interact with noncoding genetic variants to influence disease risk. This framework enables functional interpretation of noncoding variants through environmentally responsive transcriptional networks.

2026-04-13 | GSE327935 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data