Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ABSTRACT: Detection of pathogen with mNGS

PROVIDER: PRJNA842783 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR19425100_1.fastq.gz	Fastqsanger.gz
	SRR19425100_2.fastq.gz	Fastqsanger.gz
	SRR19425101_1.fastq.gz	Fastqsanger.gz
	SRR19425101_2.fastq.gz	Fastqsanger.gz
	SRR19425102_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 285

Similar Datasets

Project description:Detection of DNA and RNA pathogen in Cerebrosipinal Fluid with mNGS

| PRJNA752873 | ENA

Project description:Pathogen detected by mNGS

| PRJEB32856 | ENA

Project description:Validation of mNGS for virus detection

| PRJNA1375470 | ENA

Project description:Pathogen detection

| PRJNA944830 | ENA

Harnessing Bronchoalveolar Lavage Metagenomics for Distinguishing Lung Cancer from Pulmonary Infectious Diseases

Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.

2024-01-08 | GSE252118 | GEO

Pathogen detection by metagenomic next generation sequencing during neutropenic fever in patients with hematological malignancies.

Project description:Pathogen ID by mNGS during neutropenic fever

| PRJEB54778 | ENA

human metagenome

Project description:Bone joint mNGS for detection Brucell spa (PRJCA046935)

| PRJDB39439 | ENA

Project description:Pathogen Detection Assembly Project

| PRJNA514245 | ENA

Project description:Pathogen Detection Assembly Project

| PRJNA1321378 | ENA

Optimization and Clinical Validation of a Pathogen Detection Microarray

Project description:This series includes microarrays from 36 patient samples and 2 cell-culture controls, used to optimize and validate the pathogen detection microarray (Wong, et. al. 2007) Keywords: viral pathogen detection

2007-05-29 | GSE7779 | 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