Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ABSTRACT: Raw data of mNGS sequencing

PROVIDER: PRJNA1240079 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other
	SRR32803100.fastq.gz	Fastqsanger.gz
	SRR32803200.fastq.gz	Fastqsanger.gz
	SRR32803300.fastq.gz	Fastqsanger.gz
	SRR32803400.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 461

Similar Datasets

Project description:Raw data of mNGS sequencing

| PRJNA1243004 | ENA

Project description:Raw data of mNGS sequencing

| PRJNA1232331 | ENA

Project description:Raw data of mNGS sequencing

| PRJNA1227282 | 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

raw

Project description:Drosophila melanogaster raw, raw, is differentially expressed in 23 experiment(s);

| FBgn0003209 | ExpressionAtlas

raw

Project description:Drosophila melanogaster raw, raw, is expressed in 5 baseline experiment(s);

| FBgn0003209 | ExpressionAtlas

E-MTAB-4270

Project description:Transcription profiling by high throughput sequencing of soybean transcriptome data

2019-05-15 | E-MTAB-4270 | ExpressionAtlas

Project description:Metagenomic sequencing data (mNGS)

| PRJNA1043599 | ENA

Project description:Microbial sequencing data of mNGS

| PRJNA1139112 | ENA

Prospective Collection of PillCam SB3 Videos and Raw Data Files for Future Developments (SODA)

Project description:Observational, Multicenter, Post-market, Minimal risk, Prospective data collection of PillCam SB3 videos (including PillCam reports) and raw data files and optional collection of Eneteroscopy reports

| 2291850 | ecrin-mdr-crc

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