Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Mycobacterium shigaense

ABSTRACT: Mycobacterium shigaense Detection in a Patient by Metagenomic Next-Generation Sequencing (mNGS)

PROVIDER: PRJNA1225627 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR32453448.fastq.gz	Fastqsanger.gz

Items per page:

1 - 1 of 1

Similar Datasets

Mycobacterium shigaense

Project description:Mycobacterium shigaense overview

| PRJNA492620 | ENA

Mycobacterium shigaense strain:Strain

Project description:Whole genome sequence of Mycobacterium shigaense

| PRJDB9217 | ENA

Mycobacterium shigaense isolate:cutaneous

Project description:Mycobacterium shigaense isolate:cutaneous Genome sequencing

| PRJNA398420 | ENA

Mycobacterium shigaense strain:UN-152

Project description:Complete Genome Sequence of Mycobacterium shigaense.

| PRJDB5818 | ENA

Mycobacterium shigaense strain:UN-152

Project description:Mycobacterium shigaense strain:UN-152 Genome sequencing and assembly

| PRJNA376862 | 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

mNGS of Pulmonary Fluid in Immunocompromised Children

Project description:<p>Despite improved diagnostics, pulmonary pathogens in immunocompromised children frequently evade detection, leading to significant mortality. In this study, we performed RNA and DNA-based metagenomic next generation sequencing (mNGS) on 41 lower respiratory samples collected from 34 children. We identified a rich cross-domain pulmonary microbiome containing bacteria, fungi, RNA viruses, and DNA viruses in each patient. Potentially pathogenic bacteria were ubiquitous among samples but could be distinguished as possible causes of disease by parsing for outlier organisms. Potential pathogens were detected in half of samples previously negative by clinical diagnostics. Ongoing investigation is needed to determine the pathogenic significance of outlier microbes in the lungs of immunocompromised children with pulmonary disease. Metatranscriptomic (RNA) sequencing libraries are reported in the manuscript and are included for this release.</p>

| phs001684 | dbGaP

Project description:Detection of Mycobacterium tuberculosis complex by metagenomic next-generation sequencing

| PRJNA1073041 | ENA

Project description:Metagenomic Next-Generation Sequencing (mNGS) of Clinical Samples

| PRJNA588503 | ENA

Project description:metagenomic next-generation sequencing (mNGS) of female urine

| PRJNA866558 | ENA