Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Proficiency testing of metagenomics

ABSTRACT: A proficiency testing of metagenomics as a diagnostic tool for the detection of RNA viruses in swine fecal material

PROVIDER: PRJEB44508 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other
	ERR6118690_1.fastq.gz	Fastqsanger.gz
	ERR6118690_2.fastq.gz	Fastqsanger.gz
	ERR6118700_1.fastq.gz	Fastqsanger.gz
	ERR6118700_2.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 36

Similar Datasets

diagnostic metagenomics proficiency test dataset

Project description:Proficiency testing of metagenomics-based detection of food-borne pathogens using a complex artificial sequencing dataset

| PRJEB37463 | ENA

Transcription profiling of rat mixed-tissue RNA samples in different laboratories to monitor proficiency

Project description:A pilot program for monitoring proficiency in microarray facilities using three replicates of two different RNA sources. Participating laboratories prepared and hybridized targets from the six RNA samples using their own protocols. The entire process was repeated three times over a nine-month period and included approximately fifteen distinct laboratories in each testing round.

2008-09-12 | E-MEXP-1568 | biostudies-arrayexpress

Mre11 suppresses replication stress and gene amplification proficiency

Project description:Gene amplification, copy-number increases of particular genes and surrounding genomic segments, promotes cancer progression and acquired therapy resistance. Thus, understanding genetic traits that confer gene amplification proficiency is important. The primary step for gene amplification is spontaneous DNA rearrangements initiated by DNA breaks. Here we show that mammalian cells became gene amplification-proficient when we knocked down Mre11/Rad50/Nbs1 (MRN) complex, a multifunctional complex that guards the genome from DNA breaks. Cells with reduced Mre11 experienced severe replication stress, with marked increases of single-stranded breaks followed by double-stranded breaks during DNA replication. Such breaks underlay for the increase in spontaneous gene amplification. Other traits associated with replication stress, such as impaired intra-S phase checkpoint and global transcriptional changes in DNA metabolism genes also contributed to gene amplification proficiency. Our results define Mre11 deficiency as a cause of replication stress and gene amplification proficiency and provide a candidate marker for aggressive cancer phenotypes.

2015-03-05 | GSE59487 | GEO

GMI Proficiency Test

Project description:Global Microbial Identifier Proficiency Test

| PRJEB21132 | ENA

Mre11 suppresses replication stress and gene amplification proficiency

2015-03-05 | E-GEOD-59487 | biostudies-arrayexpress

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

Testing

Project description:Testing

| PRJEB34844 | ENA

Testing

Project description:Testing

| PRJEB12736 | ENA

microRNA expression profile of NPTr cells infected with swine H1N1/2009 influenza A virus

Project description:In recent years, the roles of microRNAs playing in the regulation of influenza viruses replication caused researchers' much attenion. However, much work focused on the interactions between human, mice or chicken microRNAs with human or avian influenza viruses rather than the interactions of swine microRNAs and swine influenza viruses. To investigate the roles of swine microRNAs playing in the regulation of swine influenza A virus replication, the microRNA microarray was performed to identify which swine microRNAs were involved in swine H1N1/2009 influenza A virus infection.

2017-03-24 | GSE96953 | GEO

Project description:GenomeTrakr Project: 2015 Proficiency Testing

| PRJNA386310 | ENA