Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Urine metagenome

ABSTRACT: source-separated urine Metagenome

PROVIDER: PRJNA399057 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR7716742_1.fastq.gz	Fastqsanger.gz
	SRR7716742_2.fastq.gz	Fastqsanger.gz
	SRR7716743_1.fastq.gz	Fastqsanger.gz
	SRR7716743_2.fastq.gz	Fastqsanger.gz
	SRR7716744_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 8

Similar Datasets

urine metagenome

Project description:urine metagenome Metagenome

| PRJNA1032262 | ENA

urine metagenome

Project description:urine metagenome Genome sequencing and assembly

| PRJNA813293 | ENA

Soil metagenome from avocado orchards in Channybearup, Western Australia in 2024

Project description:Metagenome data from soil samples were collected at 0 to 10cm deep from 2 avocado orchards in Channybearup, Western Australia, in 2024. Amplicon sequence variant (ASV) tables were constructed based on the DADA2 pipeline with default parameters.

2025-06-15 | GSE298403 | GEO

Cell and microvesicle urine microRNA deep sequencing profiles from healthy individuals

Project description:Background: Urine is a potential source of biomarkers for diseases of the kidneys and urinary tract. RNA, including microRNA, is present in the urine enclosed in detached cells or in extracellular vesicles (EVs) or bound and protected by extracellular proteins. Detection of cell- and disease-specific microRNA in urine may aid early diagnosis of organ-specific pathology. In this study, we applied barcoded deep sequencing to profile microRNAs in urine of healthy volunteers, and characterized the effects of sex, urine fraction (cells vs. EVs) and repeated voids by the same individuals. Results: Compared to urine-cell-derived small RNA libraries, urine-EV-derived libraries were relatively enriched with miRNA, and accordingly had lesser content of other small RNA such as rRNA, tRNA and sn/snoRNA. Unsupervised clustering of specimens in relation to miRNA expression levels showed prominent bundling by specimen type (urine cells or EVs) and by sex, as well as a tendency of repeated (first and second void) samples to neighbor closely. Likewise, miRNA profile correlations between void repeats, as well as fraction counterparts (cells and EVs from the same specimen) were distinctly higher than correlations between miRNA profiles overall. Differential miRNA expression by sex was similar in cells and EVs. Conclusions: miRNA profiling of both urine EVs and sediment cells can convey biologically important differences between individuals. However, to be useful as urine biomarkers, careful consideration is needed for biofluid fractionation and sex-specific analysis, while the time of voiding appears to be less important.

2016-01-28 | GSE72183 | GEO

urine metagenome

Project description:Viral metagenomics of human urine

| PRJNA603336 | ENA

urine metagenome

Project description:16S rRNA gene targeted metagenome sequencing of urine microbiome in kidney stone disease

| PRJNA398193 | ENA

Project description:Microbial community in a membrane bioreactor treating source-separated urine

| PRJNA1067394 | ENA

urine metagenome

Project description:Urine nitrification with a synthetic microbial community

| PRJNA486464 | ENA

Fragile X urine microRNA profiling

Project description:MicroRNA (miRNA) biomarkers for fragile X syndrome were searched by urine microRNA (miRNA) profiling using deep sequencing. The urine miRNA profile of twin boys who shared the same environment but one had a FXS full mutation and the other carried a premutation allele was compared based on the similar sequence reads. The urine of twin boys showed 28 differentiatially regulated miRNAs when 219 reliable identified miRNAs were compared.

2020-03-30 | GSE143347 | GEO

Urine

Project description:Urine Raw sequence reads

| PRJNA850668 | ENA