Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ABSTRACT: Rice Microbiomes

PROVIDER: PRJNA606404 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR11080700_1.fastq.gz	Fastqsanger.gz
	SRR11080700_2.fastq.gz	Fastqsanger.gz
	SRR11080701_1.fastq.gz	Fastqsanger.gz
	SRR11080701_2.fastq.gz	Fastqsanger.gz
	SRR11080702_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 120

Similar Datasets

Project description:Rice Microbiomes

| PRJNA631648 | ENA

Project description:wild rice microbiomes

| PRJNA932779 | ENA

root metagenome

Project description:Rice Root Associated Microbiomes

| PRJNA255789 | ENA

Project description:Hybrid rice-associated fungal microbiomes

| PRJNA809031 | ENA

Project description:Hybrid rice-associated bacterial microbiomes

| PRJNA808849 | ENA

Patient genetics is linked to chronic wound microbiome composition and healing

Project description:The clinical importance of microbiomes to the chronicity of wounds is widely appreciated, yet little is understood about patient-specific processes shaping wound microbiome composition. Here, a two-cohort microbiome-genome wide association study is presented through which patient genomic loci associated with chronic wound microbiome diversity were identified. Further investigation revealed that alternative TLN2 and ZNF521 genotypes explained significant inter-patient variation in relative abundance of two key pathogens, Pseudomonas aeruginosa and Staphylococcus epidermidis. Wound diversity was lowest in Pseudomonas aeruginosa infected wounds, and decreasing wound diversity had a significant negative linear relationship with healing rate. In addition to microbiome characteristics, age, diabetic status, and genetic ancestry all significantly influenced healing. Using structural equation modeling to identify common variance among SNPs, six loci were sufficient to explain 53% of variation in wound microbiome diversity, which was a 10% increase over traditional multiple regression. Focusing on TLN2, genotype at rs8031916 explained expression differences of alternative transcripts that differ in inclusion of important focal adhesion binding domains. Such differences are hypothesized to relate to wound microbiomes and healing through effects on bacterial exploitation of focal adhesions and/or cellular migration. Related, other associated loci were functionally enriched, often with roles in cytoskeletal dynamics. This study, being the first to identify patient genetic determinants for wound microbiomes and healing, implicates genetic variation determining cellular adhesion phenotypes as important drivers of infection type. The identification of predictive biomarkers for chronic wound microbiomes may serve as risk factors and guide treatment by informing patient-specific tendencies of infection.

2020-04-25 | GSE149314 | GEO

HuMiChip analysis of 86 samples from different body sites

Project description:HuMiChip was used to analyze human oral and gut microbiomes, showing significantly different functional gene profiles between oral and gut microbiome. The results were used to demonstarte the usefulness of applying HuMiChip to human microbiome studies.

2014-02-04 | E-GEOD-54290 | biostudies-arrayexpress

HuMiChip analysis of 86 samples from different body sites

Project description:HuMiChip was used to analyze human oral and gut microbiomes, showing significantly different functional gene profiles between oral and gut microbiome.

2014-02-04 | GSE54290 | GEO

Project description:Microbiomes associated with rice false smut (Ustilaginoidea virens)

| PRJNA1148231 | ENA

Project description:Effect of drought on the rice root microbiomes

| PRJNA386367 | ENA