Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Soil metagenome

ABSTRACT: Soil samples under different storage conditions Genome sequencing

PROVIDER: PRJNA258114 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR1555380.fastq.gz	Fastqsanger.gz
	SRR1555390.fastq.gz	Fastqsanger.gz
	SRR1555400.fastq.gz	Fastqsanger.gz
	SRR1555381.fastq.gz	Fastqsanger.gz
	SRR1555391.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 36

Similar Datasets

Strategies for the interpretation of soil metaproteomic data

Project description:Evaluation of different strategies to interpret metaproteomics data acquired on soil samples from a floodplain along the Seine River (France) incorporating sample-specific metagenomics data, soil genome catalogue database, and generic sequence database.

2022-02-17 | PXD026798 | Pride

Microbial regulation of soil water repellency to control soil degradation

Project description:Soil water repellency (SWR) (i.e. soil hydrophobicity or decreased soil wettability) is a major cause of global soil degradation and a key agricultural concern. This metabolomics data will support the larger effort measuring soil water repellency and soil aggregate formation caused by microbial community composition through a combination of the standard drop penetration test, transmission electron microscopy characterization and physico-chemical analyses of soil aggregates at 6 timepoints. Model soils created from clay/sand mixtures as described in Kallenbach et al. (2016, Nature Communications) with sterile, ground pine litter as a carbon/nitrogen source were inoculated with 15 different microbial communities known to have significantly different compositions based on 16S rRNA sequencing. This data will allow assessment of the direct influence of microbial community composition on soil water repellency and soil aggregate stability, which are main causes of soil degradation. The work (proposal:https://doi.org/10.46936/10.25585/60001346) conducted by the U.S. Department of Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231.

2024-02-14 | MSV000094090 | MassIVE

soil metagenome

Project description:Effect of sample storage conditions on soil microbial communities

| PRJNA575860 | ENA

soil metagenome

Project description:The soil samples of different fertilizer treatments Genome sequencing

| PRJNA292998 | ENA

soil metagenome

Project description:Metagenome study of rice samples under various conditions

| PRJNA271842 | ENA

Project description:Fungal Diversity of Barley under Different Storage Conditions

| PRJNA833230 | ENA

GNPS-Nitrogen deposition soil samples

Project description:This database was aimed to clarify how untargeted soil metabolomics change under different nitrogen deposition conditions

2019-05-16 | MSV000083789 | GNPS

soil metagenome

Project description:Soil aquifer treatment site soil samples and simulated conditions Metagenome

| PRJNA173916 | ENA

Classifier analysis of gene expression profiles reveals a discriminatory gene set for soil containing different metals

Project description:Environmental pollution is a worldwide problem, and metals are the largest group of contaminants in soil. Microarray toxicogenomic studies with ecologically relevant organisms such as springtails, supplement traditional ecotoxicological research, but are presently rather descriptive. Classifier analysis, a more analytical application of the microarray technique, is able to predict biological classes of unknown samples. We used the uncorrelated shrunken centroid (USC) method to classify gene expression profiles of the springtail Folsomia candida exposed to soil spiked with six different metals (barium, cadmium, cobalt, chromium, lead, and zinc). We identified a gene set (classifier) of 188 genes that can discriminate between six different metals present in soil, which allowed us to predict the correct classes for samples of an independent test set with an accuracy of 83% (error rate = 0.17). This study shows further that in order to apply classifier analysis to actual contaminated field soil samples, more insight and information is needed on the transcriptional responses of soil organisms to different soil types (properties) and mixtures of contaminants.

2010-02-05 | GSE19353 | GEO

Heterobasidion annosum gene expression during saprotrophic growth on topsoil (organic layer) from mineral soil, drained and undrained peatland forests.

Project description:Transcript profiles of H. annosum from different tissues and mycelium grown on different substrates and under different stresses were analyzed. The array probes were designed from gene models taken from the Joint Genome Institute (JGI, department of energy) H. irregulare genome sequence version 1. One aim of this study was to compare gene expression profiles of H. annosum during saprotrophic growth on topsoil from mineral soil, drained and undrained peatland.

2015-10-01 | GSE55290 | GEO

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