Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Soil metagenome

ABSTRACT: Amplicon-seq data from cultivation field soil samples

PROVIDER: PRJDB11935 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	DRR308535_1.fastq.gz	Fastqsanger.gz
	DRR308535_2.fastq.gz	Fastqsanger.gz
	DRR308536_1.fastq.gz	Fastqsanger.gz
	DRR308536_2.fastq.gz	Fastqsanger.gz
	DRR308537_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 576

Similar Datasets

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

16s Amplicon sequencing of soil samples from sorghum field

Project description:Amplicon sequencing of soil samples from sorghum field

| PRJEB15133 | 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

iPOTs: Internet of Things-based pot system controlling optional treatment of soil water condition for plant phenotyping under drought stress

Project description:A cultivation facility that can assist users in controlling the soil water condition is needed for accurately phenotyping plants under drought stress in an artificial environment. Here we report the Internet of Things (IoT)-based pot system controlling optional treatment of soil water condition (iPOTs), an automatic irrigation system that mimics the drought condition in a growth chamber. The Wi-Fi-enabled iPOTs system allows water supply from the bottom of the pot, based on the soil water level set by the user, and automatically controls the soil water level at a desired depth. The iPOTs also allows users to monitor environmental parameters, such as soil temperature, air temperature, humidity, and light intensity, in each pot. To verify whether the iPOTs mimics the drought condition, we conducted a drought stress test on rice varieties and near-isogenic lines, with diverse root system architecture, using the iPOTs system installed in a growth chamber. Similar to the results of a previous drought stress field trial, the growth of shallow-rooted rice accessions was severely affected by drought stress compared with that of deep-rooted accessions. The microclimate data obtained using the iPOTs system increased the accuracy of plant growth evaluation. Transcriptome analysis revealed that pot positions in the growth chamber had little impact on plant growth. Together, these results suggest that the iPOTs system represents a reliable platform for phenotyping plants under drought stress.

2021-08-24 | GSE171578 | GEO

soil metagenome

Project description:16S rRNA gene amplicon sequencing, soil samples from buckwheat field under long-term fertilization and crop rotation

| PRJDB15840 | ENA

Transcription profiling of leaves of Populus nigra × P. maximowiczii growing in monoculture and stands mixed with Robinia at poor and rich soil sites

Project description:P. nigra × P. maximowiczii plants were grown in the field, each in monoculture and in stands mixed with Robinia pseudoacacia, at two locations differing in soil nutrient levels. After 3 years of growing under ambient weather conditions, leaf samples were harvested and analyzed by RNA-seq.

2022-02-01 | E-MTAB-9040 | biostudies-arrayexpress

Transcription profiling of leaves of Populus nigra × P. maximowiczii growing in monoculture and stands mixed with Robinia at poor and rich soil sites

Project description:P. nigra × P. maximowiczii plants were grown in the field, each in monoculture and in stands mixed with Robinia pseudoacacia, at two locations differing in soil nutrient levels. After 4 years of growing under ambient weather conditions, leaf samples were harvested and analyzed by RNA-seq.

2022-02-01 | E-MTAB-9039 | biostudies-arrayexpress

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

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

2010-02-04 | E-GEOD-19353 | biostudies-arrayexpress

Transcription profiling of developing xylem of Populus maximowiczii × P. trichocarpa and P. nigra × P. maximowiczii growing pure and mixed with Robinia at poor and rich soil sites

Project description:P. maximowiczii × P. trichocarpa and P. nigra × P. maximowiczii plants were grown in the field, each pure and mixed with Robinia pseudoacacia, at two locations differing in soil nutrient levels. After 3 years of growing, samples of developing xylem were harvested and analyzed by RNA-seq.

2021-04-01 | E-MTAB-9028 | biostudies-arrayexpress

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