Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ABSTRACT: Fresh insights into Mediterranean biodiversity: Environmental DNA reveals spatio-temporal patterns of stream invertebrate communities on Sicily

PROVIDER: PRJEB45583 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other
	ERR6040300.fastq.gz	Fastqsanger.gz
	ERR6040320.fastq.gz	Fastqsanger.gz
	ERR6040350.fastq.gz	Fastqsanger.gz
	ERR6040321.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 29

Similar Datasets

Project description:Ebbs and Flows of Marine Biodiversity: Navigating Spatio-Temporal Patterns of Environmental DNA in a Coastal Tidal Ecosystem

| PRJNA1090157 | ENA

Project description:Stream biodiversity metacommunities

| PRJNA599353 | ENA

Project description:Cambridge Bay Soil Invertebrate Biodiversity

| PRJNA817555 | ENA

Spatial-proteomics reveals phospho-signaling dynamics at subcellular resolution

Project description:Dynamic change in subcellular localization of signaling proteins is a general concept that eukaryotic cells evolved for responding and elicit a coordinated response to stimuli. Mass spectrometry (MS)-based proteomics in combination with subcellular fractionation can provide comprehensive maps of spatio-temporal regulation of cells but this is highly challenging involving laborious workflows that do not cover the phosphoproteome level. Here we present a high-throughput workflow based on sequential cell fractionation to profile the global (phospho)proteome dynamics across six distinct subcellular fractions. We benchmarked the workflow by studying spatio-temporal EGFR phospho-signaling dynamics in-vitro in HeLa cells and in-vivo in mouse tissues. Finally, we investigated the spatio-temporal stress signaling, revealing cellular relocation of ribosomal proteins in response to hypertonicity and muscle contraction.

2021-11-18 | PXD023690 | Pride

Malinzi2018 - tumour-immune interaction model

Project description:The paper describes a spatio-temporal mathematical model, in the form of a moving boundary problem, to explain cancer dormancy is developed. Created by COPASI 4.24 (Build 197) Abstract: A spatio-temporal mathematical model, in the form of a moving boundary problem, to explain cancer dormancy is developed. Analysis of the model is carried out for both temporal and spatio-temporal cases. Stability analysis and numerical simulations of the temporal model replicate experimental observations of immune-induced tumour dormancy. Travelling wave solutions of the spatio-temporal model are determined using the hyperbolic tangent method and minimum wave speeds of invasion are calculated. Travelling wave analysis depicts that cell invasion dynamics are mainly driven by their motion and growth rates. A stability analysis of the spatio-temporal model shows a possibility of dynamical stabilization of the tumour-free steady state. Simulation results reveal that the tumour swells to a dormant level.

2024-09-02 | BIOMD0000000809 | BioModels

Spa sulfurous water metagenomes: spatio-temporal study

Project description:Spa sulfurous water metagenomes: spatio-temporal study

| PRJEB35095 | ENA

Higher spatial than temporal variation in soil protist beta-diversity along elevation gradients

Project description:Soil protists spatio-temporal turnover in mountains

| PRJEB59986 | ENA

marine metagenome

Project description:Magadalen Islands Spatio Temporal (MIST) Large Fraction

| PRJNA289000 | ENA

Homo sapiens

Project description:Spatio-temporal transcriptome of the human brain

| PRJNA134455 | ENA

Kolbe2019 - Spatio-temporal liver zonation

Project description:Metabolism of the adult liver in mice and human is spatially zonated with hepatocytes along the axis from each portal vein to the nearest central vein expressing enzymes in a zonated manner. Upstream of that functional zonation, interactions of Wnt and Hh signaling are orchestrating and self-organizing the spatial expression patterns. This spatio-temporal model is derived from experimental data and explores the interacting signaling pathways. To run this model, use the free, open-source software Morpheus (download for Windows, macOS, Linux from https://morpheus.gitlab.io).

2021-09-30 | MODEL2009110001 | BioModels

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