Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ABSTRACT: Metatranscriptome of a zooplankton community

PROVIDER: PRJNA505963 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR8210163_1.fastq.gz	Fastqsanger.gz
	SRR8210164_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 2 of 2

Similar Datasets

Metatranscriptomes of phytoplankton blooms from an ocean acidification mesocosm experiment

2008-02-08 | GSE10119 | GEO

Project description:Metatranscriptome of a crustacean zooplankton community

| PRJNA577375 | ENA

Abundant and diverse non-coding small RNAs identified in an extremophilic microbial community using metatranscriptomics

Project description:Regulatory small RNAs (sRNAs) represent a major class of regulatory molecules that play large-scale and essential roles in many cellular processes across all domains of life. Microbial sRNAs have been primarily investigated in a few model organisms and little is known about the dynamics of sRNA synthesis in natural environments, and the roles of these short transcripts at the community level. Analyzing the metatranscriptome of a model extremophilic community inhabiting halite nodules (salt rocks) from the Atacama Desert, sampled over two years with different weather conditions, with SnapT – a new sRNA annotation pipeline – we discovered hundreds of intergenic (itsRNAs) and antisense (asRNAs) sRNAs expressed.

2020-01-09 | GSE137164 | GEO

Project description:Metabarcoding of a Zooplankton community

| PRJNA622488 | ENA

Metatranscriptomes of phytoplankton blooms from an ocean acidification mesocosm experiment

Project description:Sequencing the metatranscriptome can provide information about the response of organisms to varying environmental conditions. We present a methodology for obtaining random whole-community mRNA from a complex microbial assemblage using Pyrosequencing. The metatranscriptome had, with minimum contamination by ribosomal RNA, significant coverage of abundant transcripts, and included significantly more potentially novel proteins than in the metagenome. Keywords: metatranscriptome, mesocosm, ocean acidification This experiment is part of a much larger experiment. We have produced 4 454 metatranscriptomic datasets and 6 454 metagenomic datasets. These were derived from 4 samples. The experiment is an ocean acidification mesocosm set up in a Norwegian Fjord in 2006. We suspended 6 bags containing 11,000 L of sea water in a Coastal Fjord and then we bubbled CO2 through three of these bags to simulate ocean acidification conditions in the year 2100. The other three bags were bubbled with air. We then induced a phytoplankton bloom in all six bags and took measurements and performed analyses of phytoplankton, bacterioplankton and physiochemical characteristics over a 22 day period. We took water samples from the peak of the phytoplankton bloom and following the decline of the phytoplankton bloom to analyses using 454 metagenomics and 454 metatranscriptomics. Day 1, High CO2 Bag and Day 1, Present Day Bag, refer to the metatranscriptomes from the peak of the bloom. Day 2, High CO2 Bag and Day 2, Present Day Bag, refer to the metatranscriptomes following the decline of the bloom. Obviously High CO2 refers to the ocean acidification mesocosm and Present Day refers to the control mesocosm. Raw data for both the metagenomic and metatranscriptomic components are available at NCBI's Short Read Archive at ftp://ftp.ncbi.nlm.nih.gov/sra/Studies/SRP000/SRP000101

2008-02-08 | E-GEOD-10119 | biostudies-arrayexpress

Comparative genomics of Staphylococcus aureus and the application of a “pan-genome” for assigning RNA-Seq transcript reads from divergent strains and in vivo human nasal samples

Project description:in vitro comparison between two MRSA grown in rich (BHI) and poor media (SNM), compared with the nasal metatranscriptome reads of S. aureus. Global expression profile of two MRSA strains of S.aureus harvested in two different growth phases and compared with a metatranscriptome nose sample of a S. aureus carrier.

2015-01-05 | E-GEOD-56294 | biostudies-arrayexpress

Metaproteome analysis of cyanobacterial crusts exposed to stratosphere

Project description:Metaproteome of cyanobacterial crusts exposed to stratosphere. To explore changes in community structure and function. The changes in function involve overall metabolic response, interactions between species and function, and content related to life history strategies. The article mainly focuses on metagenomic and metatranscriptome data, while metaproteomic data is mainly used to assist in confirming the conclusions drawn.

2024-02-19 | PXD049715 |

Transcriptome profiling reveals that feeding live zooplankton to larval Atlantic cod (Gadus morhua) influences suites of genes involved in oxidation-reduction, mitosis, and selenium homeostasis.

Project description:In this study, microarrays were used to investigate the larval cod transcriptome response to zooplankton supplementation in the diet.

2015-10-06 | GSE68792 | GEO

Metatranscriptome analysis reveals the putative venom toxin repertoire of the biofouling hydroid Ectopleura larynx

Project description:Metatranscriptome analysis reveals the putative venom toxin repertoire of the biofouling hydroid Ectopleura larynx

2023-09-29 | E-MTAB-13150 | biostudies-arrayexpress

Consistency across multi-omics layers in a drug-perturbed gut microbial community

Project description:Here we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers, namely 16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics, and metabolomics. Using this controlled setting, we find that all omics methods with species resolution in their readouts are highly consistent in estimating relative species abundances across conditions. Furthermore, different omics methods can be complementary in their ability to capture functional changes in response to the drug perturbations. For example, while nearly all omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control and metabolomics revealed a decrease in polysaccharide uptake, likely caused by Bacteroidota depletion. Taken together, our study provides insights into how multi-omics datasets can be utilised to reveal complex molecular responses to external perturbations in microbial communities.

2022-12-13 | PXD036445 | Pride

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