Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Mock multi-omics analysis.

ABSTRACT: Multi-omics analysis of a synthetic gut microbial community.

PROVIDER: PRJEB46619 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	ERR6360030_1.fastq.gz	Fastqsanger.gz
	ERR6360030_2.fastq.gz	Fastqsanger.gz
	ERR6360040_1.fastq.gz	Fastqsanger.gz
	ERR6360040_2.fastq.gz	Fastqsanger.gz
	ERR6360050_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 682

Similar Datasets

Multi-omics analysis revealing the interplay between gut microbiome and the host following opioid use [RNA-seq]

Project description:To uncover the role of opioid induced dysbiosis in disrupting intestinal homeostasis, we conducted a multi-omics analysis with gut microbial, metabolite and intestinal transcriptomics data

2023-08-30 | GSE230095 | GEO

Multi-omics analysis revealing the interplay between gut microbiome and the host following opioid use [ABX-RNA seq]

2023-08-30 | GSE230093 | GEO

Longitudinal multi-omics reveals subset-specific mechanisms underlying irritable bowel syndrome

Project description:The gut microbiome has been implicated in multiple human chronic gastrointestinal (GI) disorders. Determining its mechanistic role in disease pathogenesis has been difficult due to the apparent disconnect between animal and human studies and a lack of an integrated multi-omics view in the context of disease-specific physiological changes. We integrated longitudinal multi-omics data from the gut microbiome, metabolome, host epigenome and transcriptome in the context of irritable bowel syndrome (IBS) host physiology. We identified IBS subtype-specific and symptom-related variation in microbial composition and function. A subset of identified changes in microbial metabolites correspond to host physiological mechanisms that are relevant to IBS. By integrating multiple data layers, we identified purine metabolism as a novel host-microbial metabolic pathway in IBS with translational potential. Our study highlights the importance of longitudinal sampling and integrating complementary multi-omics data to identify functional mechanisms that can serve as therapeutic targets in a comprehensive treatment strategy for chronic GI diseases.

2020-03-13 | GSE146853 | GEO

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

Comprehensive multi-omics analysis of microbiome, metabolome, and host genome in right- and left-sided colon cancer

Project description:There are distinct interactions among microbial compositions, metabolites, and host genomes in RCC and LCC as revealed by multi-omics analysis, which brings a new dimension to understanding the role of gut microbiota in tumorigenesis of RCC and LCC.

2023-10-31 | GSE216022 | GEO

Integrated multi-omics analysis

Project description:Integrated multi-omics analysis

| PRJNA1069651 | ENA

Multi-layer omics dataset for cross-species translational systems biology

Project description:Inferring in humans biological responses to external cues such as drugs, chemicals, viruses and hormones, is an essential question in biomedicine and cannot be easily studied in humans. Thus, biomedical research has continuously relied on animal models for studying the impact of these compounds and attempted to M-^StranslateM-^T the results to humans. In this context, the Systems Biology Verification for Industrial Methodology for Process Verification in Research (SBV IMPROVER) initiative had run a Species Translation Challenge for the scientific community to explore and understand the limit of translatability from rodent to human using systems biology. Therefore, a multi-layer omics dataset was generated that comprised of phosphoproteomics, transcriptomics and cytokine data derived from normal human (NHBE) and rat (NRBE) bronchial epithelial cells exposed in parallel to more than 50 different stimuli under identical conditions. The present manuscript describes in detail the experimental settings, the generation, processing and quality control analysis of the multi-layer omics dataset. The datasets are accessible in public repositories could be leveraged for further translation studies.

2014-06-10 | E-MTAB-2091 | biostudies-arrayexpress

Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge

Project description:Multi-omics has the promise to provide a detailed molecular picture for biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimum structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to associate with a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30°C and 37°C, and ob-tained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofa-ciens at 37°C.

2023-08-18 | PXD042954 | Pride

Multi-omics analysis of aggregative multicellularity

Project description:Multi-omics analysis of aggregative multicellularity

| PRJNA1051075 | ENA

T2D of Multi-omics

Project description:T2D of Multi-omics

| PRJEB28715 | ENA