Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

GNPS - Multi-omics of dental plaque in patients with diabetes and periodontal disease

ABSTRACT: This repository contains raw files and intermediate processing files for an integrated multi-omics analysis of dental plaque.

INSTRUMENT(S): Q Exactive GC-Orbitrap (Thermo Scientific)

ORGANISM(S): Human Human Oral Bacteria Ensemble (ncbitaxon:150596)

SUBMITTER: Josh J. Coon

PROVIDER: MSV000084126 | GNPS | Tue Jul 23 16:41:00 BST 2019

REPOSITORIES: GNPS

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Multi-omics of dental plaque in patients with diabetes and periodontal disease

Project description:This repository contains raw files and intermediate processing files for an integrated multi-omics analysis of dental plaque.

2019-07-23 | MSV000084126 | MassIVE

Integrated multi-omics analysis

Project description:Integrated multi-omics analysis

| PRJNA1069651 | ENA

Reanalysis of dataset PXD003791: “MuSt multiomics - Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes”

Project description:This project contains raw data, intermediate files and results is a re-analysis of the publicly available dataset from the PRIDE dataset PXD003791. The RAW files were processed using ThermoRawFileParser, SearchGUI and PeptideShaker through standard settings (see ‘Data Processing Protocol’). This reanalysis work is part of the MetaPUF (MetaProteomics with Unknown Function) project, which is a collaboration between EMBL-EBI and the University of Luxembourg. The dataset was selected with the following conditions: 1. It has been made publicly available in PRIDE and focuses on metaproteomics of the human gut; 2. The corresponding metagenomics assemblies were also available from ENA (European Nucleotide Archive) or MGnify. The processed peptide reports for each sample are available to view at the contig level on the MGnify website. In total, the reanalysis identified 59,613 unique proteins from 36 samples.

2022-06-17 | PXD034617 | Pride

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

Integrated multi-omics approach revealed cellular senescence landscape

Project description:Integrated multi-omics approach revealed cellular senescence landscape

| PRJNA865031 | ENA

Multi-omics profiling of mouse gastrulation at single cell resolution

Project description:This SuperSeries is composed of the SubSeries listed below. Useful links: Parsed data: (ftp://ftp.ebi.ac.uk/pub/databases/scnmt_gastrulation) Github repository: (https://github.com/rargelaguet/scnmt_gastrulation)

2019-09-30 | GSE121708 | 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

Integrated multi-omics approach revealed cellular senescence landscape

Project description:Cellular senescence is a complex multifactorial biological phenomenon that plays essential roles in aging, and aging-related diseases. During this process, the senescent cells undergo gene expression altering and chromatin structure remodeling. However, studies on the epigenetic landscape of senescence using integrated multi-omics approaches are limited. In this research, we performed ATAC-seq, RNA-seq, and ChIP-seq on different senescent types to reveal the landscape of senescence and identify the prime regulatory elements.

2022-08-15 | GSE210285 | GEO

Understanding the biological processes of clear cell renal carcinoma carcinogenesis: an integrative multi-omics approach.

Project description:Multi-Omics analysis to gain novel insights into clear cell renal carcinoma aetiology and progression. The DNA methylation data of 121 clear clear renal carcinoma (ccRCC) were integrated with WGS and transcriptomic data using Multi-Omics Factor Analysis (MOFA) to detect the inter-patient variations related to aetiological and disease progression related factors.

2024-06-07 | GSE269180 | GEO

T2D of Multi-omics

Project description:T2D of Multi-omics

| PRJEB28715 | ENA