Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A multi-omics digital research object for the genetics of sleep regulation.

ABSTRACT: With the aim to uncover the molecular pathways underlying the regulation of sleep, we recently assembled an extensive and comprehensive systems genetics dataset interrogating a genetic reference population of mice at the levels of the genome, the brain and liver transcriptomes, the plasma metabolome, and the sleep-wake phenome. To facilitate a meaningful and efficient re-use of this public resource by others we designed, describe in detail, and made available a Digital Research Object (DRO), embedding data, documentation, and analytics. We present and discuss both the advantages and limitations of our multi-modal resource and analytic pipeline. The reproducibility of the results was tested by a bioinformatician not implicated in the original project and the robustness of results was assessed by re-annotating genetic and transcriptome data from the mm9 to the mm10 mouse genome assembly.

SUBMITTER: Jan M

PROVIDER: S-EPMC6823400 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

A multi-omics digital research object for the genetics of sleep regulation.

Jan Maxime M Gobet Nastassia N Diessler Shanaz S Franken Paul P Xenarios Ioannis I

Scientific data 20191031 1

With the aim to uncover the molecular pathways underlying the regulation of sleep, we recently assembled an extensive and comprehensive systems genetics dataset interrogating a genetic reference population of mice at the levels of the genome, the brain and liver transcriptomes, the plasma metabolome, and the sleep-wake phenome. To facilitate a meaningful and efficient re-use of this public resource by others we designed, describe in detail, and made available a Digital Research Object (DRO), emb ...[more]

PMID: 31672980

Similar Datasets

A multi-omics data analysis workflow packaged as a FAIR Digital Object.

Project description:BackgroundApplying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object.FindingsWe conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub.ConclusionsAlong with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.

| S-EPMC10787363 | biostudies-literature

Systems genetics of sleep regulation

Project description:We used a systems genetics approach in the BXD genetic reference population of mice and assembled a comprehensive experimental knowledge base comprising a deep ‘sleep-wake’ phenome, central and peripheral transcriptomes, and plasma metabolome data, collected under undisturbed baseline conditions and after sleep deprivation.

2018-06-26 | GSE114845 | GEO

Systems genetics of sleep regulation

Project description:Systems genetics of sleep regulation

| PRJNA472805 | ENA

Multi-Omics Approaches in Immunological Research.

Project description:The immune system plays a vital role in health and disease, and is regulated through a complex interactive network of many different immune cells and mediators. To understand the complexity of the immune system, we propose to apply a multi-omics approach in immunological research. This review provides a complete overview of available methodological approaches for the different omics data layers relevant for immunological research, including genetics, epigenetics, transcriptomics, proteomics, metabolomics, and cellomics. Thereafter, we describe the various methods for data analysis as well as how to integrate different layers of omics data. Finally, we discuss the possible applications of multi-omics studies and opportunities they provide for understanding the complex regulatory networks as well as immune variation in various immune-related diseases.

| S-EPMC8226116 | biostudies-literature

Multi-Staged Data-Integrated Multi-Omics Analysis for Symptom Science Research.

Project description:The incorporation of omics approaches into symptom science research can provide researchers with information about the molecular mechanisms that underlie symptoms. Most of the omics analyses in symptom science have used a single omics approach. Therefore, these analyses are limited by the information contained within a specific omics domain (e.g., genomics and inherited variations, transcriptomics and gene function). A multi-staged data-integrated multi-omics (MS-DIMO) analysis integrates multiple types of omics data in a single study. With this integration, a MS-DIMO analysis can provide a more comprehensive picture of the complex biological mechanisms that underlie symptoms. The results of a MS-DIMO analysis can be used to refine mechanistic hypotheses and/or discover therapeutic targets for specific symptoms. The purposes of this paper are to: (1) describe a MS-DIMO analysis using "Symptom X" as an example; (2) discuss a number of challenges associated with specific omics analyses and how a MS-DIMO analysis can address them; (3) describe the various orders of omics data that can be used in a MS-DIMO analysis; (4) describe omics analysis tools; and (5) review case exemplars of MS-DIMO analyses in symptom science. This paper provides information on how a MS-DIMO analysis can strengthen symptom science research through the prioritization of functional genes and biological processes associated with a specific symptom.

| S-EPMC8726420 | biostudies-literature

Multi-Omics Analysis for Transcriptional Regulation of Immune-Related Targets Using Epigenetic Data: A New Research Direction.

Project description:BackgroundCurrently, a comprehensive method for exploration of transcriptional regulation has not been well established. We explored a novel pipeline to analyze transcriptional regulation using co-analysis of RNA sequencing (RNA-seq), assay for transposase-accessible chromatin using sequencing (ATAC-seq), and chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq).MethodsThe G protein-coupled receptors (GPCRs) possibly associated with macrophages were further filtered using a reduced-Cox regression model. ATAC-seq profiles were used to map the chromatin accessibility of the GPRC5B promoter region. Pearson analysis was performed to identify the transcription factor (TF) whose expression was correlated with open chromatin regions of GPRC5B promoter. ChIP-seq profiles were obtained to confirm the physical binding of GATA4 and its predicted binding regions. For verification, quantitative polymerase chain reaction (qPCR) and multidimensional database validations were performed.ResultsThe reduced-Cox regression model revealed the prognostic value of GPRC5B. A novel pipeline for TF exploration was proposed. With our novel pipeline, we first identified chr16:19884686-19885185 as a reproducible open chromatin region in the GPRC5B promoter. Thereafter, we confirmed the correlation between GATA4 expression and the accessibility of this region, confirmed its physical binding, and proved in vitro how its overexpression could regulate GPRC5B. GPRC5B was significantly downregulated in colon adenocarcinoma (COAD) as seen in 28 patient samples. The correlation between GPRC5B and macrophages in COAD was validated using multiple databases.ConclusionGPRC5B, correlated with macrophages, was a key GPCR affecting COAD prognosis. Further, with our novel pipeline, TF GATA4 was identified as a direct upstream of GPRC5B. This study proposed a novel pipeline for TF exploration and provided a theoretical basis for COAD therapy.

| S-EPMC8761734 | biostudies-literature

ProteomicsDB: a multi-omics and multi-organism resource for life science research.

Project description:ProteomicsDB (https://www.ProteomicsDB.org) started as a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. The data types and contents grew over time to include RNA-Seq expression data, drug-target interactions and cell line viability data. In this manuscript, we summarize new developments since the previous update that was published in Nucleic Acids Research in 2017. Over the past two years, we have enriched the data content by additional datasets and extended the platform to support protein turnover data. Another important new addition is that ProteomicsDB now supports the storage and visualization of data collected from other organisms, exemplified by Arabidopsis thaliana. Due to the generic design of ProteomicsDB, all analytical features available for the original human resource seamlessly transfer to other organisms. Furthermore, we introduce a new service in ProteomicsDB which allows users to upload their own expression datasets and analyze them alongside with data stored in ProteomicsDB. Initially, users will be able to make use of this feature in the interactive heat map functionality as well as the drug sensitivity prediction, but ultimately will be able to use all analytical features of ProteomicsDB in this way.

| S-EPMC7145565 | biostudies-literature

Systematic assessment of multi-echo dynamic susceptibility contrast MRI using a digital reference object.

Project description:PurposeBrain tumor dynamic susceptibility contrast (DSC) MRI is adversely impacted by T1 and T2∗ contrast agent leakage effects that result in inaccurate hemodynamic metrics. While multi-echo acquisitions remove T1 leakage effects, there is no consensus on the optimal set of acquisition parameters. Using a computational approach, we systematically evaluated a wide range of acquisition strategies to determine the optimal multi-echo DSC-MRI perfusion protocol.MethodsUsing a population-based DSC-MRI digital reference object (DRO), we assessed the influence of preload dosing (no preload and full dose preload), field strength (1.5 and 3T), pulse sequence parameters (echo time, repetition time, and flip angle), and leakage correction on relative cerebral blood volume (rCBV) and flow (rCBF) accuracy. We also compared multi-echo DSC-MRI protocols with standard single-echo protocols.ResultsMulti-echo DSC-MRI is highly consistent across all protocols, and multi-echo rCBV (with or without use of a preload dose) had higher accuracy than single-echo rCBV. Regression analysis showed that choice of repetition time and flip angle had minimal impact on multi-echo rCBV and rCBV, indicating the potential for significant flexibility in acquisition parameters. The echo time combination had minimal impact on rCBV, though longer echo times should be avoided, particularly at higher field strengths. Leakage correction improved rCBV accuracy in all cases. Multi-echo rCBF was less biased than single-echo rCBF, although rCBF accuracy was reduced overall relative to rCBV.ConclusionsMulti-echo acquisitions were more robust than single-echo, essentially decoupling both repetition time and flip angle from rCBV accuracy. Multi-echo acquisitions obviate the need for preload dosing, although leakage correction to remove residual T2∗ leakage effects remains compulsory for high rCBV accuracy.

| S-EPMC6778704 | biostudies-literature

Multi-omics integration in biomedical research - A metabolomics-centric review.

Project description:Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

| S-EPMC7701361 | biostudies-literature

DriverDBv3: a multi-omics database for cancer driver gene research.

Project description:An integrative multi-omics database is needed urgently, because focusing only on analysis of one-dimensional data falls far short of providing an understanding of cancer. Previously, we presented DriverDB, a cancer driver gene database that applies published bioinformatics algorithms to identify driver genes/mutations. The updated DriverDBv3 database (http://ngs.ym.edu.tw/driverdb) is designed to interpret cancer omics' sophisticated information with concise data visualization. To offer diverse insights into molecular dysregulation/dysfunction events, we incorporated computational tools to define CNV and methylation drivers. Further, four new features, CNV, Methylation, Survival, and miRNA, allow users to explore the relations from two perspectives in the 'Cancer' and 'Gene' sections. The 'Survival' panel offers not only significant survival genes, but gene pairs synergistic effects determine. A fresh function, 'Survival Analysis' in 'Customized-analysis,' allows users to investigate the co-occurring events in user-defined gene(s) by mutation status or by expression in a specific patient group. Moreover, we redesigned the web interface and provided interactive figures to interpret cancer omics' sophisticated information, and also constructed a Summary panel in the 'Cancer' and 'Gene' sections to visualize the features on multi-omics levels concisely. DriverDBv3 seeks to improve the study of integrative cancer omics data by identifying driver genes and contributes to cancer biology.

| S-EPMC7145679 | biostudies-literature

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