Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics.

ABSTRACT: The Mergeomics web server is a flexible online tool for multi-omics data integration to derive biological pathways, networks, and key drivers important to disease pathogenesis and is based on the open source Mergeomics R package. The web server takes summary statistics of multi-omics disease association studies (GWAS, EWAS, TWAS, PWAS, etc.) as input and features four functions: Marker Dependency Filtering (MDF) to correct for known dependency between omics markers, Marker Set Enrichment Analysis (MSEA) to detect disease relevant biological processes, Meta-MSEA to examine the consistency of biological processes informed by various omics datasets, and Key Driver Analysis (KDA) to identify essential regulators of disease-associated pathways and networks. The web server has been extensively updated and streamlined in version 2.0 including an overhauled user interface, improved tutorials and results interpretation for each analytical step, inclusion of numerous disease GWAS, functional genomics datasets, and molecular networks to allow for comprehensive omics integrations, increased functionality to decrease user workload, and increased flexibility to cater to user-specific needs. Finally, we have incorporated our newly developed drug repositioning pipeline PharmOmics for prediction of potential drugs targeting disease processes that were identified by Mergeomics. Mergeomics is freely accessible at http://mergeomics.research.idre.ucla.edu and does not require login.

SUBMITTER: Ding J

PROVIDER: S-EPMC8262738 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

OmicsNet 2.0: a web-based platform for multi-omics integration and network visual analytics.

Project description:Researchers are increasingly seeking to interpret molecular data within a multi-omics context to gain a more comprehensive picture of their study system. OmicsNet (www.omicsnet.ca) is a web-based tool developed to allow users to easily build, visualize, and analyze multi-omics networks to study rich relationships among lists of 'omics features of interest. Three major improvements have been introduced in OmicsNet 2.0, which include: (i) enhanced network visual analytics with eleven 2D graph layout options and a novel 3D module layout; (ii) support for three new 'omics types: single nucleotide polymorphism (SNP) list from genetic variation studies; taxon list from microbiome profiling studies, as well as liquid chromatography-mass spectrometry (LC-MS) peaks from untargeted metabolomics; and (iii) measures to improve research reproducibility by coupling R command history with the release of the companion OmicsNetR package, and generation of persistent links to share interactive network views. We performed a case study using the multi-omics data obtained from a recent large-scale investigation on inflammatory bowel disease (IBD) and demonstrated that OmicsNet was able to quickly create meaningful multi-omics context to facilitate hypothesis generation and mechanistic insights.

| S-EPMC9252810 | biostudies-literature

The RING 2.0 web server for high quality residue interaction networks.

Project description:Residue interaction networks (RINs) are an alternative way of representing protein structures where nodes are residues and arcs physico-chemical interactions. RINs have been extensively and successfully used for analysing mutation effects, protein folding, domain-domain communication and catalytic activity. Here we present RING 2.0, a new version of the RING software for the identification of covalent and non-covalent bonds in protein structures, including π-π stacking and π-cation interactions. RING 2.0 is extremely fast and generates both intra and inter-chain interactions including solvent and ligand atoms. The generated networks are very accurate and reliable thanks to a complex empirical re-parameterization of distance thresholds performed on the entire Protein Data Bank. By default, RING output is generated with optimal parameters but the web server provides an exhaustive interface to customize the calculation. The network can be visualized directly in the browser or in Cytoscape. Alternatively, the RING-Viz script for Pymol allows visualizing the interactions at atomic level in the structure. The web server and RING-Viz, together with an extensive help and tutorial, are available from URL: http://protein.bio.unipd.it/ring.

| S-EPMC4987896 | biostudies-literature

ExpressVis: a biologist-oriented interactive web server for exploring multi-omics data.

Project description:In the era of life-omics, huge amounts of multi-omics data have been generated and widely used in biomedical research. It is challenging for biologists with limited programming skills to obtain biological insights from multi-omics data. Thus, a biologist-oriented platform containing visualization functions is needed to make complex omics data digestible. Here, we propose an easy-to-use, interactive web server named ExpressVis. In ExpressVis, users can prepare datasets; perform differential expression analysis, clustering analysis, and survival analysis; and integrate expression data with protein-protein interaction networks and pathway maps. These analyses are organized into six modules. Users can use each module independently or use several modules interactively. ExpressVis displays analysis results in interactive figures and tables, and provides comprehensive interactive operations in each figure and table, between figures or tables in each module, and among different modules. It is freely accessible at https://omicsmining.ncpsb.org.cn/ExpressVis and does not require login. To test the performance of ExpressVis for multi-omics studies of clinical cohorts, we re-analyzed a published hepatocellular carcinoma dataset and reproduced their main findings, suggesting that ExpressVis is convenient enough to analyze multi-omics data. Based on its complete analysis processes and unique interactive operations, ExpressVis provides an easy-to-use solution for exploring multi-omics data.

| S-EPMC9252728 | biostudies-literature

Integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems.

Project description:Microbes continually shape Earth's biochemical and physical landscapes by inhabiting diverse metabolic niches. Despite the important role microbes play in ecosystem functioning, most microbial species remain unknown highlighting a gap in our understanding of structured complex ecosystems. To elucidate the relevance of these unknown taxa, often referred to as "microbial dark matter," the integration of multiple high throughput sequencing technologies was used to evaluate the co-occurrence and connectivity of all microbes within the community. Since there are no standard methodologies for multi-omics integration of microbiome data, we evaluated the abundance of "microbial dark matter" in microbialite-forming communities using different types meta-omic datasets: amplicon, metagenomic, and metatranscriptomic sequencing previously generated for this ecosystem. Our goal was to compare the community structure and abundances of unknown taxa within the different data types rather than to perform a functional characterization of the data. Metagenomic and metatranscriptomic data were input into SortMeRNA to extract 16S rRNA gene reads. The output, as well as amplicon sequences, were processed through QIIME2 for taxonomy analysis. The R package mdmnets was utilized to build co-occurrence networks. Most hubs presented unknown classifications, even at the phyla level. Comparisons of the highest scoring hubs of each data type using sequence similarity networks allowed the identification of the most relevant hubs within the microbialite-forming communities. This work highlights the importance of unknown taxa in community structure and proposes that ecosystem network construction can be used on several types of data to identify keystone taxa and their potential function within microbial ecosystems.

| S-EPMC10416242 | biostudies-literature

Multi-omics integration reveals molecular networks and regulators of psoriasis.

Project description:BackgroundPsoriasis is a complex multi-factorial disease, involving both genetic susceptibilities and environmental triggers. Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have been carried out to identify genetic and epigenetic variants that are associated with psoriasis. However, these loci cannot fully explain the disease pathogenesis.MethodsTo achieve a comprehensive mechanistic understanding of psoriasis, we conducted a systems biology study, integrating multi-omics datasets including GWAS, EWAS, tissue-specific transcriptome, expression quantitative trait loci (eQTLs), gene networks, and biological pathways to identify the key genes, processes, and networks that are genetically and epigenetically associated with psoriasis risk.ResultsThis integrative genomics study identified both well-characterized (e.g., the IL17 pathway in both GWAS and EWAS) and novel biological processes (e.g., the branched chain amino acid catabolism process in GWAS and the platelet and coagulation pathway in EWAS) involved in psoriasis. Finally, by utilizing tissue-specific gene regulatory networks, we unraveled the interactions among the psoriasis-associated genes and pathways in a tissue-specific manner and detected potential key regulatory genes in the psoriasis networks.ConclusionsThe integration and convergence of multi-omics signals provide deeper and comprehensive insights into the biological mechanisms associated with psoriasis susceptibility.

| S-EPMC6332659 | biostudies-literature

MiBiOmics: an interactive web application for multi-omics data exploration and integration.

Project description:BackgroundMulti-omics experimental approaches are becoming common practice in biological and medical sciences underlining the need to design new integrative techniques and applications to enable the multi-scale characterization of biological systems. The integrative analysis of heterogeneous datasets generally allows to acquire additional insights and generate novel hypotheses about a given biological system. However, it can become challenging given the often-large size of omics datasets and the diversity of existing techniques. Moreover, visualization tools for interpretation are usually non-accessible to biologists without programming skills.ResultsHere, we present MiBiOmics, a web-based and standalone application that facilitates multi-omics data visualization, exploration, integration, and analysis by providing easy access to dedicated and interactive protocols. It implements classical ordination techniques and the inference of omics-based (multilayer) networks to mine complex biological systems, and identify robust biomarkers linked to specific contextual parameters or biological states.ConclusionsMiBiOmics provides easy-access to exploratory ordination techniques and to a network-based approach for integrative multi-omics analyses through an intuitive and interactive interface. MiBiOmics is currently available as a Shiny app at https://shiny-bird.univ-nantes.fr/app/Mibiomics and as a standalone application at https://gitlab.univ-nantes.fr/combi-ls2n/mibiomics .

| S-EPMC7789220 | biostudies-literature

The anisotropic network model web server at 2015 (ANM 2.0).

Project description:The anisotropic network model (ANM) is one of the simplest yet powerful tools for exploring protein dynamics. Its main utility is to predict and visualize the collective motions of large complexes and assemblies near their equilibrium structures. The ANM server, introduced by us in 2006 helped making this tool more accessible to non-sophisticated users. We now provide a new version (ANM 2.0), which allows inclusion of nucleic acids and ligands in the network model and thus enables the investigation of the collective motions of protein-DNA/RNA and -ligand systems. The new version offers the flexibility of defining the system nodes and the interaction types and cutoffs. It also includes extensive improvements in hardware, software and graphical interfaces.ANM 2.0 is available at http://anm.csb.pitt.edueran.eyal@sheba.health.gov.il, eyal.eran@gmail.com.

| S-EPMC4410662 | biostudies-literature

AllerCatPro 2.0: a web server for predicting protein allergenicity potential.

Project description:Proteins in food and personal care products can pose a risk for an immediate immunoglobulin E (IgE)-mediated allergic response. Bioinformatic tools can assist to predict and investigate the allergenic potential of proteins. Here we present AllerCatPro 2.0, a web server that can be used to predict protein allergenicity potential with better accuracy than other computational methods and new features that help assessors making informed decisions. AllerCatPro 2.0 predicts the similarity between input proteins using both their amino acid sequences and predicted 3D structures towards the most comprehensive datasets of reliable proteins associated with allergenicity. These datasets currently include 4979 protein allergens, 162 low allergenic proteins, and 165 autoimmune allergens with manual expert curation from the databases of WHO/International Union of Immunological Societies (IUIS), Comprehensive Protein Allergen Resource (COMPARE), Food Allergy Research and Resource Program (FARRP), UniProtKB and Allergome. Various examples of profilins, autoimmune allergens, low allergenic proteins, very large proteins, and nucleotide input sequences showcase the utility of AllerCatPro 2.0 for predicting protein allergenicity potential. The AllerCatPro 2.0 web server is freely accessible at https://allercatpro.bii.a-star.edu.sg.

| S-EPMC9252832 | biostudies-literature

GENIUS: web server to predict local gene networks and key genes for biological functions.

Project description:SummaryGENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods.Availability and implementationGENIUS currently supports eight model organisms and is freely available for public use at http://networks.bio.puc.cl/genius .Contactgenius.psbl@gmail.com.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC5408817 | biostudies-literature

multiSLIDE is a web server for exploring connected elements of biological pathways in multi-omics data.

Project description:Quantitative multi-omics data are difficult to interpret and visualize due to large volume of data, complexity among data features, and heterogeneity of information represented by different omics platforms. Here, we present multiSLIDE, a web-based interactive tool for the simultaneous visualization of interconnected molecular features in heatmaps of multi-omics data sets. multiSLIDE visualizes biologically connected molecular features by keyword search of pathways or genes, offering convenient functionalities to query, rearrange, filter, and cluster data on a web browser in real time. Various querying mechanisms make it adaptable to diverse omics types, and visualizations are customizable. We demonstrate the versatility of multiSLIDE through three examples, showcasing its applicability to a wide range of multi-omics data sets, by allowing users to visualize established links between molecules from different omics data, as well as incorporate custom inter-molecular relationship information into the visualization. Online and stand-alone versions of multiSLIDE are available at https://github.com/soumitag/multiSLIDE .

| S-EPMC8052434 | 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