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.

PharmOmics: A species- and tissue-specific drug signature database and gene-network-based drug repositioning tool

“…To allow easy data access and use of the PharmOmics database, we provide drug signature query, species and tissue comparison, drug repositioning, and drug network visualization on an open access web server Mergeomics 2.0 ( Shu et al., 2016 ; Ding et al., 2021 ) ( http://mergeomics.research.idre.ucla.edu ; STAR Methods ). The PharmOmics web server features three main functions ( Figure 2 A).…”

Chronic adolescent exposure to cannabis in mice leads to sex-biased changes in gene expression networks across brain regions

“…To predict potential key regulators of CUD-associated networks, we performed key driver (KD) analysis using tissue-specific Bayesian networks that infer causal relationships between genes and that were constructed using independent human and mouse data 39 . We identified top KD regulators of gene coexpression networks associated with THC and CUD (overlapping modules in Fig.…”

“…Recent GWAS have started to identify genetic variants associated with CUD 38 . To gain further insights into the genes and pathways associated with CUD, we applied the Mergeomic pipeline 31,39 to integrate the human CUD GWAS signals with THC-correlated gene coexpression networks for each brain region and sex in mice (Fig. 5A).…”

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

“…After analyzing the networks to identify important genes, proteins, or epigenetic marks relevant to the disease in each omics modality, the overlapping entities can be used to identify important biomolecules relevant to the underlying biology ( Figure 3A ). We can also identify relationships between different omics layers using this approach ( 38 ).…”