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

Author:

Ding Jessica12ORCID,Blencowe Montgomery12ORCID,Nghiem Thien1,Ha Sung-min1,Chen Yen-Wei13,Li Gaoyan12,Yang Xia12345ORCID

Affiliation:

1. Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA

2. Interdepartmental Program of Molecular, Cellular and Integrative Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA

3. Interdepartmental Program of Molecular Toxicology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA

4. Interdepartmental Program of Bioinformatics, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA

5. Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA

Abstract

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.

Funder

NIH

American Heart Association

UCLA

Publisher

Oxford University Press (OUP)

Subject

Genetics

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