Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/108851
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Type: Journal article
Title: Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration
Author: Arneson, D.
Bhattacharya, A.
Shu, L.
Mäkinen, V.
Yang, X.
Citation: BMC Genomics, 2016; 17(1):722-1-722-9
Publisher: BioMed Central
Issue Date: 2016
ISSN: 1471-2164
1471-2164
Statement of
Responsibility: 
Douglas Arneson, Anindya Bhattacharya, Le Shu, Ville-Petteri Mäkinen, and Xia Yang
Abstract: Background: Human diseases are commonly the result of multidimensional changes at molecular, cellular, and systemic levels. Recent advances in genomic technologies have enabled an outpour of omics datasets that capture these changes. However, separate analyses of these various data only provide fragmented understanding and do not capture the holistic view of disease mechanisms. To meet the urgent needs for tools that effectively integrate multiple types of omics data to derive biological insights, we have developed Mergeomics, a computational pipeline that integrates multidimensional disease association data with functional genomics and molecular networks to retrieve biological pathways, gene networks, and central regulators critical for disease development. Results: To make the Mergeomics pipeline available to a wider research community, we have implemented an online, user-friendly web server (http://mergeomics.research.idre.ucla.edu/). The web server features a modular implementation of the Mergeomics pipeline with detailed tutorials. Additionally, it provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. User-defined summary-level genomic association datasets (e.g., genetic, transcriptomic, epigenomic) related to a particular disease or phenotype can be uploaded and computed real-time to yield biologically interpretable results, which can be viewed online and downloaded for later use. Conclusions: Our Mergeomics web server offers researchers flexible and user-friendly tools to facilitate integration of multidimensional data into holistic views of disease mechanisms in the form of tissue-specific key regulators, biological pathways, and gene networks.
Keywords: multidimensional data integration; omics integration; web server; pathway meta-analysis; network meta-analysis; disease network; key driver; GWAS; EWAS; TWAS
Rights: © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
RMID: 0030060094
DOI: 10.1186/s12864-016-3057-8
Appears in Collections:Molecular and Biomedical Science publications

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