Mergeomics: A web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration

Douglas Arneson, Anindya Bhattacharya, Le Shu, Ville Petteri Mäkinen, Xia Yang

Research output: Contribution to journalArticle

16 Citations (Scopus)

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.

LanguageEnglish
Article number722
JournalBMC Genomics
Volume17
Issue number1
DOIs
Publication statusPublished - 9 Sep 2016

Keywords

  • Disease network
  • EWAS
  • GWAS
  • Key driver
  • Multidimensional data integration
  • Network meta-analysis
  • Omics integration
  • Pathway meta-analysis
  • TWAS
  • Web server

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

Cite this

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title = "Mergeomics: A web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration",
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.",
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Mergeomics : A web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration. / Arneson, Douglas; Bhattacharya, Anindya; Shu, Le; Mäkinen, Ville Petteri; Yang, Xia.

In: BMC Genomics, Vol. 17, No. 1, 722, 09.09.2016.

Research output: Contribution to journalArticle

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