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

View Article: PubMed Central - PubMed

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.

Electronic supplementary material: The online version of this article (doi:10.1186/s12864-016-3057-8) contains supplementary material, which is available to authorized users.

No MeSH data available.


Web server pipeline overview with red lines indicating cross-module interactions, green headers indicating resource pages, and teal headers indicating analytical modules
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Fig1: Web server pipeline overview with red lines indicating cross-module interactions, green headers indicating resource pages, and teal headers indicating analytical modules

Mentions: As depicted in Fig. 1, the web server hosts three resource pages (HOME, TUTORIAL, and DOWNLOAD) and four analytical modules (MDF, MSEA, Meta-MSEA, and wKDA). The “HOME” page provides basic information about the Mergeomics pipeline including data types involved, overall workflow, and citation information. The “TUTORIAL” section provides step-by-step instructions for using the web server and detailed descriptions about input file format requirements, analysis parameters, and result interpretation for each analytical component. The “DOWNLOAD” section gives users access to the standalone Mergeomics R package and a wide variety of useful, publically available genomic resources applicable in the pipeline, including sample disease association studies, tissue-specific eQTL studies, ENCODE resources, knowledge-driven biochemical and signaling pathways, and data-driven gene networks (Additional file 1: Table S1). The four analytical modules are: 1) MDF, standing for Marker Dependency Filtering, which corrects for dependencies between omics markers (e.g. correcting for linkage disequilibrium or LD between genetic variants in GWAS), 2) MSEA, standing for Marker Set Enrichment Analysis, that allows users to leverage multi-omics association data, functional genomics, canonical pathways and/or data-driven gene modules to identify causal subnetworks of disease or traits; 3) Meta-MSEA, which performs pathway- or network-level meta-analysis when multiple association datasets from different studies (e.g., multiple GWAS) and/or of different data types (e.g., GWAS and EWAS) are available; 4) wKDA, standing for weighted key driver analysis, which maps disease associated genes to tissue-specific gene networks to identify potential key drivers or regulators of disease. We also provide an interactive network visualization module for visualizing select key drivers and their associated disease subnetworks.Fig. 1


Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration
Web server pipeline overview with red lines indicating cross-module interactions, green headers indicating resource pages, and teal headers indicating analytical modules
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5016927&req=5

Fig1: Web server pipeline overview with red lines indicating cross-module interactions, green headers indicating resource pages, and teal headers indicating analytical modules
Mentions: As depicted in Fig. 1, the web server hosts three resource pages (HOME, TUTORIAL, and DOWNLOAD) and four analytical modules (MDF, MSEA, Meta-MSEA, and wKDA). The “HOME” page provides basic information about the Mergeomics pipeline including data types involved, overall workflow, and citation information. The “TUTORIAL” section provides step-by-step instructions for using the web server and detailed descriptions about input file format requirements, analysis parameters, and result interpretation for each analytical component. The “DOWNLOAD” section gives users access to the standalone Mergeomics R package and a wide variety of useful, publically available genomic resources applicable in the pipeline, including sample disease association studies, tissue-specific eQTL studies, ENCODE resources, knowledge-driven biochemical and signaling pathways, and data-driven gene networks (Additional file 1: Table S1). The four analytical modules are: 1) MDF, standing for Marker Dependency Filtering, which corrects for dependencies between omics markers (e.g. correcting for linkage disequilibrium or LD between genetic variants in GWAS), 2) MSEA, standing for Marker Set Enrichment Analysis, that allows users to leverage multi-omics association data, functional genomics, canonical pathways and/or data-driven gene modules to identify causal subnetworks of disease or traits; 3) Meta-MSEA, which performs pathway- or network-level meta-analysis when multiple association datasets from different studies (e.g., multiple GWAS) and/or of different data types (e.g., GWAS and EWAS) are available; 4) wKDA, standing for weighted key driver analysis, which maps disease associated genes to tissue-specific gene networks to identify potential key drivers or regulators of disease. We also provide an interactive network visualization module for visualizing select key drivers and their associated disease subnetworks.Fig. 1

View Article: PubMed Central - PubMed

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.

Electronic supplementary material: The online version of this article (doi:10.1186/s12864-016-3057-8) contains supplementary material, which is available to authorized users.

No MeSH data available.