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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.


Flexibility of Mergeomics in accommodating different datasets and study designs. a When a single disease association dataset is available, an MSEA-wKDA-visualization flow can be utilized. b When multiple disease association datasets are available, use a Meta-MSEA then wKDA flow is more appropriate. c When selected disease-associated genes or proteins are already identified, wKDA can be directly used. MSEA could also be run if additional association datasets are available, allowing for association testing in other diseases or cross-species validation
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Fig2: Flexibility of Mergeomics in accommodating different datasets and study designs. a When a single disease association dataset is available, an MSEA-wKDA-visualization flow can be utilized. b When multiple disease association datasets are available, use a Meta-MSEA then wKDA flow is more appropriate. c When selected disease-associated genes or proteins are already identified, wKDA can be directly used. MSEA could also be run if additional association datasets are available, allowing for association testing in other diseases or cross-species validation

Mentions: To use the web server, users follow a streamlined workflow (Fig. 1) by setting up the parameters and selecting or uploading datasets necessary for each of the analytical components. Due to the modular design, users are allowed the flexibility to choose subsets of the analytical components according to available datasets and specific study design (Fig. 2). For example, if a single association dataset (e.g., GWAS/EWAS/TWAS) is available, users can identify the causal subnetwork and key regulatory gene of a trait or disease using the MSEA-wKDA-Visualization workflow (Fig. 2a). If multiple association datasets of either the same data type or different data types are available, the Meta-MSEA-wKDA-Visualization workflow is more appropriate (Fig. 2b). If only groups of disease-associated genes are available, the wKDA-Visualization workflow is sufficient to generate the key regulators and disease subnetworks, and users could still explore the association of the gene sets with the same disease in other organisms or other relevant disease types using MSEA or Meta-MSEA, if the corresponding association data is available (Fig. 2c). As technological advances bring about new data types, they can be effectively incorporated into the analysis framework of Mergeomics. In the following sections we explain each analytical component of the Mergeomics web server in detail.Fig. 2


Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration
Flexibility of Mergeomics in accommodating different datasets and study designs. a When a single disease association dataset is available, an MSEA-wKDA-visualization flow can be utilized. b When multiple disease association datasets are available, use a Meta-MSEA then wKDA flow is more appropriate. c When selected disease-associated genes or proteins are already identified, wKDA can be directly used. MSEA could also be run if additional association datasets are available, allowing for association testing in other diseases or cross-species validation
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: Flexibility of Mergeomics in accommodating different datasets and study designs. a When a single disease association dataset is available, an MSEA-wKDA-visualization flow can be utilized. b When multiple disease association datasets are available, use a Meta-MSEA then wKDA flow is more appropriate. c When selected disease-associated genes or proteins are already identified, wKDA can be directly used. MSEA could also be run if additional association datasets are available, allowing for association testing in other diseases or cross-species validation
Mentions: To use the web server, users follow a streamlined workflow (Fig. 1) by setting up the parameters and selecting or uploading datasets necessary for each of the analytical components. Due to the modular design, users are allowed the flexibility to choose subsets of the analytical components according to available datasets and specific study design (Fig. 2). For example, if a single association dataset (e.g., GWAS/EWAS/TWAS) is available, users can identify the causal subnetwork and key regulatory gene of a trait or disease using the MSEA-wKDA-Visualization workflow (Fig. 2a). If multiple association datasets of either the same data type or different data types are available, the Meta-MSEA-wKDA-Visualization workflow is more appropriate (Fig. 2b). If only groups of disease-associated genes are available, the wKDA-Visualization workflow is sufficient to generate the key regulators and disease subnetworks, and users could still explore the association of the gene sets with the same disease in other organisms or other relevant disease types using MSEA or Meta-MSEA, if the corresponding association data is available (Fig. 2c). As technological advances bring about new data types, they can be effectively incorporated into the analysis framework of Mergeomics. In the following sections we explain each analytical component of the Mergeomics web server in detail.Fig. 2

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.