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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.


Screenshots of the network visualization module output generated by the default sample files in the wKDA module. a Overview of a network comprised of top KDs for LDL-associated gene sets, with 5 KDs for each gene set displayed. KDs are depicted as diamond nodes and node colors indicating gene set membership. b Focused subnetwork view of key driver of interest Fasn when filtering the network in (a) by submitting a “Fasn” query or right clicking the node. c The network can be filtered to display only nodes with edge weights higher than a certain threshold: 4.0 in this case
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Fig5: Screenshots of the network visualization module output generated by the default sample files in the wKDA module. a Overview of a network comprised of top KDs for LDL-associated gene sets, with 5 KDs for each gene set displayed. KDs are depicted as diamond nodes and node colors indicating gene set membership. b Focused subnetwork view of key driver of interest Fasn when filtering the network in (a) by submitting a “Fasn” query or right clicking the node. c The network can be filtered to display only nodes with edge weights higher than a certain threshold: 4.0 in this case

Mentions: Our web server provides a convenient module to allow users visualize top KDs and subnetworks using Cytoscape Web v0.8 [17]. The top 5 KDs for each disease gene set from wKDA will be automatically visualized, as exemplified in Fig. 5a. The visualization is interactive so that users can make real-time changes such as zooming in on a node of interest by only considering that particular subnetwork (Fig. 5b) or by filtering a subnetwork based on the edge weight information (Fig. 5c), as detailed in the tutorial page.Fig. 5


Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration
Screenshots of the network visualization module output generated by the default sample files in the wKDA module. a Overview of a network comprised of top KDs for LDL-associated gene sets, with 5 KDs for each gene set displayed. KDs are depicted as diamond nodes and node colors indicating gene set membership. b Focused subnetwork view of key driver of interest Fasn when filtering the network in (a) by submitting a “Fasn” query or right clicking the node. c The network can be filtered to display only nodes with edge weights higher than a certain threshold: 4.0 in this case
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Related In: Results  -  Collection

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

Fig5: Screenshots of the network visualization module output generated by the default sample files in the wKDA module. a Overview of a network comprised of top KDs for LDL-associated gene sets, with 5 KDs for each gene set displayed. KDs are depicted as diamond nodes and node colors indicating gene set membership. b Focused subnetwork view of key driver of interest Fasn when filtering the network in (a) by submitting a “Fasn” query or right clicking the node. c The network can be filtered to display only nodes with edge weights higher than a certain threshold: 4.0 in this case
Mentions: Our web server provides a convenient module to allow users visualize top KDs and subnetworks using Cytoscape Web v0.8 [17]. The top 5 KDs for each disease gene set from wKDA will be automatically visualized, as exemplified in Fig. 5a. The visualization is interactive so that users can make real-time changes such as zooming in on a node of interest by only considering that particular subnetwork (Fig. 5b) or by filtering a subnetwork based on the edge weight information (Fig. 5c), as detailed in the tutorial page.Fig. 5

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.