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Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics

The Mergeomics web server is a flexible online tool for multi-omics data integration to derive biological pathways, networks, and key drivers important to disease pathogenesis and is based on the open source Mergeomics R package. The web server takes summary statistics of multi-omics disease associa...

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Autores principales: Ding, Jessica, Blencowe, Montgomery, Nghiem, Thien, Ha, Sung-min, Chen, Yen-Wei, Li, Gaoyan, Yang, Xia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262738/
https://www.ncbi.nlm.nih.gov/pubmed/34048577
http://dx.doi.org/10.1093/nar/gkab405
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author Ding, Jessica
Blencowe, Montgomery
Nghiem, Thien
Ha, Sung-min
Chen, Yen-Wei
Li, Gaoyan
Yang, Xia
author_facet Ding, Jessica
Blencowe, Montgomery
Nghiem, Thien
Ha, Sung-min
Chen, Yen-Wei
Li, Gaoyan
Yang, Xia
author_sort Ding, Jessica
collection PubMed
description The Mergeomics web server is a flexible online tool for multi-omics data integration to derive biological pathways, networks, and key drivers important to disease pathogenesis and is based on the open source Mergeomics R package. The web server takes summary statistics of multi-omics disease association studies (GWAS, EWAS, TWAS, PWAS, etc.) as input and features four functions: Marker Dependency Filtering (MDF) to correct for known dependency between omics markers, Marker Set Enrichment Analysis (MSEA) to detect disease relevant biological processes, Meta-MSEA to examine the consistency of biological processes informed by various omics datasets, and Key Driver Analysis (KDA) to identify essential regulators of disease-associated pathways and networks. The web server has been extensively updated and streamlined in version 2.0 including an overhauled user interface, improved tutorials and results interpretation for each analytical step, inclusion of numerous disease GWAS, functional genomics datasets, and molecular networks to allow for comprehensive omics integrations, increased functionality to decrease user workload, and increased flexibility to cater to user-specific needs. Finally, we have incorporated our newly developed drug repositioning pipeline PharmOmics for prediction of potential drugs targeting disease processes that were identified by Mergeomics. Mergeomics is freely accessible at http://mergeomics.research.idre.ucla.edu and does not require login.
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spelling pubmed-82627382021-07-08 Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics Ding, Jessica Blencowe, Montgomery Nghiem, Thien Ha, Sung-min Chen, Yen-Wei Li, Gaoyan Yang, Xia Nucleic Acids Res Web Server Issue The Mergeomics web server is a flexible online tool for multi-omics data integration to derive biological pathways, networks, and key drivers important to disease pathogenesis and is based on the open source Mergeomics R package. The web server takes summary statistics of multi-omics disease association studies (GWAS, EWAS, TWAS, PWAS, etc.) as input and features four functions: Marker Dependency Filtering (MDF) to correct for known dependency between omics markers, Marker Set Enrichment Analysis (MSEA) to detect disease relevant biological processes, Meta-MSEA to examine the consistency of biological processes informed by various omics datasets, and Key Driver Analysis (KDA) to identify essential regulators of disease-associated pathways and networks. The web server has been extensively updated and streamlined in version 2.0 including an overhauled user interface, improved tutorials and results interpretation for each analytical step, inclusion of numerous disease GWAS, functional genomics datasets, and molecular networks to allow for comprehensive omics integrations, increased functionality to decrease user workload, and increased flexibility to cater to user-specific needs. Finally, we have incorporated our newly developed drug repositioning pipeline PharmOmics for prediction of potential drugs targeting disease processes that were identified by Mergeomics. Mergeomics is freely accessible at http://mergeomics.research.idre.ucla.edu and does not require login. Oxford University Press 2021-05-28 /pmc/articles/PMC8262738/ /pubmed/34048577 http://dx.doi.org/10.1093/nar/gkab405 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Web Server Issue
Ding, Jessica
Blencowe, Montgomery
Nghiem, Thien
Ha, Sung-min
Chen, Yen-Wei
Li, Gaoyan
Yang, Xia
Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics
title Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics
title_full Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics
title_fullStr Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics
title_full_unstemmed Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics
title_short Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics
title_sort mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262738/
https://www.ncbi.nlm.nih.gov/pubmed/34048577
http://dx.doi.org/10.1093/nar/gkab405
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