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

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          Abstract

          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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          Graphical Abstract

          Mergeomics uses single omics or multi-omics data to produce pathway- and network-level mechanistic understanding of disease and identify potential therapeutic targets.

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          Most cited references96

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          WGCNA: an R package for weighted correlation network analysis

          Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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            Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

            A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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              A global reference for human genetic variation

              The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                02 July 2021
                28 May 2021
                28 May 2021
                : 49
                : W1
                : W375-W387
                Affiliations
                Department of Integrative Biology and Physiology, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Interdepartmental Program of Molecular, Cellular and Integrative Physiology, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Department of Integrative Biology and Physiology, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Interdepartmental Program of Molecular, Cellular and Integrative Physiology, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Department of Integrative Biology and Physiology, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Department of Integrative Biology and Physiology, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Department of Integrative Biology and Physiology, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Interdepartmental Program of Molecular Toxicology, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Department of Integrative Biology and Physiology, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Interdepartmental Program of Molecular, Cellular and Integrative Physiology, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Department of Integrative Biology and Physiology, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Interdepartmental Program of Molecular, Cellular and Integrative Physiology, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Interdepartmental Program of Molecular Toxicology, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Interdepartmental Program of Bioinformatics, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Institute for Quantitative and Computational Biosciences, University of California , Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
                Author notes
                To whom correspondence should be addressed. Tel: +1 310 206 1812; Fax: +1 310 206 9184; Email: xyang123@ 123456ucla.edu

                The authors wish it to be known that, in their opinion, the first three authors should be regarded as Joint First Authors.

                Author information
                https://orcid.org/0000-0002-6320-0777
                https://orcid.org/0000-0001-7147-1895
                https://orcid.org/0000-0002-3971-038X
                Article
                gkab405
                10.1093/nar/gkab405
                8262738
                34048577
                85a27e63-e637-4230-963a-fe6cb8c7ef53
                © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.

                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/), 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@ 123456oup.com

                History
                : 02 May 2021
                : 28 April 2021
                : 28 February 2021
                Page count
                Pages: 13
                Funding
                Funded by: NIH, DOI 10.13039/100000002;
                Award ID: NS117148
                Award ID: NS111378
                Award ID: DK117850
                Award ID: HL145708
                Award ID: HL147883
                Award ID: HD100298
                Funded by: American Heart Association, DOI 10.13039/100000968;
                Funded by: UCLA, DOI 10.13039/100007185;
                Categories
                AcademicSubjects/SCI00010
                Web Server Issue

                Genetics
                Genetics

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