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      PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data

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          Abstract

          The increasing availability of multi-omic platforms poses new challenges to data analysis. Joint visualization of multi-omics data is instrumental in better understanding interconnections across molecular layers and in fully utilizing the multi-omic resources available to make biological discoveries. We present here PaintOmics 3, a web-based resource for the integrated visualization of multiple omic data types onto KEGG pathway diagrams. PaintOmics 3 combines server-end capabilities for data analysis with the potential of modern web resources for data visualization, providing researchers with a powerful framework for interactive exploration of their multi-omics information. Unlike other visualization tools, PaintOmics 3 covers a comprehensive pathway analysis workflow, including automatic feature name/identifier conversion, multi-layered feature matching, pathway enrichment, network analysis, interactive heatmaps, trend charts, and more. It accepts a wide variety of omic types, including transcriptomics, proteomics and metabolomics, as well as region-based approaches such as ATAC-seq or ChIP-seq data. The tool is freely available at www.paintomics.org

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          Pathview: an R/Bioconductor package for pathway-based data integration and visualization

          Weijun Luo, Cory Brouwer (2013)
          Summary: Pathview is a novel tool set for pathway-based data integration and visualization. It maps and renders user data on relevant pathway graphs. Users only need to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps and integrates user data onto the pathway and renders pathway graphs with the mapped data. Although built as a stand-alone program, Pathview may seamlessly integrate with pathway and functional analysis tools for large-scale and fully automated analysis pipelines. Availability: The package is freely available under the GPLv3 license through Bioconductor and R-Forge. It is available at http://bioconductor.org/packages/release/bioc/html/pathview.html and at http://Pathview.r-forge.r-project.org/. Contact: luo_weijun@yahoo.com Supplementary information: Supplementary data are available at Bioinformatics online.
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            Pathview Web: user friendly pathway visualization and data integration

            Weijun Luo, Gaurav Pant, Yeshvant Bhavnasi … (2017)
            Abstract Pathway analysis is widely used in omics studies. Pathway-based data integration and visualization is a critical component of the analysis. To address this need, we recently developed a novel R package called Pathview. Pathview maps, integrates and renders a large variety of biological data onto molecular pathway graphs. Here we developed the Pathview Web server, as to make pathway visualization and data integration accessible to all scientists, including those without the special computing skills or resources. Pathview Web features an intuitive graphical web interface and a user centered design. The server not only expands the core functions of Pathview, but also provides many useful features not available in the offline R package. Importantly, the server presents a comprehensive workflow for both regular and integrated pathway analysis of multiple omics data. In addition, the server also provides a RESTful API for programmatic access and conveniently integration in third-party software or workflows. Pathview Web is openly and freely accessible at https://pathview.uncc.edu/.
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              Dimension reduction techniques for the integrative analysis of multi-omics data

              Chen Meng, Oana A. Zeleznik, Gerhard Thallinger … (2016)
              State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput ‘omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date (Print): 02 July 2018
                Publication date (Electronic): 25 May 2018
                Publication date PMC-release: 25 May 2018
                Volume: 46
                Issue: Web Server issue
                Pages: W503-W509
                Affiliations
                [1 ]Genomics of Gene Expression Lab, Centro de Investigación Príncipe Felipe, Valencia, Spain
                [2 ]Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Spain
                [3 ]Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL, USA
                [4 ]Genetics Institute, University of Florida, Gainesville, FL, USA
                [5 ]Department of Cellular Biology, University of Brasilia, Biological Sciences Institute, Brasília, Brazil
                Author notes
                To whom correspondence should be addressed. Tel: +34 963 289 680; Fax: +34 963 289 701; Email: aconesa@ 123456cipf.es

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

                Article
                Publisher ID: gky466
                DOI: 10.1093/nar/gky466
                PMC ID: 6030972
                PubMed ID: 29800320
                SO-VID: e2efaf73-3e5a-4fc3-b38f-f171362ebbd5
                Copyright © © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial 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
                Date accepted : 16 May 2018
                Date revision received : 20 April 2018
                Date received : 22 February 2018
                Page count
                Pages: 7
                Funding
                Funded by: Seventh Framework Programme 10.13039/100011102
                Award ID: 306000-STATegra
                Funded by: Marie Curie International Research Staff Exchange Scheme s
                Award ID: 612583-DEANN
                Funded by: Spanish MINECO
                Award ID: BIO2012-40244
                Funded by: INB
                Award ID: PT17/0009/0015
                Categories
                Subject: Web Server Issue

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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