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      PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases

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          Abstract

          PaintOmics is a web server for the integrative analysis and visualisation of multi-omics datasets using biological pathway maps. PaintOmics 4 has several notable updates that improve and extend analyses. Three pathway databases are now supported: KEGG, Reactome and MapMan, providing more comprehensive pathway knowledge for animals and plants. New metabolite analysis methods fill gaps in traditional pathway-based enrichment methods. The metabolite hub analysis selects compounds with a high number of significant genes in their neighbouring network, suggesting regulation by gene expression changes. The metabolite class activity analysis tests the hypothesis that a metabolic class has a higher-than-expected proportion of significant elements, indicating that these compounds are regulated in the experiment. Finally, PaintOmics 4 includes a regulatory omics module to analyse the contribution of trans-regulatory layers (microRNA and transcription factors, RNA-binding proteins) to regulate pathways. We show the performance of PaintOmics 4 on both mouse and plant data to highlight how these new analysis features provide novel insights into regulatory biology. PaintOmics 4 is available at https://paintomics.org/.

          Graphical Abstract

          Graphical Abstract

          Overview of Paintomics 4 functionalities.

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          Most cited references49

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              Cytoscape: a software environment for integrated models of biomolecular interaction networks.

              Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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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
                05 July 2022
                24 May 2022
                24 May 2022
                : 50
                : W1
                : W551-W559
                Affiliations
                Department of Mechanical Engineering, School of Engineering, Cardiff University , Cardiff, UK
                Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València , Valencia, Spain
                Department of Biotechnology and Systems Biology, National Institute of Biology , Ljubljana, Slovenia
                Biobam Bioinformatics , Valencia, Spain
                Diabetes Institute, University of Florida , Gainesville, USA
                Department of Biotechnology and Systems Biology, National Institute of Biology , Ljubljana, Slovenia
                Department of Molecular Genetics and Microbiology, Genetics Institute, University of Florida , Gainesville, USA
                Department of Biotechnology and Systems Biology, National Institute of Biology , Ljubljana, Slovenia
                Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València , Valencia, Spain
                Institute for Integrative Systems Biology, Spanish National Research Council (CSIC) , Paterna, Spain
                Department of Microbiology and Cell Science, University of Florida , Gainesville, FL, USA
                Author notes
                To whom correspondence should be addressed. Tel: +34 963544771; Email: ana.conesa@ 123456csic.es
                Author information
                https://orcid.org/0000-0003-3644-7827
                https://orcid.org/0000-0003-0913-2715
                https://orcid.org/0000-0001-9597-311X
                Article
                gkac352
                10.1093/nar/gkac352
                9252773
                35609982
                3d25f3e2-bf01-4d0a-9005-6340be77a324
                © The Author(s) 2022. 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 License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 April 2022
                : 22 April 2022
                : 12 March 2022
                Page count
                Pages: 9
                Funding
                Funded by: Spanish Ministry of Science and Innovation, DOI 10.13039/501100004837;
                Award ID: BIO2015-1658-R
                Award ID: PID2020-119537RB-100
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: R03 CA222444
                Funded by: Slovene Research Agency Program;
                Award ID: P4-0165
                Funded by: European Union’s Horizon 2020;
                Award ID: GA 2020 862-858
                Funded by: European Cooperation in Science and Technology, DOI 10.13039/501100000921;
                Funded by: Generalitat Valenciana, DOI 10.13039/501100003359;
                Award ID: ACIF2019/081
                Funded by: Spanish Ministry of Science and Innovation, DOI 10.13039/501100004837;
                Categories
                AcademicSubjects/SCI00010
                Web Server Issue

                Genetics
                Genetics

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