Blog
About

5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

          Abstract

          With the increasing obtainability of multi-OMICs data comes the need for easy to use data analysis tools. Here, the authors introduce Metascape, a biologist-oriented portal that provides a gene list annotation, enrichment and interactome resource and enables integrated analysis of multi-OMICs datasets.

          Related collections

          Most cited references 57

          • Record: found
          • Abstract: not found
          • Article: not found

          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

            DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              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.
                Bookmark

                Author and article information

                Contributors
                yzhou@gnf.org
                schanda@sbpdiscovery.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                3 April 2019
                3 April 2019
                2019
                : 10
                Affiliations
                [1 ]ISNI 0000 0004 0627 6737, GRID grid.418185.1, Genomics Institute of the Novartis Research Foundation, ; 10675 John Jay Hopkins Drive, San Diego, CA 92121 USA
                [2 ]ISNI 0000 0001 0163 8573, GRID grid.479509.6, Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, , Sanford Burnham Prebys Medical Discovery Institute, ; 10901 North Torrey Pines Road, La Jolla, CA 92037 USA
                [3 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, Department of Medicine, , University of California, San Diego, ; 9500 Gilman Drive, La Jolla, CA 92093 USA
                Article
                9234
                10.1038/s41467-019-09234-6
                6447622
                30944313
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized

                Comments

                Comment on this article