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Nat Commun. 2019 Apr 3;10(1):1523. doi: 10.1038/s41467-019-09234-6.

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

Author information

1
Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, CA, 92121, USA. yzhou@gnf.org.
2
Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, CA, 92121, USA.
3
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.
4
Department of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
5
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. schanda@sbpdiscovery.org.

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.

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