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

Author

Listed:
  • Yingyao Zhou

    (Genomics Institute of the Novartis Research Foundation)

  • Bin Zhou

    (Genomics Institute of the Novartis Research Foundation)

  • Lars Pache

    (Sanford Burnham Prebys Medical Discovery Institute)

  • Max Chang

    (University of California, San Diego)

  • Alireza Hadj Khodabakhshi

    (Genomics Institute of the Novartis Research Foundation)

  • Olga Tanaseichuk

    (Genomics Institute of the Novartis Research Foundation)

  • Christopher Benner

    (University of California, San Diego)

  • Sumit K. Chanda

    (Sanford Burnham Prebys Medical Discovery Institute)

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.

Suggested Citation

  • Yingyao Zhou & Bin Zhou & Lars Pache & Max Chang & Alireza Hadj Khodabakhshi & Olga Tanaseichuk & Christopher Benner & Sumit K. Chanda, 2019. "Metascape provides a biologist-oriented resource for the analysis of systems-level datasets," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09234-6
    DOI: 10.1038/s41467-019-09234-6
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