ReactomeGSA: new features to simplify public data reuse

Author:

Grentner Alexander1ORCID,Ragueneau Eliot2ORCID,Gong Chuqiao2,Prinz Adrian2,Gansberger Sabina1ORCID,Oyarzun Inigo1ORCID,Hermjakob Henning2ORCID,Griss Johannes12ORCID

Affiliation:

1. Department of Dermatology, Medical University of Vienna , Vienna 1090, Austria

2. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK

Abstract

Abstract Motivation ReactomeGSA is part of the Reactome knowledgebase and one of the leading multi-omics pathway analysis platforms. ReactomeGSA provides access to quantitative pathway analysis methods supporting different ‘omics data types. Additionally, ReactomeGSA can process different datasets simultaneously, leading to a comparative pathway analysis that can also be performed across different species. Results We present a major update to the ReactomeGSA analysis platforms that greatly simplifies the reuse and direct integration of public data. In order to increase the number of available datasets, we developed the new grein_loader Python application that can directly fetch experiments from the GREIN resource. This enabled us to support both EMBL-EBI’s Expression Atlas and GEO RNA-seq Experiments Interactive Navigator within ReactomeGSA. To further increase the visibility and simplify the reuse of public datasets, we integrated a novel search function into ReactomeGSA that enables users to search for public datasets across both supported resources. Finally, we completely re-developed ReactomeGSA’s web-frontend and R/Bioconductor package to support the new search and loading features, and greatly simplify the use of ReactomeGSA. Availability and implementation The new ReactomeGSA web frontend is available at https://www.reactome.org/gsa with an built-in, interactive tutorial. The ReactomeGSA R package (https://bioconductor.org/packages/release/bioc/html/ReactomeGSA.html) is available through Bioconductor and shipped with detailed documentation and vignettes. The grein_loader Python application is available through the Python Package Index (pypi). The complete source code for all applications is available on GitHub at https://github.com/grisslab/grein_loader and https://github.com/reactome.

Funder

Austrian Science Fund

National Institutes of Health

European Bioinformatics Institute

Publisher

Oxford University Press (OUP)

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