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FirebrowseR: an R client to the Broad Institute's Firehose Pipeline
Mario Deng1, Johannes Brägelmann2, Ivan Kryukov3
1Pathology of the University Medical Center Schleswig-Holstein, Campus Luebeck and the Research Center Borstel, Leibniz Center for Medicine and Biosciences, 23538 Luebeck and 23845 Borstel, Germany.
Database : the Journal of Biological Databases and Curation
|January 8, 2017
Summary
Researchers developed a workflow to automatically generate API clients for omics data. This enables easier access to Broad Institute
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- The Broad Institute provides large-scale multi-platform omics data analysis results via the Firebrowse REST API.
- Accessing and integrating this data programmatically is crucial for researchers but can involve significant data wrangling.
- Developing robust API clients is essential for efficient data access and analysis.
Purpose of the Study:
- To develop a workflow for the automated generation, testing, and deployment of API clients for omics data.
- To create an R package (FirebrowseR) as a practical example of the developed workflow.
- To facilitate easier programmatic access to the Broad Institute's Firehose Pipeline data for cancer research.
Main Methods:
- Development of an automated workflow utilizing publicly available web services for API client generation.
- Implementation of a system that decouples server and client software by reacting to RESTful service changes.
- Creation of FirebrowseR, an R client package, built using the automated workflow.
- Demonstration of the R package's functionality through an analysis of cancer gene expression data.
Main Results:
- A workflow for automated API client generation, testing, and deployment was successfully developed.
- FirebrowseR, an R client for the Firehose Pipeline, was created as a working example of the workflow.
- The workflow effectively decouples client and server software, enhancing adaptability to API changes.
- The R package facilitates analysis of cancer gene expression data, showcasing its utility.
Conclusions:
- The automated workflow streamlines the development of API clients for large-scale omics data.
- FirebrowseR provides a user-friendly R interface for accessing and analyzing Firehose Pipeline data.
- This approach enhances data accessibility and supports reproducible research in genomics and cancer studies.
