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Proactive visual and statistical analysis of genomic data in Epiviz
Zhe Cui1,2,3,4, Jayaram Kancherla2,3,5, Kyle W Chang5
1Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA.
Bioinformatics (Oxford, England)
|November 30, 2019
Summary
Epiviz Feed automates genomic data analysis by proactively identifying and presenting significant results. This visual analytics system enhances collaboration between analysts and computational environments, speeding up discovery.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Integrative analysis of genomic data combines statistical methods with visual exploration.
- Existing tools often separate visualization interfaces from computational analysis environments.
- Epiviz is an existing tool that integrates visualization with statistical analysis for genomic data.
Purpose of the Study:
- To present Epiviz Feed, a proactive visual analytics system integrated with Epiviz.
- To automate the execution of data analysis for testing biological hypotheses.
- To enhance the efficiency and collaboration in genomic data analysis.
Main Methods:
- Integration of a proactive and automatic visual analytics system (Epiviz Feed) with the Epiviz platform.
- Server-side computations to proactively identify results of interest.
- Presentation of results as notifications in a feed for user interaction.
Main Results:
- Epiviz Feed automates analysis, reducing manual effort for hypothesis testing.
- Results are summarized for easy understanding and investigation by analysts.
- The system enables filtering and grouping of analysis results for efficient searching.
Conclusions:
- Epiviz Feed advances genomic data analysis through proactive automation and enhanced user interaction.
- The system facilitates a collaborative workflow between analysts and computational environments.
- This work expands the integration of computational and visualization frameworks for interactive genomic analysis.
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