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ReactionFlow: an interactive visualization tool for causality analysis in biological pathways

Overview of attention for article published in BMC Proceedings, August 2015
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Title
ReactionFlow: an interactive visualization tool for causality analysis in biological pathways
Published in
BMC Proceedings, August 2015
DOI 10.1186/1753-6561-9-s6-s6
Pubmed ID
Authors

Tuan Nhon Dang, Paul Murray, Jillian Aurisano, Angus Graeme Forbes

Abstract

Molecular and systems biologists are tasked with the comprehension and analysis of incredibly complex networks of biochemical interactions, called pathways, that occur within a cell. Through interviews with domain experts, we identified four common tasks that require an understanding of the causality within pathways, that is, the downstream and upstream relationships between proteins and biochemical reactions, including: visualizing downstream consequences of perturbing a protein; finding the shortest path between two proteins; detecting feedback loops within the pathway; and identifying common downstream elements from two or more proteins. We introduce ReactionFlow, a visual analytics application for pathway analysis that emphasizes the structural and causal relationships amongst proteins, complexes, and biochemical reactions within a given pathway. To support the identified causality analysis tasks, user interactions allow an analyst to filter, cluster, and select pathway components across linked views. Animation is used to highlight the flow of activity through a pathway. We evaluated ReactionFlow by providing our application to two domain experts who have significant experience with biomolecular pathways, after which we conducted a series of in-depth interviews focused on each of the four causality analysis tasks. Their feedback leads us to believe that our techniques could be useful to researchers who must be able to understand and analyze the complex nature of biological pathways. ReactionFlow is available at https://github.com/CreativeCodingLab/ReactionFlow.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belgium 1 4%
Unknown 27 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 36%
Researcher 3 11%
Student > Master 3 11%
Student > Bachelor 1 4%
Professor 1 4%
Other 4 14%
Unknown 6 21%
Readers by discipline Count As %
Computer Science 12 43%
Agricultural and Biological Sciences 3 11%
Biochemistry, Genetics and Molecular Biology 1 4%
Business, Management and Accounting 1 4%
Unspecified 1 4%
Other 3 11%
Unknown 7 25%