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

BACKGROUND: 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...

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Detalles Bibliográficos
Autores principales: Dang, Tuan Nhon, Murray, Paul, Aurisano, Jillian, Forbes, Angus Graeme
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4547159/
https://www.ncbi.nlm.nih.gov/pubmed/26361502
http://dx.doi.org/10.1186/1753-6561-9-S6-S6
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author Dang, Tuan Nhon
Murray, Paul
Aurisano, Jillian
Forbes, Angus Graeme
author_facet Dang, Tuan Nhon
Murray, Paul
Aurisano, Jillian
Forbes, Angus Graeme
author_sort Dang, Tuan Nhon
collection PubMed
description BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.
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spelling pubmed-45471592015-09-10 ReactionFlow: an interactive visualization tool for causality analysis in biological pathways Dang, Tuan Nhon Murray, Paul Aurisano, Jillian Forbes, Angus Graeme BMC Proc Research BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. BioMed Central 2015-08-13 /pmc/articles/PMC4547159/ /pubmed/26361502 http://dx.doi.org/10.1186/1753-6561-9-S6-S6 Text en Copyright © 2015 Dang et al. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Dang, Tuan Nhon
Murray, Paul
Aurisano, Jillian
Forbes, Angus Graeme
ReactionFlow: an interactive visualization tool for causality analysis in biological pathways
title ReactionFlow: an interactive visualization tool for causality analysis in biological pathways
title_full ReactionFlow: an interactive visualization tool for causality analysis in biological pathways
title_fullStr ReactionFlow: an interactive visualization tool for causality analysis in biological pathways
title_full_unstemmed ReactionFlow: an interactive visualization tool for causality analysis in biological pathways
title_short ReactionFlow: an interactive visualization tool for causality analysis in biological pathways
title_sort reactionflow: an interactive visualization tool for causality analysis in biological pathways
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4547159/
https://www.ncbi.nlm.nih.gov/pubmed/26361502
http://dx.doi.org/10.1186/1753-6561-9-S6-S6
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