Reference : PhenoTimer: Software for the Visual Mapping of Time-Resolved Phenotypic Landscapes

	Document type :	Scientific journals : Article
	Discipline(s) :	Life sciences : Multidisciplinary, general & others
	To cite this reference:	http://hdl.handle.net/10993/26549

Title :	PhenoTimer: Software for the Visual Mapping of Time-Resolved Phenotypic Landscapes
Language :	English
Author, co-author :	Secrier, Maria [European Molecular Biology Laboratory (EMBL > Structural and Computational Biology Unit]
	Schneider, Reinhard [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Publication date :	2013
Journal title :	PLoS ONE
Publisher :	Public Library of Science
Volume :	8
Issue/season :	8
Pages :	e72361
Peer reviewed :	Yes (verified by ORBilu)
e-ISSN :	1932-6203
City :	San Franscisco
Country :	CA
Abstract :	[en] Timing common and specific modulators of disease progression is crucial for treatment, but the understanding of the underlying complex system of interactions is limited. While attempts at elucidating this experimentally have produced enormous amounts of phenotypic data, tools that are able to visualize and analyze them are scarce and the insight obtained from the data is often unsatisfactory. Linking and visualizing processes from genes to phenotypes and back, in a temporal context, remains a challenge in systems biology. We introduce PhenoTimer, a 2D/3D visualization tool for the mapping of time-resolved phenotypic links in a genetic context. It uses a novel visualization approach for relations between morphological defects, pathways or diseases, to enable fast pattern discovery and hypothesis generation. We illustrate its capabilities of tracing dynamic motifs on cell cycle datasets that explore the phenotypic order of events upon perturbations of the system, transcriptional activity programs and their connection to disease. By using this tool we are able to fine-grain regulatory programs for individual time points of the cell cycle and better understand which patterns arise when these programs fail. We also illustrate a way to identify common mechanisms of misregulation in diseases and drug abuse.
Research centres :	Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group)
Funders :	TAMAHUD (LSHC-CT-2007-037472), FORSYS-ViroQuant, EMBL PhD Program
Permalink :	http://hdl.handle.net/10993/26549
DOI :	10.1371/journal.pone.0072361

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