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Knowledge Formalization and High-Throughput Data Visualization Using Signaling Network Maps

Maria Kondratova, Emmanuel Barillot, Andrei Zinovyev, Inna Kuperstein
doi: https://doi.org/10.1101/089409
Maria Kondratova
1Institut Curie, France
2INSERM, U900, France
3Mines Paris Tech, France
4PSL Research University
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Emmanuel Barillot
1Institut Curie, France
2INSERM, U900, France
3Mines Paris Tech, France
4PSL Research University
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Andrei Zinovyev
1Institut Curie, France
2INSERM, U900, France
3Mines Paris Tech, France
4PSL Research University
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Inna Kuperstein
1Institut Curie, France
2INSERM, U900, France
3Mines Paris Tech, France
4PSL Research University
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  • For correspondence: inna.kuperstein@curie.fr
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ABSTRACT

Graphical representation of molecular biology knowledge in the form of interactive diagrams became widely used in molecular and computational biology. It enables the scientific community to exchange and discuss information on cellular processes described in numerous scientific publications and to interpret high-throughput data. Constructing a signaling network map is a laborious process, therefore application of consistent procedures for representation of molecular processes and accurately organized annotation is essential for generation of a high-quality signaling network map that can be used by various computational tools. We summarize here the major aims and challenges of assembling information in a form of comprehensive maps of molecular interactions and suggest an optimized workflow. We share our experience gained while creating a biological network resource Atlas of Cancer Signaling Network (ACSN) that was successfully applied in several studies. We explain the map construction process. Then we address the problem of user interaction with large signaling maps and suggest to facilitate navigation by hierarchical organization of map structure and by application of semantic zooming principles. In addition, we describe a computational technology using Google Maps API to explore signaling networks in the manner similar to global geographical maps and provide the outline for preparing a biological network for this type of navigation. Nowadays the most demanded application of signaling maps is integration and functional interpretation of high-throughput data. We demonstrate several examples of cancer data visualization in the context of comprehensive signaling network maps.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted November 24, 2016.
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Knowledge Formalization and High-Throughput Data Visualization Using Signaling Network Maps
Maria Kondratova, Emmanuel Barillot, Andrei Zinovyev, Inna Kuperstein
bioRxiv 089409; doi: https://doi.org/10.1101/089409
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Knowledge Formalization and High-Throughput Data Visualization Using Signaling Network Maps
Maria Kondratova, Emmanuel Barillot, Andrei Zinovyev, Inna Kuperstein
bioRxiv 089409; doi: https://doi.org/10.1101/089409

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