title: Integrated web visualizations for protein-protein interaction databases.
abstract: BACKGROUND
  Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks.
  
  
  RESULTS
  We selected M=10 out of N=53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015.
  
  
  CONCLUSIONS
  Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.
authors.affiliation: Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz, 8036, Austria. f.jeanquartier@hci-kdd.org.
authors.forename: Fleur
authors.initials: F
authors.lastname: Jeanquartier
authors.affiliation: Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz, 8036, Austria. c.jeanquartier@hci-kdd.org.
authors.forename: Claire
authors.initials: C
authors.lastname: Jean-Quartier
authors.affiliation: Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz, 8036, Austria. a.holzinger@tugraz.at.|Institute for Information Systems & Computer Media Graz University of Technology, Inffeldgasse 16c, Graz, 8010, Austria. a.holzinger@tugraz.at.
authors.forename: Andreas
authors.initials: A
authors.lastname: Holzinger
mesh_terms: D019295:Computational Biology / Q000379:methods; D003196:Computer Graphics; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D020407:Internet; D060066:Protein Interaction Maps; D011506:Proteins / Q000378:metabolism*; D012984:Software; D049490:Systems Biology; D014584:User-Computer Interface
chemical_list: D011506:Proteins