title: DIVE: a data intensive visualization engine.
abstract: CONCLUSIONS
  Modern scientific investigation is generating increasingly larger datasets, yet analyzing these data with current tools is challenging. DIVE is a software framework intended to facilitate big data analysis and reduce the time to scientific insight. Here, we present features of the framework and demonstrate DIVE's application to the Dynameomics project, looking specifically at two proteins.
  
  
  METHODS
  Binaries and documentation are available at http://www.dynameomics.org/DIVE/DIVESetup.exe.
authors: Division of Biomedical and Health Informatics, University of Washington Medical School and Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.
authors: Dennis
authors: D
authors: Bromley
authors: Steven J
authors: SJ
authors: Rysavy
authors: Robert
authors: R
authors: Su
authors: Rudesh D
authors: RD
authors: Toofanny
authors: Tom
authors: T
authors: Schmidlin
authors: Valerie
authors: V
authors: Daggett
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003198:Computer Simulation; D004282:Documentation / Q000379:methods*; D006801:Humans; D050505:Mutant Proteins / Q000235:genetics / Q000378:metabolism*; D009154:Mutation / Q000235:genetics; D012984:Software; D013482:Superoxide Dismutase / Q000235:genetics / Q000378:metabolism; D000072105:Superoxide Dismutase-1; D016159:Tumor Suppressor Protein p53 / Q000235:genetics / Q000378:metabolism
chemical_list: D050505:Mutant Proteins; C000606290:SOD1 protein, human; C495901:TP53 protein, human; D016159:Tumor Suppressor Protein p53; D013482:Superoxide Dismutase; D000072105:Superoxide Dismutase-1