VENNTURE--a novel Venn diagram investigational tool for multiple pharmacological dataset analysis

• Published in: PloS one 2012-05, Vol.7 (5), p.e36911
• Main Authors: Martin, Bronwen, Chadwick, Wayne, Yi, Tie, Park, Sung-Soo, Lu, Daoyuan, Ni, Bin, Gadkaree, Shekhar, Farhang, Kathleen, Becker, Kevin G, Maudsley, Stuart
• Format: Article
• Language: English
• Subjects: Aging; Bioinformatics; Biology; Cell Line, Tumor; Computational Biology; Computer programs; Computer Science; Data analysis; Data Interpretation, Statistical; Data points; Data processing; Data transmission; Databases, Factual - statistics & numerical data; Datasets; Diabetes; Dose-Response Relationship, Drug; Drug dosages; Engineering; Filtration; Flavonoids; G proteins; Gene expression; Genomics; Growth factors; Humans; Kinases; Laboratories; Ligands; Membrane proteins; Metabolism; Methacholine Chloride - administration & dosage; Muscarinic Agonists - administration & dosage; Neuroblastoma; Neurosciences; Peptides; Pharmacology; Pharmacology - statistics & numerical data; Phosphorylation; Physiology; Proteins; Proteomics; Proteomics - statistics & numerical data; Receptors, G-Protein-Coupled - drug effects; Receptors, G-Protein-Coupled - metabolism; Receptors, Muscarinic - drug effects; Receptors, Muscarinic - metabolism; Signal Transduction - drug effects; Software; Spreadsheet software; Tandem Mass Spectrometry; Venn diagrams
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0036911

As pharmacological data sets become increasingly large and complex, new visual analysis and filtering programs are needed to aid their appreciation. One of the most commonly used methods for visualizing biological data is the Venn diagram. Currently used Venn analysis software often presents multiple problems to biological scientists, in that only a limited number of simultaneous data sets can be analyzed. An improved appreciation of the connectivity between multiple, highly-complex datasets is crucial for the next generation of data analysis of genomic and proteomic data streams. We describe the development of VENNTURE, a program that facilitates visualization of up to six datasets in a user-friendly manner. This program includes versatile output features, where grouped data points can be easily exported into a spreadsheet. To demonstrate its unique experimental utility we applied VENNTURE to a highly complex parallel paradigm, i.e. comparison of multiple G protein-coupled receptor drug dose phosphoproteomic data, in multiple cellular physiological contexts. VENNTURE was able to reliably and simply dissect six complex data sets into easily identifiable groups for straightforward analysis and data output. Applied to complex pharmacological datasets, VENNTURE's improved features and ease of analysis are much improved over currently available Venn diagram programs. VENNTURE enabled the delineation of highly complex patterns of dose-dependent G protein-coupled receptor activity and its dependence on physiological cellular contexts. This study highlights the potential for such a program in fields such as pharmacology, genomics, and bioinformatics.