title: VENNTURE--a novel Venn diagram investigational tool for multiple pharmacological dataset analysis.
abstract: 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.
authors.affiliation: Metabolism Unit, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America.
authors.forename: Bronwen
authors.initials: B
authors.lastname: Martin
authors.forename: Wayne
authors.initials: W
authors.lastname: Chadwick
authors.forename: Tie
authors.initials: T
authors.lastname: Yi
authors.forename: Sung-Soo
authors.initials: SS
authors.lastname: Park
authors.forename: Daoyuan
authors.initials: D
authors.lastname: Lu
authors.forename: Bin
authors.initials: B
authors.lastname: Ni
authors.forename: Shekhar
authors.initials: S
authors.lastname: Gadkaree
authors.forename: Kathleen
authors.initials: K
authors.lastname: Farhang
authors.forename: Kevin G
authors.initials: KG
authors.lastname: Becker
authors.forename: Stuart
authors.initials: S
authors.lastname: Maudsley
mesh_terms: D045744:Cell Line, Tumor; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000706:statistics & numerical data*; D004305:Dose-Response Relationship, Drug; D006801:Humans; D008024:Ligands; D016210:Methacholine Chloride / Q000008:administration & dosage; D018721:Muscarinic Agonists / Q000008:administration & dosage; D010600:Pharmacology / Q000706:statistics & numerical data*; D010766:Phosphorylation; D040901:Proteomics / Q000706:statistics & numerical data; D043562:Receptors, G-Protein-Coupled / Q000187:drug effects / Q000378:metabolism; D011976:Receptors, Muscarinic / Q000187:drug effects / Q000378:metabolism; D015398:Signal Transduction / Q000187:drug effects; D012984:Software; D053719:Tandem Mass Spectrometry
chemical_list: D008024:Ligands; D018721:Muscarinic Agonists; D043562:Receptors, G-Protein-Coupled; D011976:Receptors, Muscarinic; D016210:Methacholine Chloride