Title
VennPlex : a novel Venn diagram program for comparing and visualizing datasets with differentially regulated datapoints VennPlex : a novel Venn diagram program for comparing and visualizing datasets with differentially regulated datapoints
Author
Faculty/Department
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
article
Publication
Subject
Engineering sciences. Technology
Source (journal)
PLoS ONE
Volume/pages
8(2013) :1 , 6 p.
ISSN
1932-6203
1932-6203
Article Reference
e53388
Carrier
E-only publicatie
Target language
English (eng)
Full text (Publishers DOI)
Abstract
With the development of increasingly large and complex genomic and proteomic data sets, an enhancement in the complexity of available Venn diagram analytical programs is becoming increasingly important. Current freely available Venn diagram programs often fail to represent extra complexity among datasets, such as regulation pattern differences between different groups. Here we describe the development of VennPlex, a program that illustrates the often diverse numerical interactions among multiple, high-complexity datasets, using up to four data sets. VennPlex includes versatile output features, where grouped data points in specific regions can be easily exported into a spreadsheet. This program is able to facilitate the analysis of two to four gene sets and their corresponding expression values in a user-friendly manner. To demonstrate its unique experimental utility we applied VennPlex to a complex paradigm, i.e. a comparison of the effect of multiple oxygen tension environments (1-20% ambient oxygen) upon gene transcription of primary rat astrocytes. VennPlex accurately dissects complex data sets reliably into easily identifiable groups for straightforward analysis and data output. This program, which is an improvement over currently available Venn diagram programs, is able to rapidly extract important datasets that represent the variety of expression patterns available within the data sets, showing potential applications in fields like genomics, proteomics, and bioinformatics.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/e14b0f/8898.pdf
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