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VennMaster

A tool for drawing area proportional Euler diagrams.
 
VennMaster is a tool for drawing area proportional Venn/Euler-diagrams. It supports several input formats from simple tab seperated data to gene lists from GoMiner.
 
VennMaster will generate area proportional Venn diagrams for multiple sets. Since exact solutions seldomly exist for more than 3 sets, it will approximate a correct solution. VennMaster is great for assessing overlapping data at a glance, as well as for in depth analysis.
 

Please also see our corresponding publications:

Kestler HA, Müller A, Gress TM, Buchholz M. Generalized Venn Diagrams: A new method of visualizing complex genetic set relationsBioinformatics 21(8): 1592-1595, 2005.
 
Kestler HA, Müller A, Kraus JM, Buchholz M, Gress TM, Liu H, Kane DW, Zeeberg BR, Weinstein JN. VennMaster: Area-proportional Euler diagrams for functional GO analysis of microarraysBMC Bioinform 9(1): 67, 2008. https://doi.org/10.1186/1471-2105-9-67

 

Quick Download latest (0.38.2) version
 
Documentation:
1. Installation
2. Running VennMaster
3. Appendix

Latest News

 

Congratulations to Dr. Silke Werle for winning the 1st Prize with her pitch at the 1. Science Day held by ProTrainU. 

 

Our paper "Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells" has been published in the Computational and Structural Biotechnology Journal.

 

The position paper "Is there a role for statistics in artificial intelligence" has been published online first in Advances in Data Analysis and Classification.

 

Our paper "Corona Health - A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic" has been published in the International Journal of Environmental Research and Public Health.

 

Our paper "Patient empowerment during the COVID-19 pandemic: Ensuring safe and fast communication of test results" has been published in the Journal of Medical Internet Research.

 

Our paper "Perspective on mHealth Concepts to Ensure Users’ Empowerment–From Adverse Event Tracking for COVID-19 Vaccinations to Oncological Treatment" has been published in IEEE Access.

  

Our report protocol "Digitalization of adverse event management in oncology to improve treatment outcome—A prospective study protocol" has been published in PLoS One.

 

We are happy we could contribute to Beutel et al (2021) "A prospective Feasibility Trial to Challenge Patient-Derived Pancreatic Cancer Organoids in Predicting Treatment Response" published in MDPI Cancers.