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This article is part of the supplement: UT-ORNL-KBRIN Bioinformatics Summit 2011

Open Access Meeting abstract

mDAG: a web-based tool for analyzing microarray data with multiple treatments

Vinhthuy T Phan1*, Nam S Vo2 and Thomas R Sutter3

Author Affiliations

1 Department of Computer Science, University of Memphis, Memphis, TN, 38152, USA

2 Bioinformatics Program, University of Memphis, Memphis, TN, 38152, USA

3 Department of Biological Sciences, University of Memphis, Memphis, TN, 38152, USA

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BMC Bioinformatics 2011, 12(Suppl 7):A7  doi:10.1186/1471-2105-12-S7-A7

The electronic version of this article is the complete one and can be found online at:

Published:5 August 2011

© 2011 Phan et al; licensee BioMed Central Ltd.

This is an open access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In microarray experiments involving multiple treatments, pairwise comparisons between all pairs of treatments are desirable but expensive. To cope with this, we previously introduced a method that performed all pairwise comparisons in a post hoc manner. This method employs directed graphs to represent gene response to pairs of treatments. It has been applied and found useful in identifying and differentiating genes sharing similar functional pathways [1,2].


mDAG is a web-based software based on this method. mDAG allows users to upload microarray data in GCT format through a web interface. From this data, the application performs calculations to assign graphical patterns to genes and outputs images and textual data for further analyses. These graphical patterns carry specific meanings in terms of how genes respond to pairs of treatments. The application is implemented using Python and web2py.

mDAG is available at webcite.


For experiments involved multiple treatments and replicates, mDAG allows researchers to analyze and visualize in graphical representations relationships of gene interactions to all pairs of treatments. The software can be used online or off-line.


This software is supported by the Center for Alternatives to Animal Testing at the John Hopkins school of Public Heath, Project CAAT-2011-18.


  1. Phan V, George EO, Tran QT, Goodwin S, Boddreddigari S, Sutter TR: Analyzing microarray data with transitive directed acyclic graphs.

    Journal of Bioinformatics and Computational Biology 2009, 7(1):135-156. Publisher Full Text OpenURL

  2. Tran QT, Lijing X, Phan V, Goodwin S, Rahman M, Jin V, Sutter CH, Roebuck B, Kensler T, George EO, Sutter TR: Chemical genomics of cancer chemopreventive dithiolethiones.

    Carcinogenesis 2009, 30(3):480-486. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL