BMC Bioinformatics

, 12:A7 | Cite as

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

Open Access
Meeting abstract


Graphical Representation Pairwise Comparison Microarray Data Directed Graph Gene Response 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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


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. 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. 10.1142/S0219720009003972PubMedCentralCrossRefPubMedGoogle Scholar
  2. 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. 10.1093/carcin/bgn292PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Phan et al; licensee BioMed Central Ltd. 2011

This article is published under license to 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.

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of MemphisMemphisUSA
  2. 2.Bioinformatics ProgramUniversity of MemphisMemphisUSA
  3. 3.Department of Biological SciencesUniversity of MemphisMemphisUSA

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