Meeting abstract
Open Access
Volume 12 Supplement 7
UT-ORNL-KBRIN Bioinformatics Summit 2011
mDAG: a web-based tool for analyzing microarray data with multiple treatments
- Vinhthuy T Phan1Email author,
- Nam S Vo2 and
- Thomas R Sutter3
BMC Bioinformatics201112(Suppl 7):A7
https://doi.org/10.1186/1471-2105-12-S7-A7
© Phan et al; licensee BioMed Central Ltd. 2011
Published: 5 August 2011
Background
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].
Results
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 http://cetus.cs.memphis.edu:8080/mDAG.
Conclusion
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.
Declarations
Acknowledgements
This software is supported by the Center for Alternatives to Animal Testing at the John Hopkins school of Public Heath, Project CAAT-2011-18.
Authors’ Affiliations
(1)
Department of Computer Science, University of Memphis
(2)
Bioinformatics Program, University of Memphis
(3)
Department of Biological Sciences, University of Memphis
References
- 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/S0219720009003972PubMed CentralView ArticlePubMedGoogle Scholar
- 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/bgn292PubMed CentralView ArticlePubMedGoogle Scholar
Copyright
© 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 (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
