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mDAG: A web tool for analyzing, visualizing, and interpreting response patterns in gene expression data with multiple treatments

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DOI: 10.4236/abb.2013.46093    2,964 Downloads   4,244 Views  

ABSTRACT

Background: We previously introduced a method based on post hoc pairwise comparisons to analyze gene expression responses. This method utilized directed graphs to represent gene response to all treatment pairs. It has been found useful in identifying structure-activity relationships among drugs and differentiating genes sharing similar functional pathways. Directed graphs are descriptive, visually expressive and can benefit subsequent functional analysis. Results: mDAG is a web-based software package based on this established method for the analysis, visualization, and interpretation of patterns of responses in gene expression data involving multiple treatments. Genes with the same directed graph patterns hypothetically share similar biological function, which may be further analyzed using external tools. To facilitate subsequent functional analysis, several well-known tools have been incorporated into mDAG to help users explore hypotheses about gene function and regulation. This tool is useful for any studies that analyze comparatively response patterns in gene expression data with multiple treatments (chemicals, cell types, etc.). Availability: The (server/personal/demo) software is freely available at
http://cetus.cs. memphis.edu/mdag.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Vo, N. , Sutter, T. and Phan, V. (2013) mDAG: A web tool for analyzing, visualizing, and interpreting response patterns in gene expression data with multiple treatments. Advances in Bioscience and Biotechnology, 4, 706-709. doi: 10.4236/abb.2013.46093.

References

[1] Fielden, M.R., Brennan, R. and Gollub, J. (2007) A gene expression biomarker provides early prediction and mechanistic assessment of hepatic tumor induction by nongenotoxic chemicals. Toxicological Sciences, 99, 90-100. doi:10.1093/toxsci/kfm156
[2] Natsoulis, G., Pearson, C.I., Gollub, J., Eynon, P., Ferng, J., Nair, R., Idury, R., Lee, M.D., Fielden, M.R., Brennan, R.J., Roter, A.H. and Jarnagin, K. (2008) The liver pharmacological and xenobiotic gene response repertoire. BMC Systems Biology, 4, 175.
[3] Sutter, T.R., He, X.R., Dimitrov, P., Xu, L., Narasimhan, G., George, E.O., Sutter, C.H., Grubbs, C., Savory, R., Stephan-Gueldner, M., Kreder, D., Taylor, M.J., Lubet, R., Patterson, T.A. and Kensler, T.W. (2002) Multiple comparisons model-based clustering and ternary pattern tree numerical display of gene response to treatment: procedure and application to the preclinical evaluation of chemopreventive agents. Molecular Cancer Therapeutics, 1, 1283-1292.
[4] Hulshizer, R. and Blalock, E.M. (2007) Post hoc pattern matching: Assigning significance to statistically defined expression patterns in single channel microarray data. BMC Bioinformatics, 8, 240. doi:10.1186/1471-2105-8-240
[5] Phan, V., Vo, N.S. and Sutter, T.R. (2013) Inferring directed-graph patterns of gene responses in gene-expression studies with multiple treatments. The 5th International Conference on Bioinformatics and Computational Biology (BICoB), 4-6 March 2013, Honolulu, 7-12.
[6] Phan, V., George, E.O., Tran, Q.T., Goodwin, S., Bodreddigari, S. and Sutter, T.R. (2009) Analyzing microarray data with transitive directed acyclic graphs. Journal of Bioinformatics and Computational Biology, 7, 135-156. doi:10.1142/S0219720009003972
[7] Huang, D.W., Sherman, B.T. and Lempicki, R.A. (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols, 4, 44-57.
[8] Warde-Farley, D., Donaldson, S.L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., Franz, M., Grouios, C., Kazi, F., Lopes, C.T., Maitland, A., Mostafavi, S., Montojo, J., Shao, Q., Wright, G., Bader, G.D. and Morris, Q. (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Research, 38, W214-W220. doi:10.1093/nar/gkq537
[9] Xu, L.J., Furlotte, N., Lin, Y.Y., Heinrich, K., Berry, M.W., George, E.O. and Homayouni, R. (2011) Functional cohesion of gene sets determined by latent semantic indexing of PubMed abstracts. PLoS ONE, 6, e18851. doi:10.1371/journal.pone.0018851
[10] Tran, Q.T., Xu, L., Phan, V., Goodwin, S., Rahman, M., Jin, V., Sutter, C.H., Roebuck, B., Kensler, T., George, E.O. and Sutter, T.R. (2009) Chemical genomics of cancer chemopreventive dithiolethiones. Carcinogenesis, 30, 480-486. doi:10.1093/carcin/bgn292

  
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