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Summary: Identification of Global Transcriptional Dynamics
Eric H. Yang1
, Richard R. Almon2,3,4
, Debra C. DuBois2,3
, Willian J. Jusko3,4
, Ioannis P. Androulakis1
*
1 Biomedical Engineering Department, Rutgers University, New Jersey, United States of America, 2 Department of Biological Sciences, State University of New York at
Buffalo, Buffalo, New York, United States of America, 3 Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York, United States of
America, 4 New York State Center of Excellence in Bioinformatics and Life Sciences, Buffalo, New York, United States of America
Abstract
Background: One of the challenges in exploiting high throughput measurement techniques such as microarrays is the
conversion of the vast amounts of data obtained into relevant knowledge. Of particular importance is the identification of
the intrinsic response of a transcriptional experiment and the characterization of the underlying dynamics.
Methodology and Findings: The proposed algorithm seeks to provide the researcher a summary as to various aspects relating
to the dynamic progression of a biological system, rather than that of individual genes. The approach is based on the
identification of smaller number of expression motifs that define the transcriptional state of the system which quantifies the
deviation of the cellular response from a control state in the presence of an external perturbation. The approach is
demonstrated with a number of data sets including a synthetic base case and four animal studies. The synthetic dataset will be
used to establish the response of the algorithm on a ``null'' dataset, whereas the four different experimental datasets represent
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