rHVDM: An R package to predict the activity and targets of a transcription factor

Barenco, M., Papouli, E., Shah, S., Brewer, D., Miller, C. J. and Hubank, M. (2009) rHVDM: An R package to predict the activity and targets of a transcription factor. Bioinformatics, 25 3: 419-420. doi:10.1093/bioinformatics/btn639


Author Barenco, M.
Papouli, E.
Shah, S.
Brewer, D.
Miller, C. J.
Hubank, M.
Title rHVDM: An R package to predict the activity and targets of a transcription factor
Journal name Bioinformatics   Check publisher's open access policy
ISSN 1367-4803
1367-4811
Publication date 2009-01-01
Year available 2009
Sub-type Article (original research)
DOI 10.1093/bioinformatics/btn639
Open Access Status Not Open Access
Volume 25
Issue 3
Start page 419
End page 420
Total pages 2
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Language eng
Abstract Highly parallel genomic platforms like microarrays often present researchers with long lists of differentially expressed genes but contain little or no information on how these genes are regulated. rHVDM is a novel R package which uses gene expression time course data to predict the activity and targets of a transcription factor. In the first step, rHVDM uses a small number of known targets to derive the activity profile of a given transcription factor. Then, in a subsequent step, this activity profile is used to predict other putative targets of that transcription factor. A dynamic and mechanistic model of gene expression is at the heart of the technique. Measurement error is taken into account during the process, which allows an objective assessment of the robustness of fit and, therefore, the quality of the predictions. The package relies on efficient algorithms and vectorization to accomplish potentially time consuming tasks including optimization and differential equation integration. We demonstrate the efficiency and accuracy of rHVDM by examining the activity of the tumour-suppressing transcription factor, p53.
Keyword Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Statistics & Probability
Biochemistry & Molecular Biology
Biotechnology & Applied Microbiology
Computer Science
Mathematical & Computational Biology
Mathematics
BIOCHEMICAL RESEARCH METHODS
BIOTECHNOLOGY & APPLIED MICROBIOLOGY
MATHEMATICAL & COMPUTATIONAL BIOLOGY
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID BB/E008488/1
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Brain Institute Publications
 
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