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Bioinformatics Advance Access originally published online on December 6, 2005
Bioinformatics 2006 22(4):477-484; doi:10.1093/bioinformatics/bti816
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Least absolute regression network analysis of the murine osteoblast differentiation network

E. P. van Someren 1,*, B. L. T. Vaes 2, W. T. Steegenga 2,{dagger}, A. M. Sijbers 3, K. J. Dechering 3 and M. J. T. Reinders 1

1Department of Mediametics, Delft University of Technology 2600 GA Delft, The Netherlands
2Department of Applied Biology, University of Nijmegen Nijmegen, The Netherlands
3N.V.Organon, Target Discovery Unit Oss, The Netherlands

*To whom correspondence should be addressed.

Motivation: We propose a reverse engineering scheme to discover genetic regulation from genome-wide transcription data that monitors the dynamic transcriptional response after a change in cellular environment. The interaction network is estimated by solving a linear model using simultaneous shrinking of the least absolute weights and the prediction error.

Results: The proposed scheme has been applied to the murine C2C12 cell-line stimulated to undergo osteoblast differentiation. Results show that our method discovers genetic interactions that display significant enrichment of co-citation in literature. More detailed study showed that the inferred network exhibits properties and hypotheses that are consistent with current biological knowledge.

Availability: Software is freely available for academic use as a Matlab package called GENLAB: http://genlab.tudelft.nl/genlab.html

Contact: E.P.vanSomeren{at}tudelft.nl

Supplementary information: Additional data, results and figures can be found at http://genlab.tudelft.nl/larna.html

Received on November 1, 2005; revised on November 16, 2005; accepted on December 1, 2005

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