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Open Access Research

Determination of minimal transcriptional signatures of compounds for target prediction

Florian Nigsch1*, Janna Hutz2, Ben Cornett2, Douglas W Selinger2, Gregory McAllister2, Somnath Bandyopadhyay3, Joseph Loureiro2 and Jeremy L Jenkins2

Author Affiliations

1 Developmental and Molecular Pathways, Novartis Institutes for BioMedical Research, Forum 1, Novartis Campus Basel, CH-4056, Basel, Switzerland

2 Developmental and Molecular Pathways, Novartis Institutes for BioMedical Research, 220 Massachusetts Avenue, 02139 Cambridge, MA, USA

3 Immunology Clinical Biomarkers, Bristol Myers Squibb, Princeton, New Jersey

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EURASIP Journal on Bioinformatics and Systems Biology 2012, 2012:2  doi:10.1186/1687-4153-2012-2

Published: 10 May 2012

Abstract

The identification of molecular target and mechanism of action of compounds is a key hurdle in drug discovery. Multiplexed techniques for bead-based expression profiling allow the measurement of transcriptional signatures of compound-treated cells in high-throughput mode. Such profiles can be used to gain insight into compounds' mode of action and the protein targets they are modulating. Through the proxy of target prediction from such gene signatures we explored important aspects of the use of transcriptional profiles to capture biological variability of perturbed cellular assays. We found that signatures derived from expression data and signatures derived from biological interaction networks performed equally well, and we showed that gene signatures can be optimised using a genetic algorithm. Gene signatures of approximately 128 genes seemed to be most generic, capturing a maximum of the perturbation inflicted on cells through compound treatment. Moreover, we found evidence for oxidative phosphorylation to be one of the most general ways to capture compound perturbation.

Keywords:
transcriptional profiling; target prediction; genetic algorithm; graphics processing unit (GPU) programming; compute unified device architecture (CUDA)