Florian Markowetz1, Jacques Bloch, Rainer Spang. 1. Department of Computational Molecular Biology, Computational Diagnostics Group, Max Planck Institute for Molecular Genetics Ihnestrasse 63-73, 14195 Berlin, Germany. florian.markowetz@molgen.mpg.de
Abstract
MOTIVATION: Cellular signaling pathways, which are not modulated on a transcriptional level, cannot be directly deduced from expression profiling experiments. The situation changes, when external interventions such as RNA interference or gene knock-outs come into play. Even if the expression of the signaling genes is not changed, secondary effects in downstream genes shed light on the pathway, and allow partial reconstruction of its topology. RESULTS: We introduce an algorithm to infer non-transcriptional pathway features based on differential gene expression in silencing assays. We demonstrate the power of our algorithm in the controlled setting of simulation studies, and explain its practical use in the context of an RNA interference dataset investigating the response to microbial challenge in Drosophila melanogaster.
MOTIVATION: Cellular signaling pathways, which are not modulated on a transcriptional level, cannot be directly deduced from expression profiling experiments. The situation changes, when external interventions such as RNA interference or gene knock-outs come into play. Even if the expression of the signaling genes is not changed, secondary effects in downstream genes shed light on the pathway, and allow partial reconstruction of its topology. RESULTS: We introduce an algorithm to infer non-transcriptional pathway features based on differential gene expression in silencing assays. We demonstrate the power of our algorithm in the controlled setting of simulation studies, and explain its practical use in the context of an RNA interference dataset investigating the response to microbial challenge in Drosophila melanogaster.
Authors: Debashis Sahoo; David L Dill; Andrew J Gentles; Robert Tibshirani; Sylvia K Plevritis Journal: Genome Biol Date: 2008-10-30 Impact factor: 13.583