Abstract
BackgroundThe concept of reverse engineering a gene network, i.e., of inferring a genome-wide graph of putative gene-gene interactions from compendia of high throughput microarray data has been extensively used in the last few years to deduce/integrate/validate various types of “physical” networks of interactions among genes or gene products.ResultsThis paper gives a comprehensive overview of which of these networks emerge significantly when reverse engineering large collections of gene expression data for two model organisms, E.coli and S.cerevisiae, without any prior information. For the first organism the pattern of co-expression is shown to reflect in fine detail both the operonal structure of the DNA and the regulatory effects exerted by the gene products when co-participating in a protein complex. For the second organism we find that direct transcriptional control (e.g., transcription factor–binding site interactions) has little statistical significance in comparison to the other regulatory mechanisms (such as co-sharing a protein complex, co-localization on a metabolic pathway or compartment), which are however resolved at a lower level of detail than in E.coli.ConclusionThe gene co-expression patterns deduced from compendia of profiling experiments tend to unveil functional categories that are mainly associated to stable bindings rather than transient interactions. The inference power of this systematic analysis is substantially reduced when passing from E.coli to S.cerevisiae. This extensive analysis provides a way to describe the different complexity between the two organisms and discusses the critical limitations affecting this type of methodologies.
Highlights
Reverse engineering a gene network means extrapolating a graph of putative gene-gene interactions from high throughput microarray data
In order to take into account the homology and the architecture of the genomes, we considered maps of paralog genes (PAR) [15] and, for E.coli alone, a map of transcription units (TU) describing the operonal structure of the prokaryotic DNA (see Tables (a) and (b) of Fig. 1 and Supplementary Notes S1 for details and data sources)
We carry out two different tests to evaluate the performances of the algorithms. In the former the area under the receiving operating curve (AUC) is evaluated for each metric and network, see Fig. 1 (c), while in the second the edge weights resulting from the statistical analysis are rank-ordered and the percentages of ‘‘true’’ edges of each physical network in the top 1% of the inferred edges are shown in the histograms of Fig. 1 (d)
Summary
Reverse engineering a gene network means extrapolating a graph of putative gene-gene interactions from high throughput microarray data. As for gene profiling, we used three different compendia: one for E.coli and two for S.cerevisiae (one containing cDNA experiments, the other Affymetrix experiments) For this last organism, as a byproduct, the comparison of the two datasets allows the evaluation of the differences between the two gene profiling technologies (see in particular Fig. 1). The concept of reverse engineering a gene network, i.e., of inferring a genome-wide graph of putative genegene interactions from compendia of high throughput microarray data has been extensively used in the last few years to deduce/integrate/validate various types of ‘‘physical’’ networks of interactions among genes or gene products
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
