Abstract
Various methods of reconstructing transcriptional regulatory networks infer transcriptional regulatory interactions (TRIs) between strongly coexpressed gene pairs (as determined from microarray experiments measuring mRNA levels). Alternatively, however, the coexpression of two genes might imply that they are coregulated by one or more transcription factors (TFs), and do not necessarily share a direct regulatory interaction. We explore whether and under what circumstances gene pairs with a high degree of coexpression are more likely to indicate TRIs, coregulation or both. Here we use established TRIs in combination with microarray expression data from both Escherichia coli (a prokaryote) and Saccharomyces cerevisiae (a eukaryote) to assess the accuracy of predictions of coregulated gene pairs and TRIs from coexpressed gene pairs. We find that coexpressed gene pairs are more likely to indicate coregulation than TRIs for Saccharomyces cerevisiae, but the incidence of TRIs in highly coexpressed gene pairs is higher for Escherichia coli. The data processing inequality (DPI) has previously been applied for the inference of TRIs. We consider the case where a transcription factor gene is known to regulate two genes (one of which is a transcription factor gene) that are known not to regulate one another. According to the DPI, the non-interacting gene pairs should have the smallest mutual information among all pairs in the triplets. While this is sometimes the case for Escherichia coli, we find that it is almost always not the case for Saccharomyces cerevisiae. This brings into question the usefulness of the DPI sometimes employed to infer TRIs from expression data. Finally, we observe that when a TF gene is known to regulate two other genes, it is rarely the case that one regulatory interaction is positively correlated and the other interaction is negatively correlated. Typically both are either positively or negatively correlated.
Highlights
If two genes share a transcriptional regulatory interaction (TRI), one or both of them must be a transcription factor gene (TF gene) which can produce a protein called a transcription factor (TF) that regulates the mRNA expression of the other gene
We refer to the TF gene and the two genes that it regulates as a coregulation subgraph and we identify these subgraphs from the established TRI databases
Our study demonstrates that the performances of prediction of coregulated gene pairs and transcriptional regulatory interactions determined by coexpression levels are organism dependent
Summary
If two genes share a transcriptional regulatory interaction (TRI), one or both of them must be a transcription factor gene (TF gene) which can produce a protein called a transcription factor (TF) that regulates the mRNA expression of the other gene. The increased availability of high-throughput gene expression data has led to a variety of approaches for inferring TRIs [2,3,4,5,6]. More sophisticated methods of inferring TRIs integrate gene expression with other information, e.g. position weight matrices from sequence motif analysis, as in [7]. We study the use of gene expression alone in determining TRIs. In particular, we focus on the z-score metric used in the CLR algorithm (described in the Methods section). We focus on the z-score metric used in the CLR algorithm (described in the Methods section) This metric has been argued to give good performance in inferring
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