A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.

While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi-supervised REgulatory Network Discoverer), a semi-supervised learning method that uses a curated database of verified transcriptional factor-gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor-gene interactions, including whether the transcription factor activates or represses the gene. Using genome-wide binding datasets for several transcription factors, we demonstrate that our semi-supervised classification strategy improves the prediction of targets for a given transcription factor. To further demonstrate the utility of our inferred interactions, we generated a new microarray gene expression dataset for the aerobic to anaerobic shift response in E. coli. We used our inferred interactions with the verified interactions to reconstruct a dynamic regulatory network for this response. The network reconstructed when using our inferred interactions was better able to correctly identify known regulators and suggested additional activators and repressors as having important roles during the aerobic-anaerobic shift interface.

Genome-scale identification of conditionally essential genes in E. coli by DNA microarrays.

Identifying the genes required for the growth or viability of an organism under a given condition is an important step toward understanding the roles these genes play in the physiology of the organism. Currently, the combination of global transposon mutagenesis with PCR-based mapping of transposon insertion sites is the most common method for determining conditional gene essentiality. In order to accelerate the detection of essential gene products, here we test the utility and reliability of a DNA microarray technology-based method for the identification of conditionally essential genes of the bacterium, Escherichia coli, grown in rich medium under aerobic or anaerobic growth conditions using two different DNA microarray platforms. Identification and experimental verification of five hypothetical E. coli genes essential for anaerobic growth directly demonstrated the utility of the method. However, the two different DNA microarray platforms yielded largely non-overlapping results after a two standard deviations cutoff and were subjected to high false positive background levels. Thus, further methodological improvements are needed prior to the use of DNA microarrays to reliably identify conditionally essential genes on genome-scale.

Spurious spatial periodicity of co-expression in microarray data due to printing design.

Global transcriptome data is increasingly combined with sophisticated mathematical analyses to extract information about the functional state of a cell. Yet the extent to which the results reflect experimental bias at the expense of true biological information remains largely unknown. Here we show that the spatial arrangement of probes on microarrays and the particulars of the printing procedure significantly affect the log-ratio data of mRNA expression levels measured during the Saccharomyces cerevisiae cell cycle. We present a numerical method that filters out these technology-derived contributions from the existing transcriptome data, leading to improved functional predictions. The example presented here underlines the need to routinely search and compensate for inherent experimental bias when analyzing systematically collected, internally consistent biological data sets.