Correlation network analysis for data integration and biomarker selection.

High-throughput biomolecular profiling techniques such as transcriptomics, proteomics and metabolomics are increasingly being used in in vivo studies to recognize and characterize effects of xenobiotics on organs and systems. Of particular interest are biomarkers of treatment-related effects which are detectable in easily accessible biological fluids such as blood. A fundamental challenge in such biomarker studies is selecting among the plethora of biomolecular changes induced by a compound and revealed by molecular profiling, to identify biomarkers which are exclusively or predominantly due to specific processes.

Transcriptional regulatory code of a eukaryotic genome.

DNA-binding transcriptional regulators interpret the genome's regulatory code by binding to specific sequences to induce or repress gene expression. Comparative genomics has recently been used to identify potential cis-regulatory sequences within the yeast genome on the basis of phylogenetic conservation, but this information alone does not reveal if or when transcriptional regulators occupy these binding sites. We have constructed an initial map of yeast's transcriptional regulatory code by identifying the sequence elements that are bound by regulators under various conditions and that are conserved among Saccharomyces species.

Systematic analysis of essential yeast TAFs in genome-wide transcription and preinitiation complex assembly.

The general transcription factor TFIID is composed of the TATA box binding protein (TBP) and a set of conserved TBP-associated factors (TAFs). Here we report the completion of genome-wide expression profiling analyses of yeast strains bearing temperature-sensitive mutations in each of the 13 essential TAFs. The percentage of the yeast genome dependent on each TAF ranges from 3% (TAF2) to 59-61% (TAF9).

Transcriptional regulatory networks in Saccharomyces cerevisiae.

We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure.

Human macrophage activation programs induced by bacterial pathogens.

Understanding the response of innate immune cells to pathogens may provide insights to host defenses and the tactics used by pathogens to circumvent these defenses. We used DNA microarrays to explore the responses of human macrophages to a variety of bacteria. Macrophages responded to a broad range of bacteria with a robust, shared pattern of gene expression.