We have developed an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast. Our method naturally weights and combines into reliable predictions genomic features only weakly associated with interaction (e.g., messenger RNAcoexpression, coessentiality, and colocalization). In addition to de novo predictions, it can integrate often noisy, experimental interaction data sets. We observe that at given levels of sensitivity, our predictions are more accurate than the existing high-throughput experimental data sets. We validate our predictions with TAP (tandem affinity purification) tagging experiments. Our analysis, which gives a comprehensive view of yeast interactions, is available at genecensus.org/intint .

Although some of the underlying technology for quantifying protein abundance was introduced almost thirty years ago [1,2], there has recently been a significant increase in the development of new tools. Concurrently, tools for analyzing mRNA expression are becoming more mainstream. The quantification of both of these molecular populations is not an exercise in redundancy; measurements taken from mRNA and protein levels are complementary and both are necessary for a complete understanding of how the cell works [3]. Additionally, as mRNA is eventually translated into protein, one might assume that there should be some sort of correlation between the level of mRNA and that of protein. Alternatively, there may not be any significant correlation, which, in itself, is an informative conclusion.The two commonly used high-throughput methods for measuring mRNA expression, microarrays and Affymetrix chips, have both been extensively reviewed elsewhere [4][5][6]. There are also two basic methods for determining protein abundance; either based on two-dimensional electrophoresis or on mass-spectrometric methods (Table 1). We provide a brief review of these technologies and recent efforts to determine correlations between quantified protein abundances and mRNA expression. Methods for determining protein levels Two-dimensional electrophoresisDetermining relative protein expression levels by conventional two-dimensional electrophoresis requires isoelectric focusing, SDS-polyacrylamide gel electrophoresis, staining, fixing, densitometry, and careful matching of the same spots on two or more gels. Differentially expressed spots are then excised and enzymatically digested, and the resulting peptides are identified using mass spectrometry. An attractive aspect of this approach is the low capital equipment cost, but a high level of expertise is needed to obtain reproducible gels, and two-dimensional electrophoresis is generally limited to proteins that are neither too acidic, too basic, nor too hydrophobic, and that are between 10 and 200 kDa in size, so that they are reliably separated on gels. Additionally, this approach detects only those proteins that are expressed at relatively high levels and that have long half-lives [7,8]. In one study using 40 µg yeast lysate, the average protein AbstractAttempts to correlate protein abundance with mRNA expression levels have had variable success. We review the results of these comparisons, focusing on yeast. In the process, we survey experimental techniques for determining protein abundance, principally two-dimensional gel electrophoresis and mass-spectrometry. We also merge many of the available yeast protein-abundance datasets, using the resulting larger 'meta-dataset' to find correlations between protein and mRNA expression, both globally and within smaller categories.

We investigate the relationship of protein-protein interactions with mRNA expression levels, by integrating a variety of data sources for yeast. We focus on known protein complexes that have clearly defined interactions between their subunits. We find that subunits of the same protein complex show significant coexpression, both in terms of similarities of absolute mRNA levels and expression profiles, e.g., we can often see subunits of a complex having correlated patterns of expression over a time course. We classify the yeast protein complexes as either permanent or transient, with permanent ones being maintained through most cellular conditions. We find that, generally, permanent complexes, such as the ribosome and proteasome, have a particularly strong relationship with expression, while transient ones do not. However, we note that several transient complexes, such as the RNA polymerase II holoenzyme and the replication complex, can be subdivided into smaller permanent ones, which do have a strong relationship to gene expression. We also investigated the interactions in aggregated, genome-wide data sets, such as the comprehensive yeast two-hybrid experiments, and found them to have only a weak relationship with gene expression, similar to that of transient complexes. (Further details on genecensus.org/expression/interactions and bioinfo.mbb.yale.edu/expression/interactions.)

Advances in sequencing technology have led to a sharp decrease in the cost of 'data generation'. But is this sufficient to ensure cost-effective and efficient 'knowledge generation'?